Childhood abuse history and attention bias in adults

Attention biases toward unpleasant information are evident among children and adults with a history of abuse and have been identified as a potential pathway through which abused children develop psychopathology. Identifying whether a history of childhood abuse affects the time course of attention biases in adults is critical, as this may provide intervention targets. The present study examined the time course of attention bias during an emotion-word Stroop task using event-related potentials (ERPs) in a sample of adults with a range of child abuse histories using a categorical approach (comparing adults with or without a history of moderate-to-severe childhood abuse) and a dimensional approach (analyzing the range from no abuse to severe abuse in a continuous manner). Although behavioral performance did not vary as a function of abuse history, adults with a history of moderate-to-severe childhood abuse showed ERP evidence of early reduced processing of emotional stimuli (smaller N200) and later reduced processing of emotional and nonemotional stimuli (smaller P300), followed by later increased processing of unpleasant stimuli (larger slow wave [SW]). Results suggest that early disengagement from emotional stimuli may help individuals with moderate-to-severe abuse histories to achieve normal behavioral performance on the emotion-word Stroop task. Additionally, regardless of analytic approach, adults with elevated levels of childhood abuse exhibited prolonged engagement (larger SW) specifically with unpleasant stimuli. Present results demonstrate attention bias patterns in adults with a history of childhood abuse and clarify the time course of attention bias. Results are discussed in the context of potential treatment implications.     

The Muscle Sensor for on-site neuroscience lectures to pave the way for a better understanding of brain–machine-interface research

Neuroscience is an expanding field of science to investigate enigmas of brain and human body function. However, the majority of the public have never had the chance to learn the basics of neuroscience and new knowledge from advanced neuroscience research through hands-on experience. Here, we report that we produced the Muscle Sensor, a simplified electromyography, to promote educational understanding in neuroscience. The Muscle Sensor can detect myoelectric potentials which are filtered and processed as 3-V pulse signals to shine a light bulb and emit beep sounds. With this educational tool, we delivered “On-Site Neuroscience Lectures” in Japanese junior-high schools to facilitate hands-on experience of neuroscientific electrophysiology and to connect their text-book knowledge to advanced neuroscience researches. On-site neuroscience lectures with the Muscle Sensor pave the way for a better understanding of the basics of neuroscience and the latest topics such as how brain–machine-interface technology could help patients with disabilities such as spinal cord injuries.     

Investigating the effects of myofascial induction therapy techniques on pain,function and quality of life in patients with chronic low back pain

IntroductionLow back pain (LBP) is well documented as a common health problem; it is the leading cause of activity limitation and work absence throughout much of the world, and it causes an enormous economic burden on individuals, families, communities, industry, and governments. The aim of this study was to comparatively investigate the effects of myofascial induction therapy (MIT) against pain neuroscience education (PNE) on pain and function in patients with chronic low back pain (CLBP).MethodForty patients with CLBP were included and randomly divided into two groups according to the treatment program (40 min/session, 2 sessions/week during 8-week), as follows: the MIT and the PNE groups. The outcome measures were the fear-avoidance beliefs questionnaire (FABQ), Roland Morris disability questionnaire, McGill pain questionnaire, finger floor test, SF-36 quality-of-life questionnaire, and thoracolumbar fascia ultrasound imaging results. Patients were evaluated before and after treatment.ResultsWithin both groups, all outcome scores showed a significant improvement (p < 0.05). After 8-week, SF-36 physical function, physical role and mental health scores significantly improved in MIT group compared with PNE group, finger floor test score significantly decreased in MIT group compared with PNE group, and FABQ score significantly decreased in PNE group compared with MIT group (p < 0.05).ConclusionsAlthough both MIT and PNE were found to be effective on pain and function in patients with CLBP, MIT techniques were substantially better in improving the mobility of trunk flexion and quality of life in these patients.     

Neural Representations of Food-Related Attributes in the Human Orbitofrontal Cortex during Choice Deliberation in Anorexia Nervosa

Decisions about what to eat recruit the orbitofrontal cortex (OFC) and involve the evaluation of food-related attributes such as taste and health. These attributes are used differently by healthy individuals and patients with disordered eating behavior, but it is unclear whether these attributes are decodable from activity in the OFC in both groups and whether neural representations of these attributes are differentially related to decisions about food. We used fMRI combined with behavioral tasks to investigate the representation of taste and health attributes in the human OFC and the role of these representations in food choices in healthy women and women with anorexia nervosa (AN). We found that subjective ratings of tastiness and healthiness could be decoded from patterns of activity in the OFC in both groups. However, health-related patterns of activity in the OFC were more related to the magnitude of choice preferences among patients with AN than healthy individuals. These findings suggest that maladaptive decision-making in AN is associated with more consideration of health information represented by the OFC during deliberation about what to eat.SIGNIFICANCE STATEMENT An open question about the OFC is whether it supports the evaluation of food-related attributes during deliberation about what to eat. We found that healthiness and tastiness information was decodable from patterns of neural activity in the OFC in both patients with AN and healthy controls. Critically, neural representations of health were more strongly related to choices in patients with AN, suggesting that maladaptive overconsideration of healthiness during deliberation about what to eat is related to activity in the OFC. More broadly, these results show that activity in the human OFC is associated with the evaluation of relevant attributes during value-based decision-making. These findings may also guide future research into the development of treatments for AN.     

Multi‐scale network regression for brain‐phenotype associations

Brain networks are increasingly characterized at different scales, including summary statistics, community connectivity, and individual edges. While research relating brain networks to behavioral measurements has yielded many insights into brain‐phenotype relationships, common analytical approaches only consider network information at a single scale. Here, we designed, implemented, and deployed Multi‐Scale Network Regression (MSNR), a penalized multivariate approach for modeling brain networks that explicitly respects both edge‐ and community‐level information by assuming a low rank and sparse structure, both encouraging less complex and more interpretable modeling. Capitalizing on a large neuroimaging cohort (n = 1, 051) , we demonstrate that MSNR recapitulates interpretable and statistically significant connectivity patterns associated with brain development, sex differences, and motion‐related artifacts. Compared to single‐scale methods, MSNR achieves a balance between prediction performance and model complexity, with improved interpretability. Together, by jointly exploiting both edge‐ and community‐level information, MSNR has the potential to yield novel insights into brain‐behavior relationships.     

A technical review of canonical correlation analysis for neuroscience applications

Collecting comprehensive data sets of the same subject has become a standard in neuroscience research and uncovering multivariate relationships among collected data sets have gained significant attentions in recent years. Canonical correlation analysis (CCA) is one of the powerful multivariate tools to jointly investigate relationships among multiple data sets, which can uncover disease or environmental effects in various modalities simultaneously and characterize changes during development, aging, and disease progressions comprehensively. In the past 10 years, despite an increasing number of studies have utilized CCA in multivariate analysis, simple conventional CCA dominates these applications. Multiple CCA‐variant techniques have been proposed to improve the model performance; however, the complicated multivariate formulations and not well‐known capabilities have delayed their wide applications. Therefore, in this study, a comprehensive review of CCA and its variant techniques is provided. Detailed technical formulation with analytical and numerical solutions, current applications in neuroscience research, and advantages and limitations of each CCA‐related technique are discussed. Finally, a general guideline in how to select the most appropriate CCA‐related technique based on the properties of available data sets and particularly targeted neuroscience questions is provided.     

A novel method for estimating connectivity‐based parcellation of the human brain from diffusion MRI: Application to an aging cohort

Connectivity‐based parcellation (CBP) methods are used to define homogenous and biologically meaningful parcels or nodes—the foundations of brain network fingerprinting—by grouping voxels with similar patterns of brain connectivity. However, we still lack a gold standard method and the use of CBPs to study the aging brain remains scarce. Our study proposes a novel CBP method from diffusion MRI data and shows its potential to produce a more accurate characterization of the longitudinal alterations in brain network topology occurring in aging. For this, we constructed whole‐brain connectivity maps from diffusion MRI data of two datasets: an aging cohort evaluated at two timepoints (mean interval time: 52.8 ± 7.24 months) and a normative adult cohort—MGH‐HCP. State‐of‐the‐art clustering techniques were used to identify the best performing technique. Furthermore, we developed a new metric (connectivity homogeneity fingerprint [CHF]) to evaluate the success of the final CBP in improving regional/global structural connectivity homogeneity. Our results show that our method successfully generates highly homogeneous parcels, as described by the significantly larger CHF score of the resulting parcellation, when compared to the original. Additionally, we demonstrated that the developed parcellation provides a robust anatomical framework to assess longitudinal changes in the aging brain. Our results reveal that aging is characterized by a reorganization of the brain''s structural network involving the decrease of intra‐hemispheric, increase of inter‐hemispheric connectivity, and topological rearrangement. Overall, this study proposes a new methodology to perform accurate and robust evaluations of CBP of the human brain.     

Altruistic acting caused by a touching hand: neural underpinnings of the Midas touch effect

Giving and receiving touch are some of the most important social stimuli we exchange in daily life. By touching someone, we can communicate various types of information. Previous studies have also demonstrated that interpersonal touch may affect our altruistic behavior. A classic study showed that customers give bigger tips when they are lightly touched by a waitress, which has been called the Midas touch effect. Numerous studies reported similar effects of touch on different kinds of helping or prosocial behaviors. Here, we aim to examine the neural underpinnings of this effect by employing a functional magnetic resonance imaging approach. While lying in the scanner, participants played different rounds of the dictator game, a measure of prosocial behavior. Before each round, participants were touched (or not touched in the control condition) by an experimenter. We found that touching the hand increased the likeliness to behave prosocial (but not the general liking of control stimuli), thereby confirming the Midas touch effect. The effect was predicted by activity in the primary somatosensory cortex, indicating that the somatosensory cortex here plays a causal role in prosocial behavior. We conclude that the tactile modality in social life may be much more important than previously thought.