Measured Brain Activity Research Articles

A multi-view convolutional neural network method combining attention mechanism for diagnosing autism spectrum disorder

Autism Spectrum Disorder (ASD) is a neurodevelopmental condition whose current psychiatric diagnostic process is subjective and behavior-based. In contrast, functional magnetic resonance imaging (fMRI) can objectively measure brain activity and is useful for identifying brain disorders. However, the ASD diagnostic models employed to date have not reached satisfactory levels of accuracy. This study proposes the use of MAACNN, a method that utilizes multi-view convolutional neural networks (CNNs) in conjunction with attention mechanisms for identifying ASD in multi-scale fMRI. The proposed algorithm effectively combines unsupervised and supervised learning. In the initial stage, we employ stacked denoising autoencoders, an unsupervised learning method for feature extraction, which provides different nodes to adapt to multi-scale data. In the subsequent stage, we perform supervised learning by employing multi-view CNNs for classification and obtain the final results. Finally, multi-scale data fusion is achieved by using the attention fusion mechanism. The ABIDE dataset is used to evaluate the model we proposed., and the experimental results show that MAACNN achieves superior performance with 75.12% accuracy and 0.79 AUC on ABIDE-I, and 72.88% accuracy and 0.76 AUC on ABIDE-II. The proposed method significantly contributes to the clinical diagnosis of ASD.

A Neuro-Influence Experiment to Evaluate the Persuasiveness of Pre-Exposure Prophylaxis Promotion Messages Among Men Who Have Sex With Men: Protocol for a Randomized Controlled Trial.

Pre-exposure prophylaxis (PrEP) is recommended by the US Centers for Disease Control and Prevention, but behavioral factors limit uptake, especially among men who have sex with men. A better understanding of how humans cognitively process information may inform health message development to promote PrEP uptake. This paper is informed by the neuroscience of persuasion and influence and describes the protocol of a neuro-influence experiment using functional near-infrared spectroscopy (fNIRS) to evaluate the persuasiveness of PrEP promotion messages among men who have sex with men in Baltimore, Maryland. We will conduct a randomized controlled trial using fNIRS to measure brain activation among 60 participants viewing PrEP promotion messages either developed through a crowdsourcing open contest implemented by the study team or developed with a traditional social marketing approach. We will evaluate the effectiveness of PrEP promotion messages by assessing brain activation in the regions associated with persuasion and changes in PrEP willingness, behavioral intention, initiation, and action between the 2 groups. This study is funded by the National Institutes of Health (National Institute of Mental Health: R34MH116725). Participant recruitment and data collection were completed in October 2023. The first results are expected to be submitted for publication in 2024. In addition to providing insight into the effectiveness of PrEP promotion messages, this study will examine the feasibility, acceptability, and utility of neuroimaging techniques to evaluate PrEP promotion messages for high-risk men who have sex with men. The findings can also demonstrate the utility of fNIRS as a tool for preproduct testing of health campaigns and enable the public health community to deliver more effective messages to improve health outcomes. DERR1-10.2196/52546.

Post-training sleep modulates motor adaptation and task-related beta oscillations.

Motor adaptation reflects the ability of the brain's sensorimotor system to flexibly deal with environmental changes to generate effective motor behaviour. Whether sleep contributes to the consolidation of motor adaptation remains controversial. In this study, we investigated the impact of sleep on motor adaptation and its neurophysiological correlates in a novel motor adaptation task that leverages a highly automatised motor skill, that is, typing. We hypothesised that sleep-associated memory consolidation would benefit motor adaptation and induce modulations in task-related beta band (13-30 Hz) activity during adaptation. Healthy young male experts in typing on the regular computer keyboard were trained to type on a vertically mirrored keyboard while brain activity was recorded using electroencephalography. Typing performance was assessed either after a full night of sleep with polysomnography or a similar period of daytime wakefulness. Results showed improved motor adaptation performance after nocturnal sleep but not after daytime wakefulness, and decreased beta power: (a) during mirrored typing as compared with regular typing; and (b) in the post-sleep versus the pre-sleep mirrored typing sessions. Furthermore, the slope of the electroencephalography signal, a measure of aperiodic brain activity, decreased during mirrored as compared with regular typing. Changes in the electroencephalography spectral slope from pre- to post-sleep mirrored typing sessions were correlated with changes in task performance. Finally, increased fast sleep spindle density (13-15 Hz) during the night following motor adaptation training was predictive of successful motor adaptation. These findings suggest that post-training sleep modulates neural activity supporting adaptive motor functions.

65 Neuroscience in the Everyday World: Lateralization of Brain Activity During Dual-Task Walking

Objective:Functional near-infrared spectroscopy (fNIRS) is a non-invasive functional neuroimaging method that takes advantage of the optical properties of hemoglobin to provide an indirect measure of brain activation via task-related relative changes in oxygenated hemoglobin (HbO). Its advantage over fMRI is that fNIRS is portable and can be used while walking and talking. In this study, we used fNIRS to measure brain activity in prefrontal and motor region of interests (ROIs) during single- and dual-task walking, with the goal of identifying neural correlates.Participants and Methods:Nineteen healthy young adults [mean age=25.4 (SD=4.6) years; 14 female] engaged in five tasks: standing single-task cognition (serial-3 subtraction); single-task walking at a self-selected comfortable speed on a 24.5m oval-shaped course (overground walking) and on a treadmill; and dual-task cognition+walking on the same overground course and treadmill (8 trials/condition: 20 seconds standing rest, 30 seconds task). Performance on the cognitive task was quantified as the number of correct subtractions, number of incorrect subtractions, number of self-corrected errors, and percent accuracy over the 8 trials. Walking speed (m/sec) was recorded for all walking conditions. fNIRS data were collected on a system consisting of 16 sources, 15 detectors, and 8 short-separation detectors in the following ROIs: right and left lateral frontal (RLF, LLF), right and left medial frontal (RMF, LMF), right and left medial superior frontal (RMSF, LMSF), and right and left motor (RM, LM). Lateral and medial refer to ROIs’ relative positions on lateral prefrontal cortex. fNIRS data were analyzed in Homer3 using a spline motion correction and the iterative weighted least squares method in the general linear model. Correlations between the cognitive/speed variables and ROI HbO data were applied using a Bonferroni adjustment for multiple comparisons.Results:Subjects with missing cognitive data were excluded from analyses, resulting in sample sizes of 18 for the single-task cognition, dual-task overground walking, and dual-task treadmill walking conditions. During dual-task overground walking, there was a significant positive correlation between walking speed and relative change in HbO in RMSF [r(18)=.51, p<.05] and RM [r(18)=.53, p<.05)]. There was a significant negative correlation between total number of correct subtractions and relative change in HbO in LMSF ([r(18)=-.75, p<.05] and LM [r(18)=-.52, p<.05] during dual-task overground walking. No other significant correlations were identified.Conclusions:These results indicate that there is lateralization of the cognitive and motor components of overground dual-task walking. The right hemisphere appears to be more active the faster people walk during the dual-task. By contrast, the left hemisphere appears to be less active when people are working faster on the cognitive task (i.e., serial-3 subtraction). The latter results suggest that automaticity of the cognitive task (i.e., more total correct subtractions) is related to decreased brain activity in the left hemisphere. Future research will investigate whether there is a change in cognitive automaticity over trials and if there are changes in lateralization patterns in neurodegenerative disorders that are known to differentially affect the hemispheres (e.g., Parkinson’s disease).

Prefrontal activation when suppressing an automatic balance recovery step

BackgroundThe present study investigated neural mechanisms for suppressing a highly automatic balance recovery step. Response inhibition has typically been researched using focal hand reaction tasks performed by seated participants, and this has revealed a neural stopping network including the Inferior Frontal Gyrus (IFG). It is unclear if the same neural networks contribute to suppressing an unwanted balance reaction. Research questionIs there greater IFG activation when suppressing an automatic balance recovery step? MethodsFunctional near-infrared spectroscopy (fNIRS) was used to measure brain activity in 21 young adults as they performed a balance recovery task that demanded rapid step suppression following postural perturbation. The hypothesis was that the IFG would show heightened activity when suppressing an automatic balance recovery step. A lean and-release system was used to impose temporally unpredictable forward perturbations by releasing participants from a supported forward lean. For most trials (80%), participants were told to recover balance by quickly stepping forward (STEP). However, on 20% of trials at random, a high-pitch tone was played immediately after postural perturbation signaling participants to suppress a step and fully relax into a catch harness (STOP). This allowed us to target the ability to cancel an already initiated step in a balance recovery context. Average oxygenated hemoglobin changes were contrasted between STEP and STOP trials, 1–6 s post perturbation. ResultsThe results showed a greater bilateral prefrontal response during STOP trials, supporting the idea that executive brain networks are active when suppressing a balance recovery step. SignificanceOur study demonstrates one way in which higher brain processes may help us prevent falls in complex environments where behavioral flexibility is necessary. This study also presents a novel method for assessing response inhibition in an upright postural context where rapid stepping reactions are required.

Neuroimaging of opioid exposure: a review of preclinical animal models to inform addiction research.

Opioid use results in thousands of overdose deaths each year. To address this crisis, we need a better understanding of the neurobiological mechanisms that drive opioid abuse. The noninvasive imaging tools positron emission tomography (PET), functional magnetic resonance imaging (fMRI), and manganese-enhanced magnetic resonance imaging (MEMRI) can be used to identify how brain activity responds to acute opioid exposure and adapts to chronic drug treatment. These techniques can be performed in humans and animal models, and brain networks identified in animals closely map to the human brain. Animal models have the advantage of being able to systematically examine the independent effects of opioid exposure in a controlled environment accounting for the complex factors that drive opioid misuse in humans. This review synthesizes literature that utilized noninvasive neuroimaging tools (PET, fMRI, and MEMRI) measuring brain activity correlates in animals to understand the neurobiological consequences of exposure to abused opioids. A PubMed search in September 2023 identified 25 publications. These manuscripts were divided into 4 categories based on the route and duration of drug exposure (acute/chronic, active/passive administration). Within each category, the results were generally consistent across drug and imaging protocols. These papers cover a 20-year range and highlight the advancements in neuroimaging methodology during that time. These advances have enabled researchers to achieve greater resolution of brain regions altered by opioid exposure and to identify patterns of brain activation across regions (i.e., functional connectivity) and within subregions of structures. After describing the existing literature, we suggest areas where additional research is needed.

Frequency characteristics of temporal and spatial concordance among dynamic indices in inattentive and combined subtypes of attention deficit hyperactivity disorder.

Numerous voxel-based resting-state functional magnetic resonance imaging (rs-fMRI) measurements have been used to characterize spontaneous brain activity in attention deficit hyperactivity disorder (ADHD). However, the practical distinctions and commonalities among these intrinsic brain activity measures remain to be fully explored, and whether the functional concordance is related to frequency is still unknown. The study included 25 ADHD, combined type (ADHD-C); 26 ADHD, inattentive type (ADHD-I); and 28 typically developing (TD) children. We calculated the voxel-wise (temporal) and volume-wise (spatial) concordance among dynamic rs-fMRI indices in the slow-5 (0.01-0.027 Hz) and slow-4 (0.027-0.073 Hz) frequency bands, respectively. The spatiotemporal concordance within the slow-4 and slow-5 bands among the ADHD-C, ADHD-I, and TD groups was compared. Although the ADHD-C and ADHD-I groups showed similar volume-wise concordance, comparison analysis revealed that compared with ADHD-C patients, ADHD-I patients exhibited decreased voxel-wise concordance in the right median cingulate and paracingulate gyrus (MCC) and right supplementary motor area (SMA) in the slow-5 band. In addition, the voxel-wise concordance was negatively correlated with the diagnostic scores of ADHD subtypes. Our results suggest that functional concordance is frequency dependent, and dynamic concordance analysis based on specific frequency bands may provide a novel approach for investigating the pathophysiological differences among ADHD subtypes.

Consumer-Grade Electroencephalogram and Functional Near-Infrared Spectroscopy Neurofeedback Technologies for Mental Health and Wellbeing.

Neurofeedback, utilizing an electroencephalogram (EEG) and/or a functional near-infrared spectroscopy (fNIRS) device, is a real-time measurement of brain activity directed toward controlling and optimizing brain function. This treatment has often been attributed to improvements in disorders such as ADHD, anxiety, depression, and epilepsy, among others. While there is evidence suggesting the efficacy of neurofeedback devices, the research is still inconclusive. The applicability of the measurements and parameters of consumer neurofeedback wearable devices has improved, but the literature on measurement techniques lacks rigorously controlled trials. This paper presents a survey and literary review of consumer neurofeedback devices and the direction toward clinical applications and diagnoses. Relevant devices are highlighted and compared for treatment parameters, structural composition, available software, and clinical appeal. Finally, a conclusion on future applications of these systems is discussed through the comparison of their advantages and drawbacks.

Friend or foe: classifying collaborative interactions using fNIRS.

To succeed, effective teams depend on both cooperative and competitive interactions between individual teammates. Depending on the context, cooperation and competition can amplify or neutralize a team's problem solving ability. Therefore, to assess successful collaborative problem solving, it is first crucial to distinguish competitive from cooperative interactions. We investigate the feasibility of using lightweight brain sensors to distinguish cooperative from competitive interactions in pairs of participants (N=84) playing a decision-making game involving uncertain outcomes. We measured brain activity using functional near-infrared spectroscopy (fNIRS) from social, motor, and executive areas during game play alone and in competition or cooperation with another participant. To distinguish competitive, cooperative, and alone conditions, we then trained support vector classifiers using combinations of features extracted from fNIRS data. We find that features from social areas of the brain outperform other features for discriminating competitive, cooperative, and alone conditions in cross-validation. Comparing the competitive and alone conditions, social features yield a 5% improvement over motor and executive features. Social features show promise as means of distinguishing competitive and cooperative environments in problem solving settings. Using fNIRS data provides a real-time measure of subjective experience in an ecologically valid environment. These results have the potential to inform intelligent team monitoring to provide better real-time feedback and improve team outcomes in naturalistic settings.

Enhancing the accuracy of electroencephalogram-based emotion recognition through Long Short-Term Memory recurrent deep neural networks.

Emotions play a critical role in human communication, exerting a significant influence on brain function and behavior. One effective method of observing and analyzing these emotions is through electroencephalography (EEG) signals. Although numerous studies have been dedicated to emotion recognition (ER) using EEG signals, achieving improved accuracy in recognition remains a challenging task. To address this challenge, this paper presents a deep-learning approach for ER using EEG signals. ER is a dynamic field of research with diverse practical applications in healthcare, human-computer interaction, and affective computing. In ER studies, EEG signals are frequently employed as they offer a non-invasive and cost-effective means of measuring brain activity. Nevertheless, accurately identifying emotions from EEG signals poses a significant challenge due to the intricate and non-linear nature of these signals. The present study proposes a novel approach for ER that encompasses multiple stages, including feature extraction, feature selection (FS) employing clustering, and classification using Dual-LSTM. To conduct the experiments, the DEAP dataset was employed, wherein a clustering technique was applied to Hurst's view and statistical features during the FS phase. Ultimately, Dual-LSTM was employed for accurate ER. The proposed method achieved a remarkable accuracy of 97.5% in accurately classifying emotions across four categories: arousal, valence, liking/disliking, dominance, and familiarity. This high level of accuracy serves as strong evidence for the effectiveness of the deep-learning approach to emotion recognition (ER) utilizing EEG signals. The deep-learning approach proposed in this paper has shown promising results in emotion recognition using EEG signals. This method can be useful in various applications, such as developing more effective therapies for individuals with mood disorders or improving human-computer interaction by allowing machines to respond more intelligently to users' emotional states. However, further research is needed to validate the proposed method on larger datasets and to investigate its applicability to real-world scenarios.

A review of Graph Neural Networks for Electroencephalography data analysis

Electroencephalography (EEG) sensors are flexible and non-invasive sensoring devices for the measurement of electrical brain activity which is extensively used in some areas of clinical practice and psychological/psychiatric research, such as epilepsy, sleep, emotion, and brain computer interfaces. Although EEG sensor do not provide actual brain localizations of the activity sources, they allow to study brain functional connectivity. In this paper we review current application of a specific family of computational methods, the Graph Neural Networks (GNN) to the analysis of EEG data. GNNs appear to be well suited to EEG data modeling as they deal with signals whose domain is defined by a graph instead of a regular lattice in Euclidean space. Readings of EEG electrodes fall in this category, hence the increasing research activity on the application of GNNs to EEG data.

An autonomic mode of brain activity.

The relevance of interactions between autonomic and central nervous systems remains unclear for human brain function and health, particularly when both systems are challenged under sleep deprivation (SD). We measured brain activity (with fMRI), pulse and respiratory signals, and baseline brain amyloid beta burden (with PET) in healthy participants. We found that SD relative to rested wakefulness (RW) resulted in a significant increase in synchronized low frequency (LF, < 0.1Hz) activity in an autonomically-related network (AN), including dorsal attention, visual, and sensorimotor regions, which we previously found to have consistent temporal coupling with LF pulse signal changes (regulated by sympathetic tone). SD resulted in a significant phase coherence between the LF component of the pulse signal and a medial network with peak effects in the midbrain reticular formation, and between LF component of the respiratory variations (regulated by respiratory motor output) and a cerebellar network. The LF power of AN during SD was significantly and independently correlated with pulse-medial network and respiratory-cerebellar network phase coherences (total adjusted R2 = 0.78). Higher LF power of AN during SD (but not RW) was associated with lower amyloid beta burden (Cohen's d = 0.8). In sum, SD triggered an autonomic mode of synchronized brain activity that was associated with distinct autonomic-central interactions. Findings highlight the direct relevance of global cortical synchronization to brain clearance mechanisms.

Investigation of advanced mindfulness meditation “cessation” experiences using EEG spectral analysis in an intensively sampled case study

Mindfulness meditation is a contemplative practice informed by Buddhism that targets the development of present-focused awareness and non-judgment of experience. Interest in mindfulness is burgeoning, and it has been shown to be effective in improving mental and physical health in clinical and non-clinical contexts. In this report, for the first time, we used electroencephalography (EEG) combined with a neurophenomenological approach to examine the neural signature of “cessation” events, which are dramatic experiences of complete discontinuation in awareness similar to the loss of consciousness, which are reported to be experienced by very experienced meditators, and are proposed to be evidence of mastery of mindfulness meditation. We intensively sampled these cessations as experienced by a single advanced meditator (with over 23,000 h of meditation training) and analyzed 37 cessation events collected in 29 EEG sessions between November 12, 2019, and March 11, 2020. Spectral analyses of the EEG data surrounding cessations showed that these events were marked by a large-scale alpha-power decrease starting around 40 s before their onset, and that this alpha-power was lowest immediately following a cessation. Region-of-interest (ROI) based examination of this finding revealed that this alpha-suppression showed a linear decrease in the occipital and parietal regions of the brain during the pre-cessation time period. Additionally, there were modest increases in theta power for the central, parietal, and right temporal ROIs during the pre-cessation timeframe, whereas power in the Delta and Beta frequency bands were not significantly different surrounding cessations. By relating cessations to objective and intrinsic measures of brain activity (i.e., EEG power) that are related to consciousness and high-level psychological functioning, these results provide evidence for the ability of experienced meditators to voluntarily modulate their state of consciousness and lay the foundation for studying these unique states using a neuroscientific approach.

Effect of Acute Enriched Environment Exposure on Brain Oscillations and Activation of the Translation Initiation Factor 4E-BPs at Synapses across Wakefulness and Sleep in Rats.

Brain plasticity is induced by learning during wakefulness and is consolidated during sleep. But the molecular mechanisms involved are poorly understood and their relation to experience-dependent changes in brain activity remains to be clarified. Localised mRNA translation is important for the structural changes at synapses supporting brain plasticity consolidation. The translation mTOR pathway, via phosphorylation of 4E-BPs, is known to be activate during sleep and contributes to brain plasticity, but whether this activation is specific to synapses is not known. We investigated this question using acute exposure of rats to an enriched environment (EE). We measured brain activity with EEGs and 4E-BP phosphorylation at cortical and cerebellar synapses with Western blot analyses. Sleep significantly increased the conversion of 4E-BPs to their hyperphosphorylated forms at synapses, especially after EE exposure. EE exposure increased oscillations in the alpha band during active exploration and in the theta-to-beta (4-30 Hz) range, as well as spindle density, during NREM sleep. Theta activity during exploration and NREM spindle frequency predicted changes in 4E-BP hyperphosphorylation at synapses. Hence, our results suggest a functional link between EEG and molecular markers of plasticity across wakefulness and sleep.

Hook Fabric Electroencephalography Electrode for Brain Activity Measurement without Shaving the Head.

In this research, novel electroencephalogram (EEG) electrodes were developed to detect high-quality EEG signals without the requirement of conductive gels, skin treatments, or head shaving. These electrodes were created using electrically conductive hook fabric with a resistance of 1 Ω/sq. The pointed hooks of the conductive fabric establish direct contact with the skin and can penetrate through hair. To ensure excellent contact between the hook fabric electrode and the scalp, a knitted-net EEG bridge cap with a bridging effect was employed. The results showed that the hook fabric electrode exhibited lower skin-to-electrode impedance compared to the dry Ag/AgCl comb electrode. Additionally, it collected high-quality signals on par with the standard wet gold cups and commercial dry Ag/AgCl comb electrodes. Moreover, the hook fabric electrode displayed a higher signal-to-noise ratio (33.6 dB) with a 4.2% advantage over the standard wet gold cup electrode. This innovative electrode design eliminates the need for conductive gel and head shaving, offering enhanced flexibility and lightweight characteristics, making it ideal for integration into textile structures and facilitating convenient long-term monitoring.

Comparative Analysis of VGG19, ResNet50, and GoogLeNet Inception Models for BCI

A brain-computer interface (BCI) is a system that allows an individual to communicate with a computer or other external device using brain activity alone. Electroencephalography (EEG) is a common approach for detecting brain activity in BCI systems, as it provides a non-invasive measure of brain activity with high temporal resolution. Deep learning methods, such as convolutional neural networks (CNNs), have been applied to the analysis of EEG data for BCI applications, and have shown promising results in terms of accuracy and reliability. In this literature survey, we reviewed the use of CNNs for the classification of EEG data in real time to identify specific brain commands. Challenges to the development of an effective BCI using EEG and deep learning methods include the variability of EEG signals across individuals and the high dimensionality of the EEG data. Further research is needed to address these challenges and improve the accuracy and reliability of BCI systems using deep learning methods.

Binge Eating (BE) and Obesity: Brain Activity and Psychological Measures before and after Roux-En-Y Gastric Bypass (RYGB).

Brain activity in response to food cues following Roux-En-Y Gastric Bypass (RYGB) in binge eating (BE) or non-binge eating (NB) individuals is understudied. Here, 15 RYGB (8 BE; 7 NB) and 13 no treatment (NT) (7 BE; 6 NB) women with obesity underwent fMRI imaging while viewing high and low energy density food (HEF and LEF, respectively) and non-food (NF) visual cues. A region of interest (ROI) analysis compared BE participants to NB participants in those undergoing RYGB surgery pre-surgery and 4 months post. Results were corrected for multiple comparisons using liberal (p < 0.006 uncorrected) and stringent (p < 0.05 FDR corrected) thresholds. Four months following RYGB (vs. no treatment (NT) control), both BE and NB participants showed greater reductions in blood oxygen level-dependent (BOLD) signals (a proxy of local brain activity) in the dorsomedial prefrontal cortex in response to HEF (vs. LEF) cues (p < 0.006). BE (vs. NB) participants showed greater increases in the precuneus (p < 0.006) and thalamic regions (p < 0.05 corrected) to food (vs. NF). For RYGB (vs. NT) participants, BE participants, but not NB participants, showed lower BOLD signal in the middle occipital gyrus (p < 0.006), whilst NB participants, but not BE participants, showed lower signal in inferior frontal gyrus (p < 0.006) in response to HEF (vs. LEF). Results suggest distinct neural mechanisms of RGYB in BE and may help lead to improved clinical treatments.

Towards hippocampal navigation for brain–computer interfaces

Automatic wheelchairs directly controlled by brain activity could provide autonomy to severely paralyzed individuals. Current approaches mostly rely on non-invasive measures of brain activity and translate individual commands into wheelchair movements. For example, an imagined movement of the right hand would steer the wheelchair to the right. No research has investigated decoding higher-order cognitive processes to accomplish wheelchair control. We envision an invasive neural prosthetic that could provide input for wheelchair control by decoding navigational intent from hippocampal signals. Navigation has been extensively investigated in hippocampal recordings, but not for the development of neural prostheses. Here we show that it is possible to train a decoder to classify virtual-movement speeds from hippocampal signals recorded during a virtual-navigation task. These results represent the first step toward exploring the feasibility of an invasive hippocampal BCI for wheelchair control.

Increased Activity in the Prefrontal Cortex Related to Planning during a Handwriting Task

We investigated the relationship between the prefrontal cortex (PFC) and executive function during a drawing task. Thirty-three participants using pen tablets provided the data for this task. PFC activity was recorded using functional near-infrared spectroscopy (fNIRS) during a simple zig-zag task and a complex periodic line (PL) pattern task. For each task, there was a trace condition and a prediction condition. The Executive Function Questionnaire (EFQ) was used to examine the association between brain-function measurements and executive function during the task. PFC activity was analyzed in the right, middle, and left regions. Oxygenated hemoglobin values measured with fNIRS were converted to z-values and analyzed as a measure of brain activity. Drawing fluency was measured using the line length. In the PL pattern task, the line length was significantly shorter under the prediction condition than under the trace condition. Activity in the right PFC under the prediction condition was significantly higher than that under the trace condition in the PL pattern task, and the score of the EFQ planning subscale was associated with activity in the right PFC. Activity in the right PFC is important for fluent drawing, suggesting that it is also important during drawing activities involving symbols such as letters.

Sharing a political ideology predicts more similar brain activity

Even when presented with the same information, liberals and conservatives tend to interpret political information differently. By measuring brain activity while partisans watched political videos or thought about political concepts, we show that individuals that are similar in their political beliefs exhibit similar brain patterns whilst processing political information.