Hand, foot and lip representations in primary sensorimotor cortex: a high-density electroencephalography study

Distinct and additive effects of visual and vibratory feedback for motor rehabilitation: an EEG study in healthy subjects

INTRODUCTION

The use of visual and proprioceptive feedback is a key property of motor rehabilitation techniques. This feedback can be used alone, for example, for vision in mirror or video therapy, for proprioception in focal tendon vibration therapy, or in combination, for example, in robot-assisted training. This Electroencephalographic (EEG) study in healthy subjects explored the distinct neurophysiological impact of adding visual (video therapy), proprioceptive (focal tendinous vibration), or combined feedback (video therapy and focal tendinous vibration) to a motor imagery task.

METHODS

Sixteen healthy volunteers performed 20 mental imagery (MI) tasks involving right wrist extension and flexion under four conditions: MI alone (IA), MI + video feedback observation (IO), MI + vibratory feedback (IV), and MI + observation + vibratory feedback (IOV). Brain activity was monitored with EEG, and time-frequency neurophysiological markers of movement were computed. The emotions of the patients were also measured during the task.

RESULTS

In the alpha band, we observed bilateral ERD in the visual feedback conditions (IO, IOV). In the beta band, the ERD was bilateral in the IA, IV and IOV but more lateralized in the IV and IOV. After movement, we observed strong ERS in the IO and IOV but not in the IA or IV. Embodiment was stronger in conditions with vibratory feedback (IOV > IV > IA and IO) CONCLUSION: Conditions with visual feedback (IO, IOV) recruit the mirror neurons system (alpha ERD) and provide more accurate feedback of the task than IA and IV, which triggers motor validation pathways (beta rebound analysis). Vibratory feedback enhances the recruitment of the left sensorimotor areas, with a synergistic effect in the IOV (beta ERD analysis), thus maximizing embodiment. Visual and vibratory feedback recruits the sensorimotor cortex during motor imagery in different ways and can be combined to maximize the benefits of both techniques TRIAL REGISTRATION: https://clinicaltrials.gov/study/NCT04449328 .

Human brain imaging with high-density electroencephalography: Techniques and applications

Electroencephalography (EEG) is a technique for non-invasively measuring neuronal activity in the human brain using electrodes placed on the participant's scalp. With the advancement of digital technologies, EEG analysis has evolved over time from the qualitative analysis of amplitude and frequency modulations to a comprehensive analysis of the complex spatiotemporal characteristics of the recorded signals. EEG is now considered a powerful tool for measuring neural processes in the same time frame in which they happen (i.e. the subsecond range). However, it is commonly argued that EEG suffers from low spatial resolution, which makes it difficult to localize the generators of EEG activity accurately and reliably. Today, the availability of high-density EEG (hdEEG) systems, combined with methods for incorporating information on head anatomy and sophisticated source-localization algorithms, has transformed EEG into an important neuroimaging tool. hdEEG offers researchers and clinicians a rich and varied range of applications. It can be used not only for investigating neural correlates in motor and cognitive neuroscience experiments, but also for clinical diagnosis, particularly in the detection of epilepsy and the characterization of neural impairments in a wide range of neurological disorders. Notably, the integration of hdEEG systems with other physiological recordings, such as kinematic and/or electromyography data, might be especially beneficial to better understand the neuromuscular mechanisms associated with deconditioning in ageing and neuromotor disorders, by mapping the neurokinematic and neuromuscular connectivity patterns directly in the brain.

Large-scale analysis of neural activity and connectivity from high-density electroencephalographic data

INTRODUCTION

High-density electroencephalography (hdEEG) is a technique used for the characterization of the neural activity and connectivity in the human brain. The analysis of EEG data involves several steps, including signal pre-processing, head modelling, source localization and activity/connectivity quantification. Visual check of the analysis steps is often necessary, making the process time- and resource-consuming and, therefore, not feasible for large datasets.

FINDINGS

Here we present the Noninvasive Electrophysiology Toolbox (NET), an open-source software for large-scale analysis of hdEEG data, running on the cross-platform MATLAB environment. NET combines all the tools required for a complete hdEEG analysis workflow, from raw signals to final measured values. By relying on reconstructed neural signals in the brain, NET can perform traditional analyses of time-locked neural responses, as well as more advanced functional connectivity and brain mapping analyses. The extracted quantitative neural data can be exported to provide broad compatibility with other software.

CONCLUSIONS

NET is freely available (https://github.com/bind-group-kul/net) under the GNU public license for non-commercial use and open-source development, together with a graphical user interface (GUI) and a user tutorial. While NET can be used interactively with the GUI, it is primarily aimed at unsupervised automation to process large hdEEG datasets efficiently. Its implementation creates indeed a highly customizable program suitable for analysis automation and tight integration into existing workflows.

Neural basis of lower-limb visual feedback therapy: an EEG study in healthy subjects

BACKGROUND

Video-feedback observational therapy (VOT) is an intensive rehabilitation technique based on movement repetition and visualization that has shown benefits for motor rehabilitation of the upper and lower limbs. Despite an increase in recent literature on the neurophysiological effects of VOT in the upper limb, there is little knowledge about the cortical effects of visual feedback therapies when applied to the lower limbs. The aim of our study was to better understand the neurophysiological effects of VOT. Thus, we identified and compared the EEG biomarkers of healthy subjects undergoing lower limb VOT during three tasks: passive observation, observation and motor imagery, observation and motor execution.

METHODS

We recruited 38 healthy volunteers and monitored their EEG activity while they performed a right ankle dorsiflexion task in the VOT. Three graded motor tasks associated with action observation were tested: action observation alone (O), motor imagery with action observation (OI), and motor execution synchronized with action observation (OM). The alpha and beta event-related desynchronization (ERD) and event-related synchronization (or beta rebound, ERS) rhythms were used as biomarkers of cortical activation and compared between conditions with a permutation test. Changes in connectivity during the task were computed with phase locking value (PLV).

RESULTS

During the task, in the alpha band, the ERD was comparable between O and OI activities across the precentral, central and parietal electrodes. OM involved the same regions but had greater ERD over the central electrodes. In the beta band, there was a gradation of ERD intensity in O, OI and OM over central electrodes. After the task, the ERS changes were weak during the O task but were strong during the OI and OM (Cz) tasks, with no differences between OI and OM.

CONCLUSION

Alpha band ERD results demonstrated the recruitment of mirror neurons during lower limb VOT due to visual feedback. Beta band ERD reflects strong recruitment of the sensorimotor cortex evoked by motor imagery and action execution. These results also emphasize the need for an active motor task, either motor imagery or motor execution task during VOT, to elicit a post-task ERS, which is absent during passive observation. Trial Registration NCT05743647.

An Overall Automated Architecture Based on the Tapping Test Measurement Protocol: Hand Dexterity Assessment through an Innovative Objective Method

The present work focuses on the tapping test, which is a method that is commonly used in the literature to assess dexterity, speed, and motor coordination by repeatedly moving fingers, performing a tapping action on a flat surface. During the test, the activation of specific brain regions enhances fine motor abilities, improving motor control. The research also explores neuromuscular and biomechanical factors related to finger dexterity, revealing neuroplastic adaptation to repetitive movements. To give an objective evaluation of all cited physiological aspects, this work proposes a measurement architecture consisting of the following: (i) a novel measurement protocol to assess the coordinative and conditional capabilities of a population of participants; (ii) a suitable measurement platform, consisting of synchronized and non-invasive inertial sensors to be worn at finger level; (iii) a data analysis processing stage, able to provide the final user (medical doctor or training coach) with a plethora of useful information about the carried-out tests, going far beyond state-of-the-art results from classical tapping test examinations. Particularly, the proposed study underscores the importance interdigital autonomy for complex finger motions, despite the challenges posed by anatomical connections; this deepens our understanding of upper limb coordination and the impact of neuroplasticity, holding significance for motor abilities assessment, improvement, and therapeutic strategies to enhance finger precision. The proof-of-concept test is performed by considering a population of college students. The obtained results allow us to consider the proposed architecture to be valuable for many application scenarios, such as the ones related to neurodegenerative disease evolution monitoring.

Resting state brain network segregation is associated with walking speed and working memory in older adults

Older adults exhibit larger individual differences in walking ability and cognitive function than young adults. Characterizing intrinsic brain connectivity differences in older adults across a wide walking performance spectrum may provide insight into the mechanisms of functional decline in some older adults and resilience in others. Thus, the objectives of this study were to: (1) determine whether young adults and high- and low-functioning older adults show group differences in brain network segregation, and (2) determine whether network segregation is associated with working memory and walking function in these groups. The analysis included 21 young adults and 81 older adults. Older adults were further categorized according to their physical function using a standardized assessment; 54 older adults had low physical function while 27 were considered high functioning. Structural and functional resting state magnetic resonance images were collected using a Siemens Prisma 3T scanner. Working memory was assessed with the NIH Toolbox list sorting test. Walking speed was assessed with a 400 m-walk test at participants' self-selected speed. We found that network segregation in mobility-related networks (sensorimotor, vestibular, and visual networks) was higher in younger adults compared to older adults. There were no group differences in laterality effects on network segregation. We found multivariate associations between working memory and walking speed with network segregation scores. Higher right anterior cingulate cortex network segregation was associated with higher working memory function. Higher right sensorimotor, right vestibular, right anterior cingulate cortex, and lower left anterior cingulate cortex network segregation was associated with faster walking speed. These results are unique and significant because they demonstrate higher network segregation is largely related to higher physical function and not age alone.

Brain Networks Modulation during Simple and Complex Gait: A "Mobile Brain/Body Imaging" Study

Walking encompasses a complex interplay of neuromuscular coordination and cognitive processes. Disruptions in gait can impact personal independence and quality of life, especially among the elderly and neurodegenerative patients. While traditional biomechanical analyses and neuroimaging techniques have contributed to understanding gait control, they often lack the temporal resolution needed for rapid neural dynamics. This study employs a mobile brain/body imaging (MoBI) platform with high-density electroencephalography (hd-EEG) to explore event-related desynchronization and synchronization (ERD/ERS) during overground walking. Simultaneous to hdEEG, we recorded gait spatiotemporal parameters. Participants were asked to walk under usual walking and dual-task walking conditions. For data analysis, we extracted ERD/ERS in α, β, and γ bands from 17 selected regions of interest encompassing not only the sensorimotor cerebral network but also the cognitive and affective networks. A correlation analysis was performed between gait parameters and ERD/ERS intensities in different networks in the different phases of gait. Results showed that ERD/ERS modulations across gait phases in the α and β bands extended beyond the sensorimotor network, over the cognitive and limbic networks, and were more prominent in all networks during dual tasks with respect to usual walking. Correlation analyses showed that a stronger α ERS in the initial double-support phases correlates with shorter step length, emphasizing the role of attention in motor control. Additionally, β ERD/ERS in affective and cognitive networks during dual-task walking correlated with dual-task gait performance, suggesting compensatory mechanisms in complex tasks. This study advances our understanding of neural dynamics during overground walking, emphasizing the multidimensional nature of gait control involving cognitive and affective networks.

Cortical activity associated with the maintenance of balance during unstable stances

Background

Humans continuously maintain and adjust posture during gait, standing, and sitting. The difficulty of postural control is reportedly increased during unstable stances, such as unipedal standing and with closed eyes. Although balance is slightly impaired in healthy young adults in such unstable stances, they rarely fall. The brain recognizes the change in sensory inputs and outputs motor commands to the musculoskeletal system. However, such changes in cortical activity associated with the maintenance of balance following periods of instability require further clarified.

Methods

In this study, a total of 15 male participants performed two postural control tasks and the center of pressure displacement and electroencephalogram were simultaneously measured. In addition, the correlation between amplitude of center of pressure displacement and power spectral density of electroencephalogram was analyzed.

Results

The movement of the center of pressure was larger in unipedal standing than in bipedal standing under both eye open and eye closed conditions. It was also larger under the eye closed condition compared with when the eyes were open in unipedal standing. The amplitude of high-frequency bandwidth (1-3 Hz) of the center of pressure displacement was larger during more difficult postural tasks than during easier ones, suggesting that the continuous maintenance of posture was required. The power spectral densities of the theta activity in the frontal area and the gamma activity in the parietal area were higher during more difficult postural tasks than during easier ones across two postural control tasks, and these correlate with the increase in amplitude of high-frequency bandwidth of the center of pressure displacement.

Conclusions

Taken together, specific activation patterns of the neocortex are suggested to be important for the postural maintenance during unstable stances.

Functional and oncological outcomes after right hemisphere glioma resection in awake versus asleep patients: a systematic review and meta-analysis

The right hemisphere has been underestimated by being considered as the non-dominant hemisphere. However, it is involved in many functions, including movement, language, cognition, and emotion. Therefore, because lesions on this side are usually not resected under awake mapping, there is a risk of unfavorable neurological outcomes. The goal of this study is to compare the functional and oncological outcomes of awake surgery (AwS) versus surgery under general anesthesia (GA) in supratentorial right-sided gliomas. A systematic review of the literature according to PRISMA guidelines was performed up to March 2023. Four databases were screened. Primary outcome to assess was return to work (RTW). Secondary outcomes included the rate of postoperative neurological deficit, postoperative Karnofsky Performance Status (KPS) score and the extent of resection (EOR). A total of 32 articles were included with 543 patients who underwent right hemisphere tumor resection under awake surgery and 294 under general anesthesia. There were no significant differences between groups regarding age, gender, handedness, perioperative KPS, tumor location or preoperative seizures. Preoperative and long-term postoperative neurological deficits were statistically lower after AwS (p = 0.03 and p < 0.01, respectively), even though no difference was found regarding early postoperative course (p = 0.32). A subsequent analysis regarding type of postoperative impairment was performed. Severe postoperative language deficits were not different (p = 0.74), but there were fewer long-term mild motor and high-order cognitive deficits (p < 0.05) in AwS group. A higher rate of RTW (p < 0.05) was documented after AwS. The EOR was similar in both groups. Glioma resection of the right hemisphere under awake mapping is a safer procedure with a better preservation of high-order cognitive functions and a higher rate of RTW than resection under general anesthesia, despite similar EOR.

Development of a smartphone screening test for preclinical Alzheimer's disease and validation across the dementia continuum

BACKGROUND

Dementia prevalence is predicted to triple to 152 million globally by 2050. Alzheimer's disease (AD) constitutes 70% of cases. There is an urgent need to identify individuals with preclinical AD, a 10-20-year period of progressive brain pathology without noticeable cognitive symptoms, for targeted risk reduction. Current tests of AD pathology are either too invasive, specialised or expensive for population-level assessments. Cognitive tests are normal in preclinical AD. Emerging evidence demonstrates that movement analysis is sensitive to AD across the disease continuum, including preclinical AD. Our new smartphone test, TapTalk, combines analysis of hand and speech-like movements to detect AD risk. This study aims to [1] determine which combinations of hand-speech movement data most accurately predict preclinical AD [2], determine usability, reliability, and validity of TapTalk in cognitively asymptomatic older adults and [3], prospectively validate TapTalk in older adults who have cognitive symptoms against cognitive tests and clinical diagnoses of Mild Cognitive Impairment and AD dementia.

METHODS

Aim 1 will be addressed in a cross-sectional study of at least 500 cognitively asymptomatic older adults who will complete computerised tests comprising measures of hand motor control (finger tapping) and oro-motor control (syllabic diadochokinesis). So far, 1382 adults, mean (SD) age 66.20 (7.65) years, range 50-92 (72.07% female) have been recruited. Motor measures will be compared to a blood-based AD biomarker, phosphorylated tau 181 to develop an algorithm that classifies preclinical AD risk. Aim 2 comprises three sub-studies in cognitively asymptomatic adults: (i) a cross-sectional study of 30-40 adults to determine the validity of data collection from different types of smartphones, (ii) a prospective cohort study of 50-100 adults ≥ 50 years old to determine usability and test-retest reliability, and (iii) a prospective cohort study of ~1,000 adults ≥ 50 years old to validate against cognitive measures. Aim 3 will be addressed in a cross-sectional study of ~200 participants with cognitive symptoms to validate TapTalk against Montreal Cognitive Assessment and interdisciplinary consensus diagnosis.

DISCUSSION

This study will establish the precision of TapTalk to identify preclinical AD and estimate risk of cognitive decline. If accurate, this innovative smartphone app will enable low-cost, accessible screening of individuals for AD risk. This will have wide applications in public health initiatives and clinical trials.

TRIAL REGISTRATION

ClinicalTrials.gov identifier: NCT06114914, 29 October 2023. Retrospectively registered.

Hi1a Improves Sensorimotor Deficit following Endothelin-1-Induced Stroke in Rats but Does Not Improve Functional Outcomes following Filament-Induced Stroke in Mice

Activation of acid-sensing ion channel 1a (ASIC1a) plays a major role in mediating acidosis-induced neuronal injury following a stroke. Therefore, the inhibition of ASIC1a is a potential therapeutic avenue for the treatment of stroke. Venom-peptide Hi1a, a selective and highly potent ASIC1a inhibitor, reduces the infarct size and functional deficits when injected into the brain after stroke in rodents. However, its efficacy when administered using a clinically relevant route of administration remains to be established. Therefore, the current investigation aims to examine the efficacy of systemically administered Hi1a, using two different models of stroke in different species. Mice were subjected to the filament model of middle cerebral artery occlusion (MCAO) and treated with Hi1a systemically using either a single- or multiple-dosing regimen. 24 h poststroke, mice underwent functional testing, and the brain infarct size was assessed. Rats were subjected to endothelin-1 (ET-1)-induced MCAO and treated with Hi1a intravenously 2 h poststroke. Rats underwent functional tests prior to and for 3 days poststroke, when infarct volume was assessed. Mice receiving Hi1a did not show any improvements in functional outcomes, despite a trend toward reduced infarct size. This trend for reduced infarct size in mice was consistent regardless of the dosing regimen. There was also a trend toward lower infarct size in rats treated with Hi1a. More specifically, Hi1a reduced the amount of damage occurring within the somatosensory cortex, which was associated with an improved sensorimotor function in Hi1a-treated rats. Thus, this study suggests that Hi1a or more brain-permeable ASIC1a inhibitors are a potential stroke treatment.

Motor imagery ability scores are related to cortical activation during gait imagery

Motor imagery (MI) is the mental execution of actions without overt movements that depends on the ability to imagine. We explored whether this ability could be related to the cortical activity of the brain areas involved in the MI network. To this goal, brain activity was recorded using high-density electroencephalography in nineteen healthy adults while visually imagining walking on a straight path. We extracted Event-Related Desynchronizations (ERDs) in the θ, α, and β band, and we measured MI ability via (i) the Kinesthetic and Visual Imagery Questionnaire (KVIQ), (ii) the Vividness of Movement Imagery Questionnaire-2 (VMIQ), and (iii) the Imagery Ability (IA) score. We then used Pearson's and Spearman's coefficients to correlate MI ability scores and average ERD power (avgERD). Positive correlations were identified between VMIQ and avgERD of the middle cingulum in the β band and with avgERD of the left insula, right precentral area, and right middle occipital region in the θ band. Stronger activation of the MI network was related to better scores of MI ability evaluations, supporting the importance of testing MI ability during MI protocols. This result will help to understand MI mechanisms and develop personalized MI treatments for patients with neurological dysfunctions.

Favoring the cognitive-motor process in the closed-loop of BCI mediated post stroke motor function recovery: challenges and approaches

The brain-computer interface (BCI)-mediated rehabilitation is emerging as a solution to restore motor skills in paretic patients after stroke. In the human brain, cortical motor neurons not only fire when actions are carried out but are also activated in a wired manner through many cognitive processes related to movement such as imagining, perceiving, and observing the actions. Moreover, the recruitment of motor cortexes can usually be regulated by environmental conditions, forming a closed-loop through neurofeedback. However, this cognitive-motor control loop is often interrupted by the impairment of stroke. The requirement to bridge the stroke-induced gap in the motor control loop is promoting the evolution of the BCI-based motor rehabilitation system and, notably posing many challenges regarding the disease-specific process of post stroke motor function recovery. This review aimed to map the current literature surrounding the new progress in BCI-mediated post stroke motor function recovery involved with cognitive aspect, particularly in how it refired and rewired the neural circuit of motor control through motor learning along with the BCI-centric closed-loop.

Effects of collagen, chitosan and mixture on fibroblast responses and angiogenic activities in 2D and 3D in vitro models

Despite accumulating evidences have demonstrated the potential of collagen and chitosan on tissue repair, it remains unclear on their combination effects. Here, we examined the regenerative effects of single collagen, chitosan and their mixture on fibroblasts and endothelial cells at cellular levels. The results showed that fibroblast responses, as indicated by high proliferative rate, increased spheroid diameter and migrated area existing from spheroid edge, and decreased wound area, were significantly promoted by either collagen or chitosan stimulation. Similarly, both collagen and chitosan resulted in increased endothelial cell proliferation and migration with accelerated tube-like network formation and upregulated VE-cadherin expression, although collagen strongly provided this effect. While the 1:1 mixture (100:100 μg/mL of chitosan to collagen) treatment caused a reduction in fibroblast viability, the lower ratio of chitosan (1:10 mixture; 10:100 μg/mL) did not produce any impact on both fibroblast and endothelial cell viabilities. The 1:10 mixture also significantly enhanced the additional effects on fibroblast responses and angiogenic activities as shown by higher endothelial growth, proliferation and migration with accelerated capillary-like network formation than those treated with the single substance. Further investigation of signaling proteins found that collagen significantly increased expressions of p-Fak, p-Akt and Cdk5 whereas chitosan upregulated p-Fak and Cdk5 expressions. Comparing to the single treatments, p-Fak, p-Akt and Cdk5 were higher expressed in the 1:10 mixture. These observations indicate that proper collagen-chitosan mixture provides the combination effects on fibroblast responses and angiogenic activities when a high concentration of collagen is used, possibly through Fak/Akt and Cdk5 signaling pathways. Therefore, this study helps to define the clinical use of collagen and chitosan as promising biomaterials for tissue repair.

Relation of BOLD response to food-specific and generic motor response inhibition tasks to body fat gain in adults with overweight and obesity

BACKGROUND

Low inhibitory control has been theorized to contribute to the development and maintenance of obesity. Knowledge on the neurobiological indicators of inhibitory control deficits predicting future weight gain is limited. The current study examined if individual differences in blood-oxygenation-level-dependent (BOLD) activity associated with food-specific and general motor response inhibition predict future body fat change in adults with overweight or obesity.

METHODS

BOLD activity and behavioral responses of adults with overweight or obesity (N = 160) were recorded while performing a food-specific stop signal task (n = 92) or a generic stop signal task (n = 68). Percent body fat was measured at baseline, posttest, 3-month, and 6-month follow-up.

RESULTS

Elevated BOLD activity in somatosensory (postcentral gyrus), and attention (precuneus) regions during successful inhibition in the food-specific stop signal task and elevated BOLD activity in a motor region (anterior cerebellar lobe) in the generic stop signal task predicted greater body fat gain over 6-month follow-up. Elevated BOLD activity in inhibitory control regions (inferior-, middle-, superior frontal gyri) and error monitoring regions (anterior cingulate cortex, insula) during erroneous responses in the generic stop signal task predicted body fat loss.

CONCLUSIONS

Results suggest that improving motor response inhibition and error monitoring may facilitate weight loss in adults with overweight and obesity.

Improved Myelination following Camp Leg Power, a Selective Motor Control Intervention for Children with Spastic Bilateral Cerebral Palsy: A Diffusion Tensor MRI Study

BACKGROUND AND PURPOSE

Children with spastic cerebral palsy have motor deficits associated with periventricular leukomalacia indicating WM damage to the corticospinal tracts. We investigated whether practice of skilled lower extremity selective motor control movements would elicit neuroplasticity.

MATERIALS AND METHODS

Twelve children with spastic bilateral cerebral palsy and periventricular leukomalacia born preterm (mean age, 11.5 years; age range, 7.3-16.6 years) participated in a lower extremity selective motor control intervention, Camp Leg Power. Activities promoted isolated joint movement including isokinetic knee exercises, ankle-controlled gaming, gait training, and sensorimotor activities (3 hours/day, 15 sessions, 1 month). DWI scans were collected pre- and postintervention. Tract-Based Spatial Statistics was used to analyze changes in fractional anisotropy, radial diffusivity, axial diffusivity, and mean diffusivity.

RESULTS

Significantly reduced radial diffusivity (P < . 05) was found within corticospinal tract ROIs, including 28.4% of the left and 3.6% of the right posterior limb of the internal capsule and 14.1% of the left superior corona radiata. Reduced mean diffusivity was found within the same ROIs (13.3%, 11.6%, and 6.6%, respectively). Additionally, decreased radial diffusivity was observed in the left primary motor cortex. Additional WM tracts had decreased radial diffusivity and mean diffusivity, including the anterior limb of the internal capsule, external capsule, anterior corona radiata, and corpus callosum body and genu.

CONCLUSIONS

Myelination of the corticospinal tracts improved following Camp Leg Power. Neighboring WM changes suggest recruitment of additional tracts involved in regulating neuroplasticity of the motor regions. Intensive practice of skilled lower extremity selective motor control movements promotes neuroplasticity in children with spastic bilateral cerebral palsy.

Are Motor Imagery Ability scores related to cortical activity during gait imagery?

Motor imagery (MI) is the mental execution of actions without overt movements that depends on the ability to imagine. We explored whether this ability could be related to the cortical activity of the brain areas involved in the MI network. To this goal, brain activity was recorded using high-density electroencephalography (hdEEG) in nineteen healthy adults while visually imagining walking on a straight path. We extracted Event-Related Desynchronizations (ERDs) in the β band, and we measured MI ability via (i) the Kinesthetic and Visual Imagery Questionnaire (KVIQ), (ii) the Vividness of Movement Imagery Questionnaire-2 (VMIQ), and (iii) the Imagery Ability (IA) score. We then used Pearson's and Spearman's coefficients to correlate MI ability scores and average ERD power (avgERD). VMIQ was positively correlated with avgERD of frontal and cingulate areas, whereas IA SCORE was positively correlated with avgERD of left inferior frontal and superior temporal regions. Stronger activation of the MI network was related to better scores of MI ability evaluations, supporting the importance of testing MI ability during MI protocols. This result will help to understand MI mechanisms and develop personalized MI treatments for patients with neurological dysfunctions.

Functional mapping of sensorimotor activation in the human thalamus at 9.4 Tesla

Although the thalamus is perceived as a passive relay station for almost all sensory signals, the function of individual thalamic nuclei remains unresolved. In the present study, we aimed to identify the sensorimotor nuclei of the thalamus in humans using task-based fMRI at a field strength of 9.4T by assessing the individual subject-specific sensorimotor BOLD response during a combined active motor (finger-tapping) and passive sensory (tactile-finger) stimulation. We demonstrate that both tasks increase BOLD signal response in the lateral nuclei group (VPL, VA, VLa, and VLp), and in the pulvinar nuclei group (PuA, PuM, and PuL). Finger-tapping stimuli evokes a stronger BOLD response compared to the tactile stimuli, and additionally engages the intralaminar nuclei group (CM and Pf). In addition, our results demonstrate reproducible thalamic nuclei activation during motor and tactile stimuli. This work provides important insight into understanding the function of individual thalamic nuclei in processing various input signals and corroborates the benefits of using ultra-high-field MR scanners for functional imaging of fine-scale deeply located brain structures.

What do functional neuroimaging studies tell us about the association between falls and cognition in older adults? A systematic review

Impaired cognition is a known risk factor for falls in older adults. To enhance prevention strategies and treatment of falls among an aging global population, an understanding of the neural processes and networks involved is required. We present a systematic review investigating how functional neuroimaging techniques have been used to examine the association between falls and cognition in seniors. Peer-reviewed articles were identified through searching five electronic databases: 1) Medline, 2) PsycINFO, 3) CINAHL, 4) EMBASE, and 5) Pubmed. Key author, key paper, and reference searching was also conducted. Nine studies were included in this review. A questionnaire composed of seven questions was used to assess the quality of each study. EEG, fMRI, and PET were utilized across studies to examine brain function in older adults. Consistent evidence demonstrates that cognition is associated with measures of falls/falls risk, specifically visual attention and executive function. Our results show that falls/falls risk may be implicated with specific brain regions and networks. Future studies should be prospective and long-term in nature, with standardized outcome measures. Mobile neuroimaging techniques may also provide insight into brain activity as it pertains to cognition and falls in older adults in real-world settings.

Strength gains after 12 weeks of resistance training correlate with neurochemical markers of brain health in older adults: a randomized control 1H-MRS study

Physical exercise is considered a potent countermeasure against various age-associated physiological deterioration processes. We therefore assessed the effect of 12 weeks of resistance training on brain metabolism in older adults (age range: 60-80 years). Participants either underwent two times weekly resistance training program which consisted of four lower body exercises performed for 3 sets of 6-10 repetitions at 70-85% of 1 repetition maximum (n = 20) or served as the passive control group (n = 21). The study used proton magnetic resonance spectroscopy to quantify the ratio of total N-acetyl aspartate, total choline, glutamate-glutamine complex, and myo-inositol relative to total creatine (tNAA/tCr, tCho/tCr, Glx/tCr, and mIns/tCr respectively) in the hippocampus (HPC), sensorimotor (SM1), and prefrontal (dlPFC) cortices. The peak torque (PT at 60°/s) of knee extension and flexion was assessed using an isokinetic dynamometer. We used repeated measures time × group ANOVA to assess time and group differences and correlation coefficient analyses to examine the pre-to-post change (∆) associations between PT and neurometabolite variables. The control group showed significant declines in tNAA/tCr and Glx/tCr of SM1, and tNAA/tCr of dlPFC after 12 weeks, which were not seen in the experimental group. A significant positive correlation was found between ∆PT knee extension and ∆SM1 Glx/tCr, ∆dlPFC Glx/tCr and between ∆PT knee flexion and ∆dlPFC mIns/tCr in the experimental group. Overall, findings suggest that resistance training seems to elicit alterations in various neurometabolites that correspond to exercise-induced "preservation" of brain health, while simultaneously having its beneficial effect on augmenting muscle functional characteristics in older adults.

NeuroGrasp: Real-Time EEG Classification of High-Level Motor Imagery Tasks Using a Dual-Stage Deep Learning Framework

Brain-computer interfaces (BCIs) have been widely employed to identify and estimate a user's intention to trigger a robotic device by decoding motor imagery (MI) from an electroencephalogram (EEG). However, developing a BCI system driven by MI related to natural hand-grasp tasks is challenging due to its high complexity. Although numerous BCI studies have successfully decoded large body parts, such as the movement intention of both hands, arms, or legs, research on MI decoding of high-level behaviors such as hand grasping is essential to further expand the versatility of MI-based BCIs. In this study, we propose NeuroGrasp, a dual-stage deep learning framework that decodes multiple hand grasping from EEG signals under the MI paradigm. The proposed method effectively uses an EEG and electromyography (EMG)-based learning, such that EEG-based inference at test phase becomes possible. The EMG guidance during model training allows BCIs to predict hand grasp types from EEG signals accurately. Consequently, NeuroGrasp improved classification performance offline, and demonstrated a stable classification performance online. Across 12 subjects, we obtained an average offline classification accuracy of 0.68 (±0.09) in four-grasp-type classifications and 0.86 (±0.04) in two-grasp category classifications. In addition, we obtained an average online classification accuracy of 0.65 (±0.09) and 0.79 (±0.09) across six high-performance subjects. Because the proposed method has demonstrated a stable classification performance when evaluated either online or offline, in the future, we expect that the proposed method could contribute to different BCI applications, including robotic hands or neuroprosthetics for handling everyday objects.

Clinical variables, including novel joint index, associated with future patient-physician discordance in global assessment of rheumatoid arthritis (RA) disease activity based on a large RA database in Japan

BACKGROUND

Discordance between patient global assessment (PGA) and physician global assessment (PhGA) of rheumatoid arthritis (RA) disease activity is mainly determined by pain and functional disabilities. This study aimed to investigate the shift in PGA-PhGA discordance and the variables associated with future positive discordance (PGA > PhGA) based on the NinJa database in Japan.

METHODS

We examined 7557 adults with RA registered in both NinJa 2014 and 2018, with a discordance cutoff of 3 on a 10-cm scale. The affected joint distribution was investigated using the joint indices x, y, and z, which were calculated as indices for the upper joint, lower joint, and large joint involvement, respectively. The variables in NinJa 2014 that were associated with positive discordance in NinJa 2018 were examined using binary stepwise logistic regression analysis.

RESULTS

Due to the small number of patients with RA categorized as having negative discordance (PGA < PhGA), we focused on patients with RA categorized as having either concordance or positive discordance. Logistic regression analysis revealed that positive discordance in NinJa 2018 was associated with age, pain, modified Health Assessment Questionnaire (mHAQ) score, corticosteroid use, and existent positive discordance and was inversely associated with C-reactive protein (CRP) and x at baseline (NinJa 2014). The same findings were observed when patients with RA were divided based on the discordance status at baseline. Persistence (positive discordance to positive discordance) was associated with pain and mHAQ scores but inversely associated with CRP.

CONCLUSIONS

Positive discordance may persist. Circumventing this requires adequate management of pain and functional impairment.

State-dependent tDCS modulation of the somatomotor network: A MEG study

OBJECTIVE

Transcranial direct current stimulation (tDCS) is a non-invasive technique widely used to investigate brain excitability and activity. However, the variability in both brain and behavioral responses to tDCS limits its application for clinical purposes. This study aims to shed light on state-dependency, a phenomenon that contributes to the variability of tDCS.

METHODS

To this aim, we investigated changes in spectral activity and functional connectivity in somatomotor regions after Real and Sham tDCS using generalized additive mixed models (GAMMs), which allowed us to investigate how modulation depends on the initial state of the brain.

RESULTS

Results showed that changes in spectral activity, but not connectivity, in the somatomotor regions depend on the initial state of the brain, confirming state-dependent effects. Specifically, we found a non-linear interaction between stimulation conditions (Real vs Sham) and initial state: a reduction of alpha and beta power was observed only in participants that had higher alpha and beta power before Real tDCS.

CONCLUSIONS

This study highlights the importance of considering state-dependency to tDCS and shows how it can be taken into account with appropriate statistical models.

SIGNIFICANCE

Our findings bear insight into tDCS mechanisms, potentially leading to discriminate between tDCS responders and non-responders.

Theta-phase connectivity between medial prefrontal and posterior areas underlies novel instructions implementation

Implementing novel instructions is a complex and uniquely human cognitive ability, that requires the rapid and flexible conversion of symbolic content into a format that enables the execution of the instructed behavior. Preparing to implement novel instructions, as opposed to their mere maintenance, involves the activation of the instructed motor plans, and the binding of the action information to the specific context in which this should be executed. Recent evidence and prominent computational models suggest that this efficient configuration of the system might involve a central role of frontal theta oscillations in establishing top-down long-range synchronization between distant and task-relevant brain areas. In the present EEG study (human subjects, 30 females, 4 males), we demonstrate that proactively preparing for the implementation of novels instructions, as opposed to their maintenance, involves a strengthened degree of connectivity in the theta frequency range between medial prefrontal and motor/visual areas. Moreover, we replicated previous results showing oscillatory features associated specifically with implementation demands, and extended on them demonstrating the role of theta oscillations in mediating the effect of task demands on behavioral performance. Taken together, these findings support our hypothesis that the modulation of connectivity patterns between frontal and task-relevant posterior brain areas is a core factor in the emergence of a behavior-guiding format from novel instructions.Significance statementEveryday life requires the use and manipulation of currently available information to guide behavior and reach specific goals. In the present study we investigate how the same instructed content elicits different neural activity depending on the task being performed. Crucially, connectivity between medial prefrontal cortex and posterior brain areas is strengthened when novel instructions have to be implemented, rather than simply maintained. This finding suggests that theta oscillations play a role in setting up a dynamic and flexible network of task-relevant regions optimized for the execution of the instructed behavior.

Somatosensory Deficits After Stroke: Insights From MRI Studies

Somatosensory deficits after stroke are a major health problem, which can impair patients' health status and quality of life. With the developments in human brain mapping techniques, particularly magnetic resonance imaging (MRI), many studies have applied those techniques to unravel neural substrates linked to apoplexy sequelae. Multi-parametric MRI is a vital method for the measurement of stroke and has been applied to diagnose stroke severity, predict outcome and visualize changes in activation patterns during stroke recovery. However, relatively little is known about the somatosensory deficits after stroke and their recovery. This review aims to highlight the utility and importance of MRI techniques in the field of somatosensory deficits and synthesizes corresponding articles to elucidate the mechanisms underlying the occurrence and recovery of somatosensory symptoms. Here, we start by reviewing the anatomic and functional features of the somatosensory system. And then, we provide a discussion of MRI techniques and analysis methods. Meanwhile, we present the application of those techniques and methods in clinical studies, focusing on recent research advances and the potential for clinical translation. Finally, we identify some limitations and open questions of current imaging studies that need to be addressed in future research.

Assessing Neurokinematic and Neuromuscular Connectivity During Walking Using Mobile Brain-Body Imaging

Gait is a common but rather complex activity that supports mobility in daily life. It requires indeed sophisticated coordination of lower and upper limbs, controlled by the nervous system. The relationship between limb kinematics and muscular activity with neural activity, referred to as neurokinematic and neuromuscular connectivity (NKC/NMC) respectively, still needs to be elucidated. Recently developed analysis techniques for mobile high-density electroencephalography (hdEEG) recordings have enabled investigations of gait-related neural modulations at the brain level. To shed light on gait-related neurokinematic and neuromuscular connectivity patterns in the brain, we performed a mobile brain/body imaging (MoBI) study in young healthy participants. In each participant, we collected hdEEG signals and limb velocity/electromyography signals during treadmill walking. We reconstructed neural signals in the alpha (8–13 Hz), beta (13–30 Hz), and gamma (30–50 Hz) frequency bands, and assessed the co-modulations of their power envelopes with myogenic/velocity envelopes. Our results showed that the myogenic signals have larger discriminative power in evaluating gait-related brain-body connectivity with respect to kinematic signals. A detailed analysis of neuromuscular connectivity patterns in the brain revealed robust responses in the alpha and beta bands over the lower limb representation in the primary sensorimotor cortex. There responses were largely contralateral with respect to the body sensor used for the analysis. By using a voxel-wise analysis of variance on the NMC images, we revealed clear modulations across body sensors; the variability across frequency bands was relatively lower, and below significance. Overall, our study demonstrates that a MoBI platform based on hdEEG can be used for the investigation of gait-related brain-body connectivity. Future studies might involve more complex walking conditions to gain a better understanding of fundamental neural processes associated with gait control, or might be conducted in individuals with neuromotor disorders to identify neural markers of abnormal gait.

Dynamic corticomuscular multi-regional modulations during finger movement revealed by time-varying network analysis

OBJECTIVE

A body movement involves the complicated information exchange between the central and peripheral systems, which is characterized by the dynamical coupling patterns between the multiple brain areas and multiple muscle units. How the central and peripheral nerves coordinate multiple internal brain regions and muscle groups is very important when accomplishing the action.

APPROACH

In this study, we extend the adaptive directed transfer function to construct the time-varying networks between multiple corticomuscular regions and divide the movement duration into different stages by the time-varying corticomuscular network patterns.

MAIN RESULTS

The inter dynamical corticomuscular network demonstrated the different interaction patterns between the central and peripheral systems during the different hand movement stages. The muscles transmit bottom-up movement information in the preparation stage, but the brain issues top-down control commands and dominates in the execution stage, and finally, the brain's dominant advantage gradually weakens in the relaxation stage. When classifying the different movement stages based on time-varying corticomuscular network indicators, an average accuracy above 74% could be reliably achieved.

SIGNIFICANCE

The findings of this study help deepen our knowledge of central-peripheral nerve pathways and coordination mechanisms, and also provide opportunities for monitoring and regulating movement disorders.

AI Prediction of Brain Signals for Human Gait Using BCI Device and FBG Based Sensorial Platform for Plantar Pressure Measurements

Artificial intelligence (AI) in developing modern solutions for biomedical problems such as the prediction of human gait for human rehabilitation is gaining ground. An attempt was made to use plantar pressure information through fiber Bragg grating (FBG) sensors mounted on an in-sole, in tandem with a brain-computer interface (BCI) device to predict brain signals corresponding to sitting, standing and walking postures of a person. Posture classification was attained with an accuracy range between 87–93% from FBG and BCI signals using machine learning models such as K-nearest neighbor (KNN), logistic regression (LR), support vector machine (SVM), and naïve Bayes (NB). These models were used to identify electrodes responding to sitting, standing and walking activities of four users from a 16 channel BCI device. Six electrode positions based on the 10–20 system for electroencephalography (EEG) were identified as the most sensitive to plantar activities and found to be consistent with clinical investigations of the sensorimotor cortex during foot movement. A prediction of brain EEG corresponding to given FBG data with lowest mean square error (MSE) values (0.065–0.109) was made with the selection of a long-short term memory (LSTM) machine learning model when compared to the recurrent neural network (RNN) and gated recurrent unit (GRU) models.

Frequency-dependent modulation of neural oscillations across the gait cycle

Balance and walking are fundamental to support common daily activities. Relatively accurate characterizations of normal and impaired gait features were attained at the kinematic and muscular levels. Conversely, the neural processes underlying gait dynamics still need to be elucidated. To shed light on gait‐related modulations of neural activity, we collected high‐density electroencephalography (hdEEG) signals and ankle acceleration data in young healthy participants during treadmill walking. We used the ankle acceleration data to segment each gait cycle in four phases: initial double support, right leg swing, final double support, left leg swing. Then, we processed hdEEG signals to extract neural oscillations in alpha, beta, and gamma bands, and examined event‐related desynchronization/synchronization (ERD/ERS) across gait phases. Our results showed that ERD/ERS modulations for alpha, beta, and gamma bands were strongest in the primary sensorimotor cortex (M1), but were also found in premotor cortex, thalamus and cerebellum. We observed a modulation of neural oscillations across gait phases in M1 and cerebellum, and an interaction between frequency band and gait phase in premotor cortex and thalamus. Furthermore, an ERD/ERS lateralization effect was present in M1 for the alpha and beta bands, and in the cerebellum for the beta and gamma bands. Overall, our findings demonstrate that an electrophysiological source imaging approach based on hdEEG can be used to investigate dynamic neural processes of gait control. Future work on the development of mobile hdEEG‐based brain–body imaging platforms may enable overground walking investigations, with potential applications in the study of gait disorders.

Embedding decomposition for artifacts removal in EEG signals

Electroencephalogram (EEG) recordings are often contaminated with artifacts. Various methods have been developed to eliminate or weaken the influence of artifacts. However, most of them rely on prior experience for analysis. Here, we propose an deep learning framework to separate neural signal and artifacts in the embedding space and reconstruct the denoised signal, which is called DeepSeparator. DeepSeparator employs an encoder to extract and amplify the features in the raw EEG, a module called decomposer to extract the trend, detect and suppress artifact and a decoder to reconstruct the denoised signal. Besides, DeepSeparator can extract the artifact, which largely increases the model interpretability. The proposed method is tested with a semi-synthetic EEG dataset and a real task-related EEG dataset, suggesting that DeepSeparator outperforms the conventional models in both EOG and EMG artifact removal. DeepSeparator can be extended to multi-channel EEG and data with any arbitrary length. It may motivate future developments and application of deep learning-based EEG denoising. The code for DeepSeparator is available at https://github.com/ncclabsustech/DeepSeparator.

Age-Related Changes in Functional Connectivity during the Sensorimotor Integration Detected by Artificial Neural Network

Large-scale functional connectivity is an important indicator of the brain’s normal functioning. The abnormalities in the connectivity pattern can be used as a diagnostic tool to detect various neurological disorders. The present paper describes the functional connectivity assessment based on artificial intelligence to reveal age-related changes in neural response in a simple motor execution task. Twenty subjects of two age groups performed repetitive motor tasks on command, while the whole-scalp EEG was recorded. We applied the model based on the feed-forward multilayer perceptron to detect functional relationships between five groups of sensors located over the frontal, parietal, left, right, and middle motor cortex. Functional dependence was evaluated with the predicted and original time series coefficient of determination. Then, we applied statistical analysis to highlight the significant features of the functional connectivity network assessed by our model. Our findings revealed the connectivity pattern is consistent with modern ideas of the healthy aging effect on neural activation. Elderly adults demonstrate a pronounced activation of the whole-brain theta-band network and decreased activation of frontal–parietal and motor areas of the mu-band. Between-subject analysis revealed a strengthening of inter-areal task-relevant links in elderly adults. These findings can be interpreted as an increased cognitive demand in elderly adults to perform simple motor tasks with the dominant hand, induced by age-related working memory decline.

Neural oscillations during motor imagery of complex gait: an HdEEG study

The aim of this study was to investigate differences between usual and complex gait motor imagery (MI) task in healthy subjects using high-density electroencephalography (hdEEG) with a MI protocol. We characterized the spatial distribution of α- and β-bands oscillations extracted from hdEEG signals recorded during MI of usual walking (UW) and walking by avoiding an obstacle (Dual-Task, DT). We applied a source localization algorithm to brain regions selected from a large cortical-subcortical network, and then we analyzed α and β bands Event-Related Desynchronizations (ERDs). Nineteen healthy subjects visually imagined walking on a path with (DT) and without (UW) obstacles. Results showed in both gait MI tasks, α- and β-band ERDs in a large cortical-subcortical network encompassing mostly frontal and parietal regions. In most of the regions, we found α- and β-band ERDs in the DT compared with the UW condition. Finally, in the β band, significant correlations emerged between ERDs and scores in imagery ability tests. Overall we detected MI gait-related α- and β-band oscillations in cortical and subcortical areas and significant differences between UW and DT MI conditions. A better understanding of gait neural correlates may lead to a better knowledge of pathophysiology of gait disturbances in neurological diseases.

A multi-step blind source separation approach for the attenuation of artifacts in mobile high-density electroencephalography data

Objective.Electroencephalography (EEG) is a widely used technique to address research questions about brain functioning, from controlled laboratorial conditions to naturalistic environments. However, EEG data are affected by biological (e.g. ocular, myogenic) and non-biological (e.g. movement-related) artifacts, which-depending on their extent-may limit the interpretability of the study results. Blind source separation (BSS) approaches have demonstrated to be particularly promising for the attenuation of artifacts in high-density EEG (hdEEG) data. Previous EEG artifact removal studies suggested that it may not be optimal to use the same BSS method for different kinds of artifacts.Approach.In this study, we developed a novel multi-step BSS approach to optimize the attenuation of ocular, movement-related and myogenic artifacts from hdEEG data. For validation purposes, we used hdEEG data collected in a group of healthy participants in standing, slow-walking and fast-walking conditions. During part of the experiment, a series of tone bursts were used to evoke auditory responses. We quantified event-related potentials (ERPs) using hdEEG signals collected during an auditory stimulation, as well as the event-related desynchronization (ERD) by contrasting hdEEG signals collected in walking and standing conditions, without auditory stimulation. We compared the results obtained in terms of auditory ERP and motor-related ERD using the proposed multi-step BSS approach, with respect to two classically used single-step BSS approaches.Main results. The use of our approach yielded the lowest residual noise in the hdEEG data, and permitted to retrieve stronger and more reliable modulations of neural activity than alternative solutions. Overall, our study confirmed that the performance of BSS-based artifact removal can be improved by using specific BSS methods and parameters for different kinds of artifacts.Significance.Our technological solution supports a wider use of hdEEG-based source imaging in movement and rehabilitation studies, and contributes to the further development of mobile brain/body imaging applications.

Unraveling somatotopic organization in the human brain using machine learning and adaptive supervoxel-based parcellations

In addition to the well-established somatotopy in the pre- and post-central gyrus, there is now strong evidence that somatotopic organization is evident across other regions in the sensorimotor network. This raises several experimental questions: To what extent is activity in the sensorimotor network effector-dependent and effector-independent? How important is the sensorimotor cortex when predicting the motor effector? Is there redundancy in the distributed somatotopically organized network such that removing one region has little impact on classification accuracy? To answer these questions, we developed a novel experimental approach. fMRI data were collected while human subjects performed a precisely controlled force generation task separately with their hand, foot, and mouth. We used a simple linear iterative clustering (SLIC) algorithm to segment whole-brain beta coefficient maps to build an adaptive brain parcellation and then classified effectors using extreme gradient boosting (XGBoost) based on parcellations at various spatial resolutions. This allowed us to understand how data-driven adaptive brain parcellation granularity altered classification accuracy. Results revealed effector-dependent activity in regions of the post-central gyrus, precentral gyrus, and paracentral lobule. SMA, regions of the inferior and superior parietal lobule, and cerebellum each contained effector-dependent and effector-independent representations. Machine learning analyses showed that increasing the spatial resolution of the data-driven model increased classification accuracy, which reached 94% with 1755 supervoxels. Our SLIC-based supervoxel parcellation outperformed classification analyses using established brain templates and random simulations. Occlusion experiments further demonstrated redundancy across the sensorimotor network when classifying effectors. Our observations extend our understanding of effector-dependent and effector-independent organization within the human brain and provide new insight into the functional neuroanatomy required to predict the motor effector used in a motor control task.

Investigating the spectral features of the brain meso-scale structure at rest

Recent studies provide novel insights into the meso-scale organization of the brain, highlighting the co-occurrence of different structures: classic assortative (modular), disassortative, and core-periphery. However, the spectral properties of the brain meso-scale remain mostly unexplored. To fill this knowledge gap, we investigated how the meso-scale structure is organized across the frequency domain. We analyzed the resting state activity of healthy participants with source-localized high-density electroencephalography signals. Then, we inferred the community structure using weighted stochastic block-model (WSBM) to capture the landscape of meso-scale structures across the frequency domain. We found that different meso-scale modalities co-exist and are diversely organized over the frequency spectrum. Specifically, we found a core-periphery structure dominance, but we also highlighted a selective increase of disassortativity in the low frequency bands (<8 Hz), and of assortativity in the high frequency band (30-50 Hz). We further described other features of the meso-scale organization by identifying those brain regions which, at the same time, (a) exhibited the highest degree of assortativity, disassortativity, and core-peripheriness (i.e., participation) and (b) were consistently assigned to the same community, irrespective from the granularity imposed by WSBM (i.e., granularity-invariance). In conclusion, we observed that the brain spontaneous activity shows frequency-specific meso-scale organization, which may support spatially distributed and local information processing.

EEGdenoiseNet: a benchmark dataset for deep learning solutions of EEG denoising

Objective.Deep learning (DL) networks are increasingly attracting attention across various fields, including electroencephalography (EEG) signal processing. These models provide comparable performance to that of traditional techniques. At present, however, there is a lack of well-structured and standardized datasets with specific benchmark limit the development of DL solutions for EEG denoising.Approach.Here, we present EEGdenoiseNet, a benchmark EEG dataset that is suited for training and testing DL-based denoising models, as well as for performance comparisons across models. EEGdenoiseNet contains 4514 clean EEG segments, 3400 ocular artifact segments and 5598 muscular artifact segments, allowing users to synthesize contaminated EEG segments with the ground-truth clean EEG.Main results.We used EEGdenoiseNet to evaluate denoising performance of four classical networks (a fully-connected network, a simple and a complex convolution network, and a recurrent neural network). Our results suggested that DL methods have great potential for EEG denoising even under high noise contamination.Significance.Through EEGdenoiseNet, we hope to accelerate the development of the emerging field of DL-based EEG denoising. The dataset and code are available athttps://github.com/ncclabsustech/EEGdenoiseNet.

Ultrasound-Powered Implants: A Critical Review of Piezoelectric Material Selection and Applications

Ultrasound-powered implants (UPIs) represent cutting edge power sources for implantable medical devices (IMDs), as their powering strategy allows for extended functional lifetime, decreased size, increased implant depth, and improved biocompatibility. IMDs are limited by their reliance on batteries. While batteries proved a stable power supply, batteries feature relatively large sizes, limited life spans, and toxic material compositions. Accordingly, energy harvesting and wireless power transfer (WPT) strategies are attracting increasing attention by researchers as alternative reliable power sources. Piezoelectric energy scavenging has shown promise for low power applications. However, energy scavenging devices need be located near sources of movement, and the power stream may suffer from occasional interruptions. WPT overcomes such challenges by more stable, on-demand power to IMDs. Among the various forms of WPT, ultrasound powering offers distinct advantages such as low tissue-mediated attenuation, a higher approved safe dose (720 mW cm^-2 ), and improved efficiency at smaller device sizes. This study presents and discusses the state-of-the-art in UPIs by reviewing piezoelectric materials and harvesting devices including lead-based inorganic, lead-free inorganic, and organic polymers. A comparative discussion is also presented of the functional material properties, architecture, and performance metrics, together with an overview of the applications where UPIs are being deployed.

Detection of Resting-State Functional Connectivity from High-Density Electroencephalography Data: Impact of Head Modeling Strategies

Recent technological advances have been permitted to use high-density electroencephalography (hdEEG) for the estimation of functional connectivity and the mapping of resting-state networks (RSNs). The reliable estimate of activity and connectivity from hdEEG data relies on the creation of an accurate head model, defining how neural currents propagate from the cortex to the sensors placed over the scalp. To the best of our knowledge, no study has been conducted yet to systematically test to what extent head modeling accuracy impacts on EEG-RSN reconstruction. To address this question, we used 256-channel hdEEG data collected in a group of young healthy participants at rest. We first estimated functional connectivity in EEG-RSNs by means of band-limited power envelope correlations, using neural activity estimated with an optimized analysis workflow. Then, we defined a series of head models with different levels of complexity, specifically testing the effect of different electrode positioning techniques and head tissue segmentation methods. We observed that robust EEG-RSNs can be obtained using a realistic head model, and that inaccuracies due to head tissue segmentation impact on RSN reconstruction more than those due to electrode positioning. Additionally, we found that EEG-RSN robustness to head model variations had space and frequency specificity. Overall, our results may contribute to defining a benchmark for assessing the reliability of hdEEG functional connectivity measures.

Inflammation-driven brain and gut barrier dysfunction in stress and mood disorders

Regulation of emotions is generally associated exclusively with the brain. However, there is evidence that peripheral systems are also involved in mood, stress vulnerability vs. resilience, and emotion‐related memory encoding. Prevalence of stress and mood disorders such as major depression, bipolar disorder, and post‐traumatic stress disorder is increasing in our modern societies. Unfortunately, 30%–50% of individuals respond poorly to currently available treatments highlighting the need to further investigate emotion‐related biology to gain mechanistic insights that could lead to innovative therapies. Here, we provide an overview of inflammation‐related mechanisms involved in mood regulation and stress responses discovered using animal models. If clinical studies are available, we discuss translational value of these findings including limitations. Neuroimmune mechanisms of depression and maladaptive stress responses have been receiving increasing attention, and thus, the first part is centered on inflammation and dysregulation of brain and circulating cytokines in stress and mood disorders. Next, recent studies supporting a role for inflammation‐driven leakiness of the blood–brain and gut barriers in emotion regulation and mood are highlighted. Stress‐induced exacerbated inflammation fragilizes these barriers which become hyperpermeable through loss of integrity and altered biology. At the gut level, this could be associated with dysbiosis, an imbalance in microbial communities, and alteration of the gut–brain axis which is central to production of mood‐related neurotransmitter serotonin. Novel therapeutic approaches such as anti‐inflammatory drugs, the fast‐acting antidepressant ketamine, and probiotics could directly act on the mechanisms described here improving mood disorder‐associated symptomatology. Discovery of biomarkers has been a challenging quest in psychiatry, and we end by listing promising targets worth further investigation.

6-Gingerol, an active constituent of ginger, attenuates lipopolysaccharide-induced oxidation, inflammation, cognitive deficits, neuroplasticity, and amyloidogenesis in rat

This study examined the protective effect of 6-Gingerol (6G) against lipopolysaccharide (LPS)-induced cognitive impairments, oxidative stress, neuroplasticity, amyloidogenesis, and inflammation. Male rats were allocated into six groups in this manner; Group I placed on normal saline only. Group II was treated for 7 days with LPS alone intraperitoneally at 250 µg/kg body weight (bw). Group III received 6G alone at 50 mg/kg bw orally for 14 days. Groups IV and V received 6G at 20 and 50 mg/kg bw for 7 days, respectively, and LPS for another 7 days to induce neurotoxicity. Group VI received 5 mg/kg bw of donepezil for 7 days and LPS for 7 days. Pretreatment with 20 and 50 mg/kg bw of 6G protected against LPS-mediated learning and memory function, and also locomotor and motor deficits. Besides, 20 and 50 mg/kg bw 6G mitigated LPS-induced alteration in markers of oxidative stress. Furthermore, induction of amyloidogenesis associated with disruption of histoarchitecture and high expression of interleukin 1β, inducible nitric oxide synthase, amyloid precursor protein (APP), β-secretase 1, and brain-derived neurotrophic factor by LPS was mitigated by the two doses of 6G in the rat hippocampus and cerebral cortex region of the brain. 6G pretreatment at the two doses mitigated LPS-mediated histopathological changes in the hippocampus and cerebral cortex of rats. Overall, our results demonstrate that the protective effect of 6G is mediated through the reversal of neurobehavioral deficit, oxidative stress, inflammation, and amyloidogenesis, thus making 6G a possible chemoprophylactic agent against brain injury as a result of LPS exposure. PRACTICAL APPLICATIONS: In the search for a holistic prevention of inflammation-associated neurodegeneration, nutraceuticals are becoming prominent. Hence, this study presents 6G, an active constituent of ginger, as a chemoprotective, antioxidant, and anti-inflammatory agent, which is able to ameliorate cognitive impairments, oxidative stress, neuroplasticity, amyloidogenesis, and inflammation in LPS-induced rat model of neuroinflammation.

Host resistance to Bacillus thuringiensis is linked to altered bacterial community within a specialist insect herbivore

Evolution of resistance to transgenic crops producing toxins from Bacillus thuringiensis (Bt) threatens the sustainability of the technology. Examination of resistance mechanisms has largely focused on characterization of mutations in proteins serving as Bt toxin binding sites. However, insect microbial communities have the potential to provide host resistance to pesticides in a myriad of ways. Previous findings suggest the killing mechanism of Bt relies on enteric bacteria becoming pathogenic in the disrupted gut environment of the insect following Bt intoxication. Thus, here we hypothesized that resistance to Bt would alter the microbiome composition of the insect. Previous studies have manipulated the microbiome of susceptible insects and monitored their response to Bt. In our study, we characterized the associated bacterial communities of Bt‐resistant and ‐susceptible western corn rootworms, a widespread pest of maize in the United States. We found resistant insects harbor a bacterial community that is less rich and distinct from susceptible insects. After feeding on Bt‐expressing maize, susceptible insects exhibited dysbiosis of the associated bacterial community, whereas the community within resistant insects remained relatively unchanged. These results suggest resistance to Bt produces alterations in the microbiome of the western corn rootworm that may contribute to resistance. We further demonstrated that by itself, feeding on Bt toxin‐expressing seedlings caused a shift in the microbiota. This work provides a broader picture of the effect stressors have on microbiome composition, and the potential heritable changes induced as a result of intense selection.

Automated Head Tissue Modelling Based on Structural Magnetic Resonance Images for Electroencephalographic Source Reconstruction

In the last years, technological advancements for the analysis of electroencephalography (EEG) recordings have permitted to investigate neural activity and connectivity in the human brain with unprecedented precision and reliability. A crucial element for accurate EEG source reconstruction is the construction of a realistic head model, incorporating information on electrode positions and head tissue distribution. In this paper, we introduce MR-TIM, a toolbox for head tissue modelling from structural magnetic resonance (MR) images. The toolbox consists of three modules: 1) image pre-processing – the raw MR image is denoised and prepared for further analyses; 2) tissue probability mapping – template tissue probability maps (TPMs) in individual space are generated from the MR image; 3) tissue segmentation – information from all the TPMs is integrated such that each voxel in the MR image is assigned to a specific tissue. MR-TIM generates highly realistic 3D masks, five of which are associated with brain structures (brain and cerebellar grey matter, brain and cerebellar white matter, and brainstem) and the remaining seven with other head tissues (cerebrospinal fluid, spongy and compact bones, eyes, muscle, fat and skin). Our validation, conducted on MR images collected in healthy volunteers and patients as well as an MR template image from an open-source repository, demonstrates that MR-TIM is more accurate than alternative approaches for whole-head tissue segmentation. We hope that MR-TIM, by yielding an increased precision in head modelling, will contribute to a more widespread use of EEG as a brain imaging technique.

Feasibility and Challenges of Performing Magnetoencephalography Experiments in Children With Arthrogryposis Multiplex Congenita

Arthrogryposis multiplex congenita (AMC) has recently drawn substantial attention from researchers and clinicians. New effective surgical and physiotherapeutic methods have been developed to improve the quality of life of patients with AMC. While it is clear that all these interventions should strongly rely on the plastic reorganization of the central nervous system, almost no studies have investigated this topic. The present study demonstrates the feasibility of using magnetoencephalography (MEG) to investigate brain activity in young AMC patients. We also outlined the general challenges and limitations of electrophysiological investigations on patients with arthrogryposis. We conducted MEG recordings using a 306-channel Elekta Neuromag VectorView system during a cued motor task performance in four patients with arthrogryposis, five normally developed children, and five control adults. Following the voice command of the experimenter, each subject was asked to bring their hand toward their mouth to imitate the self-feeding process. Two patients had latissimus dorsi transferred to the biceps brachii position, one patient had a pectoralis major transferred to the biceps brachii position, and one patient had no elbow flexion restoration surgery before the MEG investigation. Three patients who had undergone autotransplantation prior to the MEG investigation demonstrated activation in the sensorimotor area contralateral to the elbow flexion movement similar to the healthy controls. One patient who was recorded before the surgery demonstrated subjectively weak distributed bilateral activation during both left and right elbow flexion. Visual inspection of MEG data suggested that neural activity associated with motor performance was less pronounced and more widely distributed across the cortical areas of patients than of healthy control subjects. In general, our results could serve as a proof of principle in terms of the application of MEG in studies on cortical activity in patients with AMC. Reported trends might be consistent with the idea that prolonged motor deficits are associated with more difficult neuronal recruitment and the spatial heterogeneity of neuronal sources, most likely reflecting compensatory neuronal mechanisms. On the practical side, MEG could be a valuable technique for investigating the neurodynamics of patients with AMC as a function of postoperative abilitation.

Importance of general adiposity, visceral adiposity and vital signs in predicting blood biomarkers using machine learning

INTRODUCTION

Blood biomarkers are measured for their ability to characterise physiological and disease states. Much is known about linear relations between blood biomarker concentrations and individual vital signs or adiposity indexes (eg, BMI). Comparatively little is known about non-linear relations with these easily accessible features, particularly when they are modelled in combination and can potentially interact with one another.

METHODS

In this study, we used advanced machine learning algorithms to create non-linear computational models for predicting blood biomarkers (cells, lipids, metabolic factors) from age, general adiposity (BMI), visceral adiposity (Waist-to-Height Ratio, a Body Shape Index) and vital signs (systolic blood pressure, diastolic blood pressure, pulse). We determined the predictive power of the overall feature set. We further calculated feature importance in our models to identify the features with the strongest relations with each blood biomarker. Data were collected in 2018 and 2019 and analysed in 2020.

RESULTS

Our findings characterise previously unknown relations between these predictors and blood biomarkers; in many instances the importance of certain features or feature classes (general adiposity, visceral adiposity or vital signs) differed from their expected contribution based on simplistic linear modelling techniques.

CONCLUSIONS

This work could lead to the formation of new hypotheses for explaining complex biological systems and informs the creation of predictive models for potential clinical applications.

Modulation of neural oscillations during working memory update, maintenance, and readout: An hdEEG study

Working memory (WM) performance is very often measured using the n‐back task, in which the participant is presented with a sequence of stimuli, and required to indicate whether the current stimulus matches the one presented n steps earlier. In this study, we used high‐density electroencephalography (hdEEG) coupled to source localization to obtain information on spatial distribution and temporal dynamics of neural oscillations associated with WM update, maintenance and readout. Specifically, we a priori selected regions from a large fronto‐parietal network, including also the insula and the cerebellum, and we analyzed modulation of neural oscillations by event‐related desynchronization and synchronization (ERD/ERS). During update and readout, we found larger θ ERS and smaller β ERS respect to maintenance in all the selected areas. γ_LOW and γ_HIGH bands oscillations decreased in the frontal and insular cortices of the left hemisphere. In the maintenance phase we observed decreased θ oscillations and increased β oscillations (ERS) in most of the selected posterior areas and focally increased oscillations in γ_LOW and γ_HIGH bands in the frontal and insular cortices of the left hemisphere. Finally, during WM readout, we also found a focal modulation of the γ_LOW band in the left fusiform cortex and cerebellum, depending on the response trial type (true positive vs. true negative). Overall, our study demonstrated specific spectral signatures associated with updating of memory information, WM maintenance, and readout, with relatively high spatial resolution.

Tunable Piezoelectricity of Multifunctional Boron Nitride Nanotube/Poly(dimethylsiloxane) Stretchable Composites

Boron nitride nanotubes (BNNT) uniformly dispersed in stretchable materials, such as poly(dimethylsiloxane) (PDMS), could create the next generation of composites with augmented mechanical, thermal, and piezoelectric characteristics. This work reports tunable piezoelectricity of multifunctional BNNT/PDMS stretchable composites prepared via co-solvent blending with tetrahydrofuran (THF) to disperse BNNTs in PDMS while avoiding sonication or functionalization. The resultant stretchable BNNT/PDMS composites demonstrate augmented Young's modulus (200% increase at 9 wt% BNNT) and thermal conductivity (120% increase at 9 wt% BNNT) without losing stretchability. Furthermore, BNNT/PDMS composites demonstrate piezoelectric responses that are linearly proportional to BNNT wt%, achieving a piezoelectric constant (|d₃₃ |) of 18 pmV^-1 at 9 wt% BNNT without poling, which is competitive with commercial piezoelectric polymers. Uniquely, BNNT/PDMS accommodates tensile strains up to 60% without plastic deformation by aligning BNNTs, which enhances the composites' piezoelectric response approximately five times. Finally, the combined stretchable and piezoelectric nature of the composite was exploited to produce a vibration sensor sensitive to low-frequency (≈1 kHz) excitation. This is the first demonstration of multifunctional, stretchable BNNT/PDMS composites with enhanced mechanical strength and thermal conductivity and furthermore tunable piezoelectric response by varying BNNT wt% and applied strain, permitting applications in soft actuators and vibration sensors.

Frequency-dependent functional connectivity in resting state networks

Functional magnetic resonance imaging studies have documented the resting human brain to be functionally organized in multiple large‐scale networks, called resting‐state networks (RSNs). Other brain imaging techniques, such as electroencephalography (EEG) and magnetoencephalography (MEG), have been used for investigating the electrophysiological basis of RSNs. To date, it is largely unclear how neural oscillations measured with EEG and MEG are related to functional connectivity in the resting state. In addition, it remains to be elucidated whether and how the observed neural oscillations are related to the spatial distribution of the network nodes over the cortex. To address these questions, we examined frequency‐dependent functional connectivity between the main nodes of several RSNs, spanning large part of the cortex. We estimated connectivity using band‐limited power correlations from high‐density EEG data collected in healthy participants. We observed that functional interactions within RSNs are characterized by a specific combination of neuronal oscillations in the alpha (8–13 Hz), beta (13–30 Hz), and gamma (30–80 Hz) bands, which highly depend on the position of the network nodes. This finding may contribute to a better understanding of the mechanisms through which neural oscillations support functional connectivity in the brain.

Small-World Propensity Reveals the Frequency Specificity of Resting State Networks

Goal: Functional connectivity (FC) is an important indicator of the brain's state in different conditions, such as rest/task or health/pathology. Here we used high-density electroencephalography coupled to source reconstruction to assess frequency-specific changes of FC during resting state. Specifically, we computed the Small-World Propensity (SWP) index to characterize network small-world architecture across frequencies. Methods: We collected resting state data from healthy participants and built connectivity matrices maintaining the heterogeneity of connection strengths. For a subsample of participants, we also investigated whether the SWP captured FC changes after the execution of a working memory (WM) task. Results: We found that SWP demonstrated a selective increase in the alpha and low beta bands. Moreover, SWP was modulated by a cognitive task and showed increased values in the bands entrained by the WM task. Conclusions: SWP is a valid metric to characterize the frequency-specific behavior of resting state networks.