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, Mauro Di Marco, Alberto Tesi, Mauro Forti
Published: 10 June 2021
Frontiers in Neuroscience, Volume 15; doi:10.3389/fnins.2021.681035

Abstract:
Since the introduction of memristors, it has been widely recognized that they can be successfully employed as synapses in neuromorphic circuits. This paper focuses on showing that memristor circuits can be also used for mimicking some features of the dynamics exhibited by neurons in response to an external stimulus. The proposed approach relies on exploiting multistability of memristor circuits, i.e., the coexistence of infinitely many attractors, and employing a suitable pulse-programmed input for switching among the different attractors. Specifically, it is first shown that a circuit composed of a resistor, an inductor, a capacitor and an ideal charge-controlled memristor displays infinitely many stable equilibrium points and limit cycles, each one pertaining to a planar invariant manifold. Moreover, each limit cycle is approximated via a first-order periodic approximation analytically obtained via the Describing Function (DF) method, a well-known technique in the Harmonic Balance (HB) context. Then, it is shown that the memristor charge is capable to mimic some simplified models of the neuron response when an external independent pulse-programmed current source is introduced in the circuit. The memristor charge behavior is generated via the concatenation of convergent and oscillatory behaviors which are obtained by switching between equilibrium points and limit cycles via a properly designed pulse timing of the current source. The design procedure takes also into account some relationships between the pulse features and the circuit parameters which are derived exploiting the analytic approximation of the limit cycles obtained via the DF method.
Dongpeng Wu, Han Zhao, Huali Gu, Bin Han, Qingqing Wang, Xu Man, Renliang Zhao, Xuejun Liu, Jinping Sun
Published: 10 June 2021
Frontiers in Neuroscience, Volume 15; doi:10.3389/fnins.2021.677823

Abstract:
Background There is evidence that the T allele of rs405509 located in the apolipoprotein E (APOE) promotor region is a risk factor for Alzheimer’s disease (AD). However, the effect of the T/T allele on brain function in non-demented aging is still unclear. Methods We analyzed the effects of the rs405509 T/T allele on cognitive performances using multiple neuropsychological tests and local brain function using resting-state functional magnetic resonance imaging (rs-fMRI). Results Significant differences were found between T/T carriers and G allele carriers on general cognitive status, memory, and attention (p < 0.05). Rs-fMRI analyses demonstrated decreased amplitude of low frequency fluctuation (ALFF) in the right middle frontal gyrus, decreased percent amplitude of fluctuation (PerAF) in the right middle frontal gyrus, increased regional homogeneity (ReHo) in the right cerebellar tonsil and decreased ReHo in the right putamen, and decreased degree centrality (DC) in the left middle frontal gyrus (p < 0.05, corrected). Furthermore, significant correlations were found between cognitive performance and these neuroimaging changes (p < 0.05). Conclusion These findings suggest that T/T allele may serve as an independent risk factor that can influence brain function in different regions in non-demented aging.
, Joel M. Cooper, Gus G. Erickson, Trent G. Simmons, Amy S. McDonnell, Amanda E. Carriero, Kaedyn W. Crabtree, David L. Strayer
Published: 10 June 2021
Frontiers in Neuroscience, Volume 15; doi:10.3389/fnins.2021.577418

Abstract:
Introduction Partial driving automation is not always reliable and requires that drivers maintain readiness to take over control and manually operate the vehicle. Little is known about differences in drivers’ arousal and cognitive demands under partial automation and how it may make it difficult for drivers to transition from automated to manual modes. This research examined whether there are differences in drivers’ arousal and cognitive demands during manual versus partial automation driving. Method We compared arousal (using heart rate) and cognitive demands (using the root mean square of successive differences in normal heartbeats; RMSSD, and Detection Response Task; DRT) while 39 younger (M = 28.82 years) and 32 late-middle-aged (M = 52.72 years) participants drove four partially automated vehicles (Cadillac, Nissan Rogue, Tesla, and Volvo) on interstate highways. If compared to manual driving, drivers’ arousal and cognitive demands were different under partial automation, then corresponding differences in heart rate, RMSSD, and DRT would be expected. Alternatively, if drivers’ arousal and cognitive demands were similar in manual and partially automated driving, no difference in the two driving modes would be expected. Results Results suggest no significant differences in heart rate, RMSSD, or DRT reaction time performance between manual and partially automated modes of driving for either younger or late-middle-aged adults across the four test vehicles. A Bayes Factor analysis suggested that heart rate, RMSSD, and DRT data showed extreme evidence in favor of the null hypothesis. Conclusion This novel study conducted on real roads with a representative sample provides important evidence of no difference in arousal and cognitive demands. Younger and late-middle-aged motorists who are new to partial automation are able to maintain arousal and cognitive demands comparable to manual driving while using the partially automated technology. Drivers who are more experienced with partially automated technology may respond differently than those with limited prior experience.
Jennifer A. Liu, James C. Walton, A. Courtney DeVries, Randy J. Nelson
Published: 10 June 2021
Frontiers in Neuroscience, Volume 15; doi:10.3389/fnins.2021.675732

Abstract:
Several endogenous and exogenous factors interact to influence stroke occurrence, in turn contributing to discernable daily distribution patterns in the frequency and severity of cerebrovascular events. Specifically, strokes that occur during the morning tend to be more severe and are associated with elevated diastolic blood pressure, increased hospital stay, and worse outcomes, including mortality, compared to strokes that occur later in the day. Furthermore, disrupted circadian rhythms are linked to higher risk for stroke and play a role in stroke outcome. In this review, we discuss the interrelation among core clock genes and several factors contributing to ischemic outcomes, sources of disrupted circadian rhythms, the implications of disrupted circadian rhythms in foundational stroke scientific literature, followed by a review of clinical implications. In addition to highlighting the distinct daily pattern of onset, several aspects of physiology including immune response, endothelial/vascular and blood brain barrier function, and fibrinolysis are under circadian clock regulation; disrupted core clock gene expression patterns can adversely affect these physiological processes, leading to a prothrombotic state. Lastly, we discuss how the timing of ischemic onset increases morning resistance to thrombolytic therapy and the risk of hemorrhagic transformation.
, Leigh L. Van Den Heuvel, Cathryn M. Lewis, Soraya Seedat, Sian M. J. Hemmings
Published: 10 June 2021
Frontiers in Neuroscience, Volume 15; doi:10.3389/fnins.2021.677800

Abstract:
Posttraumatic stress disorder (PTSD) is a trauma-related disorder that frequently co-occurs with metabolic syndrome (MetS). MetS is characterized by obesity, dyslipidemia, and insulin resistance. To provide insight into these co-morbidities, we performed a genome-wide association study (GWAS) meta-analysis to identify genetic variants associated with PTSD, and determined if PTSD polygenic risk scores (PRS) could predict PTSD and MetS in a South African mixed-ancestry sample. The GWAS meta-analysis of PTSD participants (n = 260) and controls (n = 343) revealed no SNPs of genome-wide significance. However, several independent loci, as well as five SNPs in the PARK2 gene, were suggestively associated with PTSD (p < 5 × 10–6). PTSD-PRS was associated with PTSD diagnosis (Nagelkerke’s pseudo R 2 = 0.0131, p = 0.00786), PTSD symptom severity [as measured by CAPS-5 total score (R 2 = 0.00856, p = 0.0367) and PCL-5 score (R 2 = 0.00737, p = 0.0353)], and MetS (Nagelkerke’s pseudo R 2 = 0.00969, p = 0.0217). These findings suggest an association between PTSD and PARK2, corresponding with results from the largest PTSD-GWAS conducted to date. PRS analysis suggests that genetic variants associated with PTSD are also involved in the development of MetS. Overall, the results contribute to a broader goal of increasing diversity in psychiatric genetics.
Zhexuan Wang, Chenli Feng, Ruyi Yang, Tingting Liu, Yin Chen, Aihua Chen, , ,
Published: 10 June 2021
Frontiers in Neuroscience, Volume 15; doi:10.3389/fnins.2021.617175

Abstract:
Photocoagulation is used for the treatment of retinal ischemic disease. However, due to the invasive nature of photocoagulation and variety of melanin concentrations between individuals, it is challenging to avoid damaging the adjacent photoreceptors and inducing several side effects. Previous studies indicate the role of laser power, duration, and spot size on retinal lesions, but the effect of interspot distance of the laser pulses needs to be considered in panretinal photocoagulation. In this study, we examine different parameters of photocoagulation on lesions of the retina in rabbit, finding that the lesion level of the outer nuclear layer of the retina depended on the pulse duration and laser spot size, and decreasing interspot distance could completely abolish the photoreceptor layer. The degeneration of the photoreceptor by photocoagulation occurred in 24 h and was not restored afterward. We then conducted panretinal photocoagulation in rabbit and found that oxidative stress was decreased in the inner nuclear layer of the retina, and pupillary light reflex and ERG signals were impaired. Our study could provide a rabbit model to explore the mechanism of photoreceptor degeneration and therapies for the side effects after photocoagulation.
Published: 10 June 2021
Frontiers in Neuroscience, Volume 15; doi:10.3389/fnins.2021.655247

Abstract:
A prominent goal of neuroscience is to improve our understanding of how brain structure and activity interact to produce perception, emotion, behavior, and cognition. The brain’s network activity is inherently organized in distinct spatiotemporal patterns that span scales from nanometer-sized synapses to meter-long nerve fibers and millisecond intervals between electrical signals to decades of memory storage. There is currently no single imaging method that alone can provide all the relevant information, but intelligent combinations of complementary techniques can be effective. Here, we thus present the latest advances in biomedical and biological engineering on photoacoustic neuroimaging in the context of complementary imaging techniques. A particular focus is placed on recent advances in whole-brain photoacoustic imaging in rodent models and its influential role in bridging the gap between fluorescence microscopy and more non-invasive techniques such as magnetic resonance imaging (MRI). We consider current strategies to address persistent challenges, particularly in developing molecular contrast agents, and conclude with an overview of potential future directions for photoacoustic neuroimaging to provide deeper insights into healthy and pathological brain processes.
, Alix Lamouroux, Christophe Rocher, Jules Bouvet, Giulia Lioi
Published: 10 June 2021
Frontiers in Neuroscience, Volume 15; doi:10.3389/fnins.2021.626723

Abstract:
In this paper, we describe the results of a single subject study attempting at a better understanding of the subjective mental state during musical improvisation. In a first experiment, we setup an ecological paradigm measuring EEG on a musician in free improvised concerts with an audience, followed by retrospective rating of the mental state of the improviser. We introduce Subjective Temporal Resolution (STR), a retrospective rating assessing the instantaneous quantization of subjective timing of the improviser. We identified high and low STR states using Hidden Markov Models in two performances, and were able to decode those states using supervised learning on instantaneous EEG power spectrum, showing increases in theta and alpha power with high STR values. In a second experiment, we found an increase of theta and beta power when experimentally manipulating STR in a musical improvisation imagery experiment. These results are interpreted with respect to previous research on flow state in creativity, as well as with the temporal processing literature. We suggest that a component of the subjective state of musical improvisation may be reflected in an underlying mechanism related to the subjective quantization of time. We also demonstrate the feasibility of single case studies of musical improvisation using brain activity measurements and retrospective reports, by obtaining consistent results across multiple sessions.
Fan Jiang, Xiaopeng Huang, Houxue Xia, Bingqi Wang, , Bin Zhang, Jun Jiang
Frontiers in Neuroscience, Volume 15; doi:10.3389/fnins.2021.686932

Abstract:
Purpose To determine if the spatial distribution of the relative corneal refractive power shift (RCRPS) explains the retardation of axial length (AL) elongation after treatment by either orthokeratology (OK) or multifocal soft contact lenses (MFCLs). Methods Children (8–14 years) were enrolled in the OK (n = 35) or MFCL (n = 36) groups. RCRPS maps were derived by computing the difference between baseline and 12-month corneal topography maps and then subtracting the apex values. Values at the same radius were averaged to obtain the RCRPS profile, from which four parameters were extracted: (1) Half_x and (2) Half_y, i.e., the x- and y-coordinates where each profile first reached the half peak; (3) Sum4 and (4) Sum7, i.e., the summation of powers within a corneal area of 4- and 7-mm diameters. Correlations between AL elongation and these parameters were analyzed by multiple linear regression. Results AL elongation in the OK group was significantly smaller than that in the MFCL group (p = 0.040). Half_x and Half_y were also smaller in the OK group than the MFCL group (p < 0.001 each). Half_x was correlated with AL elongation in the OK group (p = 0.005), but not in the MFCL group (p = 0.600). In an analysis that combined eyes of both groups, Half_x was correlated with AL elongation (β = 0.161, p < 0.001). Conclusions The OK-induced AL elongation and associated RCRPS Half_x were smaller than for the MFCL. Contact lenses that induce RCRPS closer to the corneal center may exert better myopia control.
Laura Bonzano, Ambra Bisio, Ludovico Pedullà, Giampaolo Brichetto,
Frontiers in Neuroscience, Volume 15; doi:10.3389/fnins.2021.656856

Abstract:
Handwriting is a complex activity including motor planning and visuomotor integration and referring to some brain areas identified as “writing centers.” Although temporal features of handwriting are as important as spatial ones, to our knowledge, there is no evidence of the description of specific brain areas associated with handwriting tempo. People with multiple sclerosis (PwMS) show handwriting impairments that are mainly referred to as the temporal features of the task. The aim of this work was to assess differences in the brain activation pattern elicited by handwriting between PwMS and healthy controls (HC), with the final goal of identifying possible areas specific for handwriting tempo. Subjects were asked to write a sentence at their spontaneous speed. PwMS differed only in temporal handwriting features from HC and showed reduced activation with a subset of the clusters observed in HC. Spearman’s correlation analysis was performed between handwriting temporal parameters and the activity in the brain areas resulting from the contrast analysis, HC > PwMS. We found that the right inferior parietal lobule (IPL) negatively correlated with the duration of the sentence, indicating that the higher the right IPL activity, the faster the handwriting performance. We propose that the right IPL might be considered a “writing tempo center.”
Akothirene C. Dutra-Marques, Sara Rodrigues, Felipe X. Cepeda, Edgar Toschi-Dias, Eduardo Rondon, Jefferson C. Carvalho, Maria Janieire N. N. Alves, Ana Maria F. W. Braga, Maria Urbana P. B. Rondon,
Frontiers in Neuroscience, Volume 15; doi:10.3389/fnins.2021.680195

Abstract:
Introduction Exaggerated blood pressure response to exercise (EEBP = SBP ≥ 190 mmHg for women and ≥210 mmHg for men) during cardiopulmonary exercise test (CPET) is a predictor of cardiovascular risk. Sympathetic hyperactivation and decreased baroreflex sensitivity (BRS) seem to be involved in the progression of metabolic syndrome (MetS) to cardiovascular disease. Objective To test the hypotheses: (1) MetS patients within normal clinical blood pressure (BP) may present EEBP response to maximal exercise and (2) increased muscle sympathetic nerve activity (MSNA) and reduced BRS are associated with this impairment. Methods We selected MetS (ATP III) patients with normal BP (MetS_NT, n = 27, 59.3% males, 46.1 ± 7.2 years) and a control group without MetS (C, n = 19, 48.4 ± 7.4 years). We evaluated BRS for increases (BRS+) and decreases (BRS−) in spontaneous BP and HR fluctuations, MSNA (microneurography), BP from ambulatory blood pressure monitoring (ABPM), and auscultatory BP during CPET. Results Normotensive MetS (MetS_NT) had higher body mass index and impairment in all MetS risk factors when compared to the C group. MetS_NT had higher peak systolic BP (SBP) (195 ± 17 vs. 177 ± 24 mmHg, P = 0.007) and diastolic BP (91 ± 11 vs. 79 ± 10 mmHg, P = 0.001) during CPET than C. Additionally, we found that MetS patients with normal BP had lower spontaneous BRS− (9.6 ± 3.3 vs. 12.2 ± 4.9 ms/mmHg, P = 0.044) and higher levels of MSNA (29 ± 6 vs. 18 ± 4 bursts/min, P < 0.001) compared to C. Interestingly, 10 out of 27 MetS_NT (37%) showed EEBP (MetS_NT+), whereas 2 out of 19 C (10.5%) presented (P = 0.044). The subgroup of MetS_NT with EEBP (MetS_NT+, n = 10) had similar MSNA (P = 0.437), but lower BRS+ (P = 0.039) and BRS− (P = 0.039) compared with the subgroup without EEBP (MetS_NT−, n = 17). Either office BP or BP from ABPM was similar between subgroups MetS_NT+ and MetS_NT−, regardless of EEBP response. In the MetS_NT+ subgroup, there was an association of peak SBP with BRS− (R = −0.70; P = 0.02), triglycerides with peak SBP during CPET (R = 0.66; P = 0.039), and of triglycerides with BRS− (R = 0.71; P = 0.022). Conclusion Normotensive MetS patients already presented higher peak systolic and diastolic BP during maximal exercise, in addition to sympathetic hyperactivation and decreased baroreflex sensitivity. The EEBP in MetS_NT with apparent well-controlled BP may indicate a potential depressed neural baroreflex function, predisposing these patients to increased cardiovascular risk.
, Carl A. Verschuur
Frontiers in Neuroscience, Volume 15; doi:10.3389/fnins.2021.581414

Abstract:
Cochlear implants (CIs) have been remarkably successful at restoring speech perception for severely to profoundly deaf individuals. Despite their success, several limitations remain, particularly in CI users’ ability to understand speech in noisy environments, locate sound sources, and enjoy music. A new multimodal approach has been proposed that uses haptic stimulation to provide sound information that is poorly transmitted by the implant. This augmenting of the electrical CI signal with haptic stimulation (electro-haptic stimulation; EHS) has been shown to improve speech-in-noise performance and sound localization in CI users. There is also evidence that it could enhance music perception. We review the evidence of EHS enhancement of CI listening and discuss key areas where further research is required. These include understanding the neural basis of EHS enhancement, understanding the effectiveness of EHS across different clinical populations, and the optimization of signal-processing strategies. We also discuss the significant potential for a new generation of haptic neuroprosthetic devices to aid those who cannot access hearing-assistive technology, either because of biomedical or healthcare-access issues. While significant further research and development is required, we conclude that EHS represents a promising new approach that could, in the near future, offer a non-invasive, inexpensive means of substantially improving clinical outcomes for hearing-impaired individuals.
Jingcong Li, Shuqi Li, Jiahui Pan, Fei Wang
Frontiers in Neuroscience, Volume 15; doi:10.3389/fnins.2021.611653

Abstract:
As a physiological process and high-level cognitive behavior, emotion is an important subarea in neuroscience research. Emotion recognition across subjects based on brain signals has attracted much attention. Due to individual differences across subjects and the low signal-to-noise ratio of EEG signals, the performance of conventional emotion recognition methods is relatively poor. In this paper, we propose a self-organized graph neural network (SOGNN) for cross-subject EEG emotion recognition. Unlike the previous studies based on pre-constructed and fixed graph structure, the graph structure of SOGNN are dynamically constructed by self-organized module for each signal. To evaluate the cross-subject EEG emotion recognition performance of our model, leave-one-subject-out experiments are conducted on two public emotion recognition datasets, SEED and SEED-IV. The SOGNN is able to achieve state-of-the-art emotion recognition performance. Moreover, we investigated the performance variances of the models with different graph construction techniques or features in different frequency bands. Furthermore, we visualized the graph structure learned by the proposed model and found that part of the structure coincided with previous neuroscience research. The experiments demonstrated the effectiveness of the proposed model for cross-subject EEG emotion recognition.
, Rebekka Rupprecht, Ludger Tebartz Van Elst, Jürgen Kornmeier
Frontiers in Neuroscience, Volume 15; doi:10.3389/fnins.2021.569918

Abstract:
The electroencephalography (EEG) is a well-established non-invasive method in neuroscientific research and clinical diagnostics. It provides a high temporal but low spatial resolution of brain activity. To gain insight about the spatial dynamics of the EEG, one has to solve the inverse problem, i.e., finding the neural sources that give rise to the recorded EEG activity. The inverse problem is ill-posed, which means that more than one configuration of neural sources can evoke one and the same distribution of EEG activity on the scalp. Artificial neural networks have been previously used successfully to find either one or two dipole sources. These approaches, however, have never solved the inverse problem in a distributed dipole model with more than two dipole sources. We present ConvDip, a novel convolutional neural network (CNN) architecture, that solves the EEG inverse problem in a distributed dipole model based on simulated EEG data. We show that (1) ConvDip learned to produce inverse solutions from a single time point of EEG data and (2) outperforms state-of-the-art methods on all focused performance measures. (3) It is more flexible when dealing with varying number of sources, produces less ghost sources and misses less real sources than the comparison methods. It produces plausible inverse solutions for real EEG recordings from human participants. (4) The trained network needs <40 ms for a single prediction. Our results qualify ConvDip as an efficient and easy-to-apply novel method for source localization in EEG data, with high relevance for clinical applications, e.g., in epileptology and real-time applications.
Cuicui Jia, Yangpan Ou, Yunhui Chen, Jidong Ma, Chuang Zhan, Dan Lv, Ru Yang, Tinghuizi Shang, Lei Sun, Yuhua Wang, et al.
Frontiers in Neuroscience, Volume 15; doi:10.3389/fnins.2021.634557

Abstract:
Disrupted functional asymmetry of cerebral hemispheres may be altered in patients with obsessive-compulsive disorder (OCD). However, little is known about whether anomalous brain asymmetries originate from inter- and/or intra-hemispheric functional connectivity (FC) at rest in OCD. In this study, resting-state functional magnetic resonance imaging was applied to 40 medication-free patients with OCD and 38 gender-, age-, and education-matched healthy controls (HCs). Data were analyzed using the parameter of asymmetry (PAS) and support vector machine methods. Patients with OCD showed significantly increased PAS in the left posterior cingulate cortex, left precentral gyrus/postcentral gyrus, and right inferior occipital gyrus and decreased PAS in the left dorsolateral prefrontal cortex (DLPFC), bilateral middle cingulate cortex (MCC), left inferior parietal lobule, and left cerebellum Crus I. A negative correlation was found between decreased PAS in the left DLPFC and Yale–Brown Obsessive-compulsive Scale compulsive behavior scores in the patients. Furthermore, decreased PAS in the bilateral MCC could be used to distinguish OCD from HCs with a sensitivity of 87.50%, an accuracy of 88.46%, and a specificity of 89.47%. These results highlighted the contribution of disrupted asymmetry of intra-hemispheric FC within and outside the cortico-striato-thalamocortical circuits at rest in the pathophysiology of OCD, and reduced intra-hemispheric FC in the bilateral MCC may serve as a potential biomarker to classify individuals with OCD from HCs.
Zhongliang Yin, Yue Wang, Minghao Dong, Shenghan Ren, Haihong Hu, Kuiying Yin, Jimin Liang
Frontiers in Neuroscience, Volume 15; doi:10.3389/fnins.2021.652920

Abstract:
Face processing is a spatiotemporal dynamic process involving widely distributed and closely connected brain regions. Although previous studies have examined the topological differences in brain networks between face and non-face processing, the time-varying patterns at different processing stages have not been fully characterized. In this study, dynamic brain networks were used to explore the mechanism of face processing in human brain. We constructed a set of brain networks based on consecutive short EEG segments recorded during face and non-face (ketch) processing respectively, and analyzed the topological characteristic of these brain networks by graph theory. We found that the topological differences of the backbone of original brain networks (the minimum spanning tree, MST) between face and ketch processing changed dynamically. Specifically, during face processing, the MST was more line-like over alpha band in 0–100 ms time window after stimuli onset, and more star-like over theta and alpha bands in 100–200 and 200–300 ms time windows. The results indicated that the brain network was more efficient for information transfer and exchange during face processing compared with non-face processing. In the MST, the nodes with significant differences of betweenness centrality and degree were mainly located in the left frontal area and ventral visual pathway, which were involved in the face-related regions. In addition, the special MST patterns can discriminate between face and ketch processing by an accuracy of 93.39%. Our results suggested that special MST structures of dynamic brain networks reflected the potential mechanism of face processing in human brain.
Shai Baharav, Gal Nitsan,
Frontiers in Neuroscience, Volume 15; doi:10.3389/fnins.2021.663930

Abstract:
A Commentary on Working Memory Load Affects Processing Time in Spoken Word Recognition: Test Retest Reliability of the E-WINDMIL Eyetracking Paradigm by Hadar, B., Skrzypek, J. E., Wingfield, A., and Ben-David, B. M. (2016). Front. Neurosci. 10:221. doi: 10.3389/fnins.2016.00221 A paper by Hadar et al. (2016) published in this journal suggested a new eyetracking paradigm to gauge the cognitive load associated with speech processing. This was accomplished using an adapted version of the eyetracking Visual World Paradigm (VWP). Young normal-hearing listeners completed a spoken word identification task with a concurrent working memory load task as their eye-gaze on the monitor was recorded. While following spoken instructions to touch an object (out of four objects presented on the monitor), the listener was asked to retain either 1 or 4 digits (low load or high load) for later recall. Eye-fixations on a named target-object were compared to fixations on an object whose name had shared phonology (e.g., toweR and toweL), as the spoken word (named target-object) unfolded in time. Results indicated the important role working memory plays in speech perception, even when performed by younger adults in ideal listening conditions. A recent paper by Nitsan et al. (2019), extended this paradigm and tested the effect of individual differences in working memory capacity on spoken word identification in noise. This adapted paradigm, coined the E-WINDMIL (Eyetracking of Word Identification in Noise Under Memory Increased Load), further highlighted the role of cognitive resources in speech processing, even with younger adults. As researchers increasingly apply the VWP in clinical settings to study speech processing in aging, we were asked whether this paradigm is reliable like the common VWP (Farris-Trimble and McMurray, 2013) across the lifespan. In response, we investigated the test-retest reliability of the E-WINDMIL in both younger and older adults. Twenty-four younger adults (M age = 25.34 years, SD = 1.61 years) and 24 older adults (M age = 69.04 years, SD = 3.61 years) were recruited. Inclusion criteria closely mimicked our previous studies: clinically normal visual acuity, color-vision, pure-tone audiometric thresholds, language proficiency, forward digit span and basic cognitive diagnosis MoCA for older adults. Out of 34 younger adults tested, six were excluded due to loss of eye tracker signal in at least one of the test sessions and four were excluded due to attrition, as they did not return to the second experimental session. The final number of young participants, 24, matched the original Hadar et al. study. Hadar et al.'s methodology (using Hebrew spoken words) was also closely followed, with the following changes: (1) Spoken instructions were mixed with a continuous speech spectrum noise at −4 dB and 0 dB signal-to-noise ratios for young and older adults, respectively; (2) Participants completed the task twice following a 2-week interval; and (3) Two image sets and four test versions were created from the original studies to prevent learning of the paradigm stimuli. As such, no participant viewed images nor heard target-object instructions from the first session in the second session (see counterbalancing descriptions here: www.canlab.idc.ac.il/ewindmil). Growth curve analysis (Mirman, 2014) was used to analyze the time course of fixations on the target-object, from word onset to 200 ms after average word offset, for each age-group separately. Growth Curve Analysis, a multilinear polynomial regression model, is commonly used to model the Visual World Paradigm (Mirman, 2014; Nitsan et al., 2019) demonstrating sufficient statistical power with the sample size chosen for this study. The overall time course of target fixations was captured with a third order (cubic) orthogonal polynomial with fixed effects of load (1 vs. 4 digits preload), and test-session (test vs. retest) on all time terms, and participant random effects on all time terms (note, item order was fully randomized). Onset vs. offset phonemic competitors were modeled separately. Apart from the younger adult offset model, there was no effect of test-session on the time terms in both groups, indicating no significant difference in the rate or number of fixations on the target between the two testing sessions (all p > 0.5) in either age group (See Figure 1). However, in the younger adult offset model we witnessed an effect of test-session on the time terms indicating slightly faster fixations to target in the second session (For all tables see Supplementary Materials). This analysis suggests E-WINDMIL's test-retest reliability. The model's syntax and coefficients can be found here: https://github.com/G-Nitsan/GCA-test-retest-2020. Figure 1. Mean proportion of fixations to the target for older adults in the offset competitor condition compared across test-session 1 and 2 according to low (1 digit) or high (4 digit) working memory preload. The red line indicates test-session 1 and the blue line indicates test-session 2 following a 2 week interval. The model fits (smooth lines) are plotted along with the observed target fixation data. The test-retest reliability of the E-WINDMIL may facilitate investigating the interaction of cognition and speech processing within clinical settings. Further research is needed to determine how this version of VWP could be used. For example, the E-WINDMILL may serve as a far-transfer gauge of working memory for diagnosis or even cognitive training. This study calls for increased efforts to verify the reliability of tools that may provide new paths for cognitive assessment in aging (Ben-David et al., 2018). SB, GN, and BB-D co-authored the commentary with SB acting as main writer and BB-D as corresponding author. BB-D was head of the lab and architect of the study. GN ran analysis. SB designed and ran the experiment. All authors contributed to the article and approved the submitted version. The...
Xiaohan Xu, Kexin Wang, Xuezhao Cao, Zhe Li, Yongjian Zhou, Jiancong Ren,
Frontiers in Neuroscience, Volume 15; doi:10.3389/fnins.2021.664641

Abstract:
Accumulating evidence has demonstrated that damages of gut microbiota are strongly associated with central nervous system (CNS) diseases, such as perioperative neurocognitive disorders (PND). The present study investigated the role of gut microbial metabolite short-chain fatty acids (SCFAs) in surgery-induced cognitive deficits and neuroinflammation in the hippocampus. Adult male C57BL/6J mice received either SCFA mixture or saline orally for 4 weeks, and then partial hepatectomy was performed. The fecal supernatant of surgical mice was transplanted to normal mice for 3 weeks. The Morris water maze (MWM) and open-field tests were used to evaluate behavioral performance on postoperative or post-transplantation days 3 and 7. In the MWM test, pretreatment with exogenous SCFAs partially reversed surgery-induced impairments in crossing times and the time spent in the target quadrant on postoperative day 3 (p < 0.05, p < 0.05, respectively). In the open-field test, compared with the surgical mice, exogenous SCFA administration prior to surgery partially improved the locomotor activity (p < 0.05) and anxiety-like behavior (p < 0.05) on postoperative day 3. Surgical trauma and anesthesia enhanced ionized calcium-binding adapter molecule 1 (Iba-1) expression (p < 0.001), increased the levels of interleukin (IL)-1β (p < 0.001) and IL-6 (p < 0.001), and inhibited SCFA production (p < 0.001) on postoperative day 3. The expression of the brain-derived neurotrophic factor (BDNF) was also decreased (p < 0.001). Overall, surgical trauma and anesthesia exacerbated cognitive impairment, enhanced neuroinflammatory responses, and inhibited SCFA production. Pretreatment with SCFAs attenuated these effects partially by reversing microglial overactivation, inhibiting neuroinflammatory responses, and enhancing BDNF expression.
Frontiers in Neuroscience, Volume 15; doi:10.3389/fnins.2021.683122

Abstract:
Editorial on the Research Topic Stem Cells in Neurodegeneration: Disease Modeling and Therapeutics With the increasing number of people with neurodegenerative disease world-wide, novel directions and paradigms are sought to understand disease-specific neuronal death and to offer novel therapeutic strategies to patients. The ability to transform a differentiated cell into a pluripotent state and to differentiate again to a defined target cell type (Takahashi and Yamanaka, 2006) has led to the hope of the development of novel therapeutics. Although this holds promise in many fields, there is an extra challenge to replace any lost post-mitotic cells, such as neurons in neurodegeneration. With the development of replacement therapy for certain neuronal subtypes, e.g. dopaminergic neurons in models of Parkinson's Disease (PD) (Wakeman et al., 2017), there are remaining challenges with the long axons grown throughout development, e.g., in motor neurons in diseases such as Amyotrophic Lateral Sclerosis (ALS). Despite that stem cell-derived neurons may not always be suitable for replacement therapies, they are extremely informative in disease modeling to understand more of human disease as well as to discover and develop novel therapeutics. This Research Topic has included original and review papers spanning both topics, therapeutics and disease modeling, and discuss a large number of neuronal cell types. In the context of ALS and Frontal Temporal Dementia (FTD), Guo et al. summarize the disease-specific phenotypes in patient-derived IPSC-derived neurons and the variability between reports (Guo et al.), identifying the limitations of these approaches, including the variable genetic backgrounds, off-target effects of genetic corrections or targeting, the lacking cellular maturity, and the heterogeneity of differentiation techniques. This latter point is further detailed by Ghaffari et al. whom provide an impressive deep-dive into the different means of differentiation of specific neuronal and glial subtypes in detail, and compare and contrast IPSCs and direct conversion. Despite these challenges, the use of patient-derived tissue is strongly recommended in disease-modeling and preclinical ALS studies for drug discovery and development (van den Berg et al., 2019). Besides disease modeling and the replacement of neurons, stem cell derived cells can also be developed as a therapeutic strategy to support neuronal survival, instead of adopting a neuronal fate. A summary of these strategies and their (pre)clinical support in ALS is provided by the article from Forostyak and Sykova, in which they outline the terminology and cell types that have been published. In particular, they highlight the protective effects on motor neurons that mesenchymal stromal cells offer by producing neurotrophic factors upon transplantation (Forostyak and Sykova). With the epigenetic markers of cellular maturity lost when cells are converted to iPSCs (Mertens et al., 2015; Traxler et al., 2019), it can be challenging to detect late onset disease-specific pathology in iPSC-derived neurons. Seminary et al., recapitulated an impaired heat shock response in iPSC-derived motor neurons harboring ALS mutations, In addition, with this model they identified an accumulation of insoluble and aggregation-prone proteins, and that the presence of these was not sufficient to induce a heat shock response or stress-granule formation (Seminary et al.). The importance of how a gene may cause disease and whether that mechanism remains present in iPSC-derived neurons is also of relevance in the context of the regulation of the gene SNCA, encoding for the protein alpha-synuclein, in Parkinson's Disease (Piper et al.). Piper et al. describe in-depth the different ways SNCA may genetically cause disease, as well as how SCNA may be regulated. The authors stress the importance of the understanding of temporal and cell type-specific regulation of SNCA in disease, and in disease-models (Piper et al.). In Huntington's disease (HD), this Research Topic's contributions span the increased understanding of pathophysiology, testing of novel therapeutics, and the transplantation of cells as a potential therapeutic. Naphade et al. used patient-derived, isogenic, and control-corrected IPSCs to generate neural stem cells to assess the role of matrix metalloproteinases (MMPs) and their inhibitors in HD (Naphade et al.). They found that MMPs' endogenous inhibitors are decreased in HD cells and are elevated by TGFb treatment (Naphade et al.), illustrating a potential new direction for HD therapeutic strategies. Rindt et al. used a similar technique to assess the potential of the pre-mRNA repair of mutant Huntingtin, and also identified a beneficial response in HD-derived IPSC neural models (Rindt et al.). Subsequnetly, Masnata and Cicchetti describe the evidence for seeding of the Huntingtin protein in in vitro cultures of HD, including by IPSC disease modeling, and find sufficient evidence to suggest that this occurs, prompting the conclusion that in vivo assessment is now needed to further assess this (Masnata and Cicchetti). Al-Gharaibeh et al. transplanted IPSC-derived neural stem cells as a potential therapeutic into the striata of aged HD model mice (YAC128) and observed a striking protective effect on pathology and behavior in these animals (Al-Gharaibeh et al.), indicating the potential for neuronal replacement therapy in HD. To understand the potential and limitations of the use of non-primary neurons, Drouin-Ouellet et al. summarize the use of inducible neurons (iNeurons), which are derived from direct differentiation from somatic cells (Drouin-Ouellet et al.). They delineate what constitutes an iNeuron, describe the benefits of each stage of differentiation per neurological disease, discuss whether generating subtype-specific iNeurons is critical to the disease-related features of these cells, and subsequently explain the biomedical potential and...
, Nadine Steingräber, Joachim Gross
Frontiers in Neuroscience, Volume 15; doi:10.3389/fnins.2021.682419

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Recording brain activity during speech production using magnetoencephalography (MEG) can help us to understand the dynamics of speech production. However, these measurements are challenging due to the induced artifacts coming from several sources such as facial muscle activity, lower jaw and head movements. Here, we aimed to characterize speech-related artifacts, focusing on head movements, and subsequently present an approach to remove these artifacts from MEG data. We recorded MEG from 11 healthy participants while they pronounced various syllables in different loudness. Head positions/orientations were extracted during speech production to investigate its role in MEG distortions. Finally, we present an artifact rejection approach using the combination of regression analysis and signal space projection (SSP) in order to correct the induced artifact from MEG data. Our results show that louder speech leads to stronger head movements and stronger MEG distortions. Our proposed artifact rejection approach could successfully remove the speech-related artifact and retrieve the underlying neurophysiological signals. As the presented artifact rejection approach was shown to remove artifacts arising from head movements, induced by overt speech in the MEG, it will facilitate research addressing the neural basis of speech production with MEG.
, Marc Jarczok, Andrew Owens, Julian F. Thayer
Frontiers in Neuroscience, Volume 15; doi:10.3389/fnins.2021.564159

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According to the Johns Hopkins Coronavirus Resources Center, the number of confirmed COVID-19 cases exceeds 170,558,922 worldwide today with more than 3,546,881 fatalities (2021). The pandemic's massive health and well-being issues have already impacted the lives of millions globally, including spikes in mortality and morbidity. Many nations were unprepared for these significant consequences, revealing the critical need for standard public health principles of population assessment, intervention, and treatment. This editorial will address an innovative use of Heart Rate Variability (HRV), which is a non-invasive, inexpensive, and sensitive measure of inflammatory processes and immunomodulation (Kovatchev et al., 2003; Ahmad et al., 2009; Leitzke et al., 2020; Owens, 2020), among other health and well-being parameters. Specifically, the vagus nerve maintains tonic inhibitory control of proinflammatory cytokines via acetylcholine release into the reticuloendothelial system (e.g., spleen, gastrointestinal tract, heart, liver), mediating the inflammatory reflex through the cholinergic anti-inflammatory pathway (Dantzer and Kelley, 2007). HRV has been described in this special HRV Horizons 2030 Frontiers series as follows: “HRV offers insights into humoral, neural, and neurovisceral processes in health and disorders of brain, body, and behavior but has yet to be fully potentiated in the digital age” (Owens, 2020). Building on a growing body of HRV data (Rangon et al., 2020; Whitelaw et al., 2020; Hirten et al., 2021), we propose use of a wearable high fidelity Oura sensor ring (https://ouraring.com/blog/category/research-validation/) to acquire HRV, in addition to other physiological indicators, to track both pre-illness longitudinal baseline and an ongoing longitudinal Community assessment of those indicators associated with COVID-19 using algorithmic analysis and actionable feedback. While various aspects of this proposal have been used by investigators producing promising results at UC San Francisco (Smarr et al., 2020), UC San Diego/Scripps Research (Whitelaw et al., 2020), Stanford School of Medicine (Perez et al., 2019), Mt. Sinai's Icahn School of Medicine (Hirten et al., 2021), and others (Chung, 2020; Hasty et al., 2020), we propose a synthetic approach that incorporates the advantages of the most promising, actionable and practical elements to elucidate how HRV can act as a predictor of COVID19 infection. The use of longitudinal HRV data acquired by a personal device, transferred by smart phone application and analyzed by high throughput cloud-based machine learning algorithm represents an innovative, inexpensive, easily deployable, and scalable method for both individual use for health behavior maintenance and for communication and decision support with clinical and public health professionals in communities and larger jurisdictions. HRV research has produced extensive literature, with a recent PubMed search of the term “heart rate variability” producing more than 50,000 citations (Malik and Camm, 2004; Shaffer and Ginsberg, 2019), with rapidly evolving neuroscientific HRV studies (Holzman and Bridgett, 2017). Major theoretical contributions have been made by Porges' Polyvagal Theory (Porges, 2011); Grossman's biobehavioral studies of cardiac vagal tone (Grossman and Taylor, 2007); Owens, Critchley and associates studies of HRV as a remote digital biomarker (Owens et al., 2018); and Thayer's neurovisceral integration approach (Thayer and Lane, 2020), all show the important role of HRV as a physiological indicator of inflammatory and immune system activity. Briefly, HRV is the instantaneous variation in the inter-beat interval (IBI) of the electrocardiogram. HRVs relation to many disease states and human psychophysiological functions has been studied extensively. Perhaps counterintuitively, greater variability in IBI, measured as the time between adjacent R to R peaks in the ECG, is positively correlated to fewer and/or lesser negative health or well-being consequences in many diseases and conditions. These constant allostatic variations can be seen as analogous to the over 22,000 course corrections necessary for Apollo 8 to land on the moon (McEwen, 2017). Recent reviews have described the wide variety of applications of HRV in both medical and psychosocial settings (Drury et al., 2019). In particular, the Thayer group showed that HRV is related to inflammatory processes in humans (Williams et al., 2019; Jarczok et al., 2021) and identified an HRV related cholinergic anti-inflammatory pathway (Thayer and Fischer, 2009). Investigators have explored the use of HRV in medical conditions, including infectious and immune related disorders, in both human and animal studies showing various HRV parameters to be related to infection and immune system function (Fairchild, 2013; Herry et al., 2016; Pavlov and Tracey, 2019; Pavlov et al., 2020). Based on this body of basic and applied HRV research, we wish to urgently propose using HRV monitoring as an element of a larger framework of truly personalized health (Drury, 2019; Hood et al., 2019). HRV screening, analysis and feedback can be applied immediately to the present COVID-19 pandemic. A recent report by Jarczok et al. (2019) has presented proof of concept of HRV as a marker of health risks in human adults. We propose applying the Jarczok et al. method to IBI data obtained from personal devices such as the Oura ring, Apple Watch, Fitbit, and the Polar strap, among others, facilitating scalability, accessibility, economy and high fidelity data acquisition. The Apple Heart Study conducted by Stanford University's School of Medicine demonstrated the feasibility of using wearable technology, specifically the Apple Watch, to examine cardiovascular data for atrial fibrillation. They point out “this is just the beginning, as this study opens the door to further research into wearable technologies and how they...
Yongqiang Xu, Ping Yu, Jianmin Zheng, Chen Wang, Tian Hu, Qi Yang, Ziliang Xu, Fan Guo, Xing Tang, Fang Ren, et al.
Frontiers in Neuroscience, Volume 15; doi:10.3389/fnins.2021.660365

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Sleep deprivation (SD) has become very common in contemporary society, where people work around the clock. SD-induced cognitive deficits show large inter-individual differences and are trait-like with known neural correlates. However, few studies have used neuroimaging to predict vulnerability to SD. Here, resting state functional magnetic resonance imaging (fMRI) data and psychomotor vigilance task (PVT) data were collected from 60 healthy subjects after resting wakefulness and after one night of SD. The number of PVT lapses was then used to classify participants on the basis of whether they were vulnerable or resilient to SD. We explored the viability of graph-theory-based degree centrality to accurately classify vulnerability to SD. Compared with during resting wakefulness, widespread changes in degree centrality (DC) were found after SD, indicating significant reorganization of sleep homeostasis with respect to activity in resting state brain network architecture. Support vector machine (SVM) analysis using leave-one-out cross-validation achieved a correct classification rate of 84.75% [sensitivity 82.76%, specificity 86.67%, and area under the receiver operating characteristic curve (AUC) 0.94] for differentiating vulnerable subjects from resilient subjects. Brain areas that contributed most to the classification model were mainly located within the sensorimotor network, default mode network, and thalamus. Furthermore, we found a significantly negative correlation between changes in PVT lapses and DC in the thalamus after SD. These findings suggest that resting-state network measures combined with a machine learning algorithm could have broad potential applications in screening vulnerability to SD.
, Erica Voss
Frontiers in Neuroscience, Volume 15; doi:10.3389/fnins.2021.637221

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Innovations in LED lighting technology have led to tremendous adoption rates and vastly improved the metrics by which they are traditionally evaluated–including color quality, longevity, and energy efficiency to name a few. Additionally, scientific insight has broadened with respect to the biological impact of light, specifically our circadian rhythm. Indoor electric lighting, despite its many attributes, fails to specifically address the biological responses to light. Traditional electric lighting environments are biologically too dim during the day, too bright at night, and with many people spending much of their lives in these environments, it can lead to circadian dysfunction. The lighting industry’s biological solution has been to create bluer days and yellower nights, but the technology created to do so caters primarily to the cones. A better call to action is to provide biologically brighter days and biologically darker nights within the built environment. However, current lighting design practices have specified the comfort and utility of electric light. Brighter intensity during the day can often be uncomfortable or glary, and reduced light intensity at night may compromise visual comfort and safety, both of which will affect user compliance. No single lighting solution will effectively create biologically brighter days and biologically darker nights, but rather a variety of parameters need to be considered. This paper discusses the contributions of spectral power distribution, hue or color temperature, spatial distribution, as well as architectural geometry and surface reflectivity, to achieve biologically relevant lighting.
Florian Missey, Evgeniia Rusina, Emma Acerbo, Boris Botzanowski, Agnès Trébuchon, Fabrice Bartolomei, Viktor Jirsa, Romain Carron,
Frontiers in Neuroscience, Volume 15; doi:10.3389/fnins.2021.633988

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In patients with focal drug-resistant epilepsy, electrical stimulation from intracranial electrodes is frequently used for the localization of seizure onset zones and related pathological networks. The ability of electrically stimulated tissue to generate beta and gamma range oscillations, called rapid-discharges, is a frequent indication of an epileptogenic zone. However, a limit of intracranial stimulation is the fixed physical location and number of implanted electrodes, leaving numerous clinically and functionally relevant brain regions unexplored. Here, we demonstrate an alternative technique relying exclusively on non-penetrating surface electrodes, namely an orientation-tunable form of temporally interfering (TI) electric fields to target the CA3 of the mouse hippocampus which focally evokes seizure-like events (SLEs) having the characteristic frequencies of rapid-discharges, but without the necessity of the implanted electrodes. The orientation of the topical electrodes with respect to the orientation of the hippocampus is demonstrated to strongly control the threshold for evoking SLEs. Additionally, we demonstrate the use of Pulse-width-modulation of square waves as an alternative to sine waves for TI stimulation. An orientation-dependent analysis of classic implanted electrodes to evoke SLEs in the hippocampus is subsequently utilized to support the results of the minimally invasive temporally interfering fields. The principles of orientation-tunable TI stimulation seen here can be generally applicable in a wide range of other excitable tissues and brain regions, overcoming several limitations of fixed electrodes which penetrate tissue and overcoming several limitations of other non-invasive stimulation methods in epilepsy, such as transcranial magnetic stimulation (TMS).
Yi Yuan, Yasneli Lleo, Rebecca Daniel, Alexandra White,
Frontiers in Neuroscience, Volume 15; doi:10.3389/fnins.2021.678029

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Speech perception often takes place in noisy environments, where multiple auditory signals compete with one another. The addition of visual cues such as talkers’ faces or lip movements to an auditory signal can help improve the intelligibility of speech in those suboptimal listening environments. This is referred to as audiovisual benefits. The current study aimed to delineate the signal-to-noise ratio (SNR) conditions under which visual presentations of the acoustic amplitude envelopes have their most significant impact on speech perception. Seventeen adults with normal hearing were recruited. Participants were presented with spoken sentences in babble noise either in auditory-only or auditory-visual conditions with various SNRs at −7, −5, −3, −1, and 1 dB. The visual stimulus applied in this study was a sphere that varied in size syncing with the amplitude envelope of the target speech signals. Participants were asked to transcribe the sentences they heard. Results showed that a significant improvement in accuracy in the auditory-visual condition versus the audio-only condition was obtained at the SNRs of −3 and −1 dB, but no improvement was observed in other SNRs. These results showed that dynamic temporal visual information can benefit speech perception in noise, and the optimal facilitative effects of visual amplitude envelope can be observed under an intermediate SNR range.
Tongye Liu, Xinhe Li, Yiteng Cui, Pingping Meng, Guanghui Zeng, Yuyang Wang, Qiang Wang
Frontiers in Neuroscience, Volume 15; doi:10.3389/fnins.2021.661663

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Intracerebral hemorrhage (ICH) is a dangerous neurological disease. The mechanism of ferroptosis in ICH remains unclear. Using bioinformatics analysis, we aimed to identify the key molecules involved in ferroptosis and provide treatment targets for ICH to further explore the mechanism of ferroptosis in ICH. GSE24265 was downloaded from the Gene Expression Omnibus (GEO) dataset and intersected with ferroptosis genes. A total of 45 differentially expressed genes (DEGs) were selected, most of which were involved in the TNF signaling pathway and oxidative stress response. Key modules constructed by the protein–protein interaction (PPI) network analysis and screening of genes related to the TNF signaling pathway led to the confirmation of the following genes of interest: MAPK1, MAPK8, TNFAIP3, ATF4, and SLC2A1. Moreover, MAPK1 was one of the key genes related to TNF signaling and oxidative stress, and it may play an important role in ferroptosis after cerebral hemorrhage. The MAPK1-related molecules included hsa-miR-15b-5P, hsa-miR-93-5P, miR-20b-5p, SNHG16, XIST, AC084219.4, RP11-379K17.11, CTC-444N24.11, GS1-358P8.4, CTB-89H12.4, RP4-773N10.5, and FGD5-AS1. We also generated a hemorrhage rat model, which was used to conduct exercise intervention in ICH rats, and qRT-PCR was used to assess the expression levels of our genes of interest. The mRNA levels after cerebral hemorrhage showed that MAPK1, ATF4, SLC2A1, and TNFAIP3 were upregulated, whereas MAPK8 was downregulated. Treadmill training increased the expression of anti-inflammatory molecules TNFAIP3 and SLC2A1 and reduced the expression of MAPK1, ATF4, and MAPK8, indicating that treadmill training may be utilized as antioxidant therapy to decrease neuronal ferroptosis. The results of this study indicated that the MAPK1-related mRNA–miRNA–lncRNA interaction chain could be potentially employed as a biomarker of the inception and progression of ferroptosis after cerebral hemorrhage.
Kate Beecher, Ignatius Alvarez Cooper, Joshua Wang, Shaun B. Walters, Fatemeh Chehrehasa, ,
Frontiers in Neuroscience, Volume 15; doi:10.3389/fnins.2021.670430

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Sugar has become embedded in modern food and beverages. This has led to overconsumption of sugar in children, adolescents, and adults, with more than 60 countries consuming more than four times (>100 g/person/day) the WHO recommendations (25 g/person/day). Recent evidence suggests that obesity and impulsivity from poor dietary habits leads to further overconsumption of processed food and beverages. The long-term effects on cognitive processes and hyperactivity from sugar overconsumption, beginning at adolescence are not known. Using a well-validated mouse model of sugar consumption, we found that long-term sugar consumption, at a level that significantly augments weight gain, elicits an abnormal hyperlocomotor response to novelty and alters both episodic and spatial memory. Our results are similar to those reported in attention deficit and hyperactivity disorders. The deficits in hippocampal-dependent learning and memory were accompanied by altered hippocampal neurogenesis, with an overall decrease in the proliferation and differentiation of newborn neurons within the dentate gyrus. This suggests that long-term overconsumption of sugar, as that which occurs in the Western Diet might contribute to an increased risk of developing persistent hyperactivity and neurocognitive deficits in adulthood.
Kevin K. K. Yu, Gladys L. Y. Cheing, Charlton Cheung, , Alex Kwok-Kuen Cheung
Frontiers in Neuroscience, Volume 15; doi:10.3389/fnins.2021.638861

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Aims/hypothesis: Diabetes mellitus (DM) is associated with comorbid brain disorders. Neuroimaging studies in DM revealed neuronal degeneration in several cortical and subcortical brain regions. Previous studies indicate more pronounced brain alterations in type 2 diabetes mellitus (T2DM) than in type 1 diabetes mellitus (T1DM). However, a comparison of both types of DM in a single analysis has not been done so far. The aim of this meta-analysis was to conduct an unbiased objective investigation of neuroanatomical differences in DM by combining voxel-based morphometry (VBM) studies of T1DM and T2DM using dual disorder anatomical likelihood estimation (ALE) quantification. Methods: PubMed, Web of Science and Medline were systematically searched for publications until June 15, 2020. VBM studies comparing gray matter volume (GMV) differences between DM patients and controls at the whole-brain level were included. Study coordinates were entered into the ALE meta-analysis to investigate the extent to which T1DM, T2DM, or both conditions contribute to gray matter volume differences compared to controls. Results: Twenty studies (comprising of 1,175 patients matched with 1,013 controls) were included, with seven studies on GMV alterations in T1DM and 13 studies on GMV alterations in T2DM. ALE analysis revealed seven clusters of significantly lower GMV in T1DM and T2DM patients relative to controls across studies. Both DM subtypes showed GMV reductions in the left caudate, right superior temporal lobe, and left cuneus. Conversely, GMV reductions associated exclusively with T2DM (>99% contribution) were found in the left cingulate, right posterior lobe, right caudate and left occipital lobe. Meta-regression revealed no significant influence of study size, disease duration, and HbA1c values. Conclusions/interpretation: Our findings suggest a more pronounced gray matter atrophy in T2DM compared to T1DM. The increased risk of microvascular or macrovascular complications, as well as the disease-specific pathology of T2DM may contribute to observed GMV reductions. Systematic Review Registration: [PROSPERO], identifier [CRD42020142525].
Lu Liu, Xiao-Bai Xu, Zheng-Yang Qu, Luo-Peng Zhao, Claire-Shuiqing Zhang, Zhi-Juan Li, Tian-Li Lyu, Xue-Fei Wang, Xiang-Hong Jing,
Frontiers in Neuroscience, Volume 15; doi:10.3389/fnins.2021.668616

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Electroacupuncture (EA) is widely used in clinical practice to relieve migraine pain. 5-HT7 receptor (5-HT7R) has been reported to play an excitatory role in neuronal systems and regulate hyperalgesic pain and neurogenic inflammation. 5-HT7R could influence phosphorylation of protein kinase A (PKA)- or extracellular signal-regulated kinase1 / 2 (ERK1 / 2)-mediated signaling pathways, which mediate sensitization of nociceptive neurons via interacting with cyclic adenosine monophosphate (cAMP). In this study, we evaluated the role of 5-HT7R in the antihyperalgesic effects of EA and the underlying mechanism through regulation of PKA and ERK1 / 2 in trigeminal ganglion (TG) and trigeminal nucleus caudalis (TNC). Hyperalgesia was induced in rats with dural injection of inflammatory soup (IS) to cause meningeal neurogenic inflammatory pain. Electroacupuncture was applied for 15 min every other day before IS injection. Von Frey filaments, tail-flick, hot-plate, and cold-plated tests were used to evaluate the mechanical and thermal hyperalgesia. Neuronal hyperexcitability in TNC was studied by an electrophysiological technique. The 5-HT7R antagonist (SB269970) or 5-HT7R agonist (AS19) was administered intrathecally before each IS application at 2-day intervals during the 7-day injection protocol. The changes in 5-HT7R and 5-HT7R-associated signaling pathway were examined by real-time polymerase chain reaction (RT-PCR), Western blot, immunofluorescence, and enzyme-linked immunosorbent assay (ELISA) analyses. When compared with IS group, mechanical and thermal pain thresholds of the IS + EA group were significantly increased. Furthermore, EA prevented the enhancement of both spontaneous activity and evoked responses of second-order trigeminovascular neurons in TNC. Remarkable decreases in 5-HT7R mRNA expression and protein levels were detected in the IS + EA group. More importantly, 5-HT7R agonist AS19 impaired the antihyperalgesic effects of EA on p-PKA and p-ERK1 / 2. Injecting 5-HT7R antagonist SB-269970 into the intrathecal space of IS rats mimicked the effects of EA antihyperalgesia and inhibited p-PKA and p-ERK1 / 2. Our findings indicate that 5-HT7R mediates the antihyperalgesic effects of EA on IS-induced migraine pain by regulating PKA and ERK1 / 2 in TG and TNC.
Christopher Bengel, Felix Cüppers, Melika Payvand, Regina Dittmann, Rainer Waser, Susanne Hoffmann-Eifert,
Frontiers in Neuroscience, Volume 15; doi:10.3389/fnins.2021.661856

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With the arrival of the Internet of Things (IoT) and the challenges arising from Big Data, neuromorphic chip concepts are seen as key solutions for coping with the massive amount of unstructured data streams by moving the computation closer to the sensors, the so-called “edge computing.” Augmenting these chips with emerging memory technologies enables these edge devices with non-volatile and adaptive properties which are desirable for low power and online learning operations. However, an energy- and area-efficient realization of these systems requires disruptive hardware changes. Memristor-based solutions for these concepts are in the focus of research and industry due to their low-power and high-density online learning potential. Specifically, the filamentary-type valence change mechanism (VCM memories) have shown to be a promising candidate In consequence, physical models capturing a broad spectrum of experimentally observed features such as the pronounced cycle-to-cycle (c2c) and device-to-device (d2d) variability are required for accurate evaluation of the proposed concepts. In this study, we present an in-depth experimental analysis of d2d and c2c variability of filamentary-type bipolar switching HfO2/TiOx nano-sized crossbar devices and match the experimentally observed variabilities to our physically motivated JART VCM compact model. Based on this approach, we evaluate the concept of parallel operation of devices as a synapse both experimentally and theoretically. These parallel synapses form a synaptic array which is at the core of neuromorphic chips. We exploit the c2c variability of these devices for stochastic online learning which has shown to increase the effective bit precision of the devices. Finally, we demonstrate that stochastic switching features for a pattern classification task that can be employed in an online learning neural network.
Johannes Rodrigues, Martin Weiß, Johannes Hewig, John J. B. Allen
Frontiers in Neuroscience, Volume 15; doi:10.3389/fnins.2021.660449

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Background Since the replication crisis, standardization has become even more important in psychological science and neuroscience. As a result, many methods are being reconsidered, and researchers’ degrees of freedom in these methods are being discussed as a potential source of inconsistencies across studies. New Method With the aim of addressing these subjectivity issues, we have been working on a tutorial-like EEG (pre-)processing pipeline to achieve an automated method based on the semi-automated analysis proposed by Delorme and Makeig. Results Two scripts are presented and explained step-by-step to perform basic, informed ERP and frequency-domain analyses, including data export to statistical programs and visual representations of the data. The open-source software EEGlab in MATLAB is used as the data handling platform, but scripts based on code provided by Mike Cohen (2014) are also included. Comparison with existing methods This accompanying tutorial-like article explains and shows how the processing of our automated pipeline affects the data and addresses, especially beginners in EEG-analysis, as other (pre)-processing chains are mostly targeting rather informed users in specialized areas or only parts of a complete procedure. In this context, we compared our pipeline with a selection of existing approaches. Conclusion The need for standardization and replication is evident, yet it is equally important to control the plausibility of the suggested solution by data exploration. Here, we provide the community with a tool to enhance the understanding and capability of EEG-analysis. We aim to contribute to comprehensive and reliable analyses for neuro-scientific research.
Junhan Wei, Deying Kong, Xi Yu, Lili Wei, Yue Xiong, Adeline Yang, Björn Drobe, Jinhua Bao, Jiawei Zhou, , et al.
Frontiers in Neuroscience, Volume 15; doi:10.3389/fnins.2021.683153

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Purpose The current study was to investigate whether myopia affected peripheral motion detection and whether the potential effect interacted with spatial frequency, motion speed, or eccentricity. Methods Seventeen young adults aged 22–26 years participated in the study. They were six low to medium myopes [spherical equivalent refractions −1.0 to −5.0 D (diopter)], five high myopes (<-5.5 D) and six emmetropes (+0.5 to −0.5 D). All myopes were corrected by self-prepared, habitual soft contact lenses. A four-alternative forced-choice task in which the subject was to determine the location of the phase-shifting Gabor from the four quadrants (superior, inferior, nasal, and temporal) of the visual field, was employed. The experiment was blocked by eccentricity (20° and 27°), spatial frequency (0.6, 1.2, 2.4, and 4.0 cycles per degree (c/d) for 20° eccentricity, and 0.6, 1.2, 2.0, and 3.2 c/d for 27° eccentricity), as well as the motion speed [2 and 6 degree per second (d/s)]. Results Mixed-model analysis of variances showed no significant difference in the thresholds of peripheral motion detection between three refractive groups at either 20° (F[2,14] = 0.145, p = 0.866) or 27° (F[2,14] = 0.475, p = 0.632). At 20°, lower motion detection thresholds were associated with higher myopia (p < 0.05) mostly for low spatial frequency and high-speed targets in the nasal and superior quadrants, and for high spatial frequency and high-speed targets in the temporal quadrant in myopic viewers. Whereas at 27°, no significant correlation was found between the spherical equivalent and the peripheral motion detection threshold under all conditions (all p > 0.1). Spatial frequency, speed, and quadrant of the visual field all showed significant effect on the peripheral motion detection threshold. Conclusion There was no significant difference between the three refractive groups in peripheral motion detection. However, lower motion detection thresholds were associated with higher myopia, mostly for low spatial frequency targets, at 20° in myopic viewers.
Xia Hu, Yi Qin, Xiaoxiao Ying, Junli Yuan, Rong Cui, Xiaowei Ruan, Xianghang He, Zhong-Lin Lu, ,
Frontiers in Neuroscience, Volume 15; doi:10.3389/fnins.2021.673491

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Purpose Amblyopia affects not only spatial vision but also temporal vision. In this study, we aim to investigate temporal processing deficits in amblyopia. Methods Twenty amblyopic patients (age: 27.0 ± 5.53 years, 15 males), and 25 normal observers (age: 25.6 ± 4.03 years, 15 males) were recruited in this study. Contrast thresholds in an orientation discrimination task in five target-mask stimulus onset asynchronies (SOA) conditions (16.7 ms, 33.4 ms, 50.0 ms, 83.4 ms, and ∞/no noise) were measured. An elaborated perceptual template model (ePTM) was fit to the behavioral data to derive the temporal profile of visual processing for each participant. Results There were significant threshold differences between the amblyopic and normal eyes [F(1,43) = 10.6, p = 0.002] and a significant group × SOA interaction [F(2.75,118) = 4.98, p = 0.004], suggesting different temporal processing between the two groups. The ePTM fitted the data well (χ 2 test, all ps > 0.50). Compared to the normal eye, the amblyopic eye had a lower template gain (p = 0.046), and a temporal window with lower peak and broader width (all ps < 0.05). No significant correlation was found between the observed temporal deficits and visual acuity in amblyopia (ps > 0.50). Similar results were found in the anisometropic amblyopia subgroup. No significant difference was found between the fellow eyes of the monocular amblyopia and the normal eyes. Conclusion Amblyopia is less efficient in processing dynamic visual stimuli. The temporal deficits in amblyopia, represented by a flattened temporal window, are likely independent of spatial vision deficits.
, Jun Li, Yanxiang Niu, Chang Wang, Junqiang Zhao, Qingli Yuan, Qiongqiong Ren, Yongtao Xu, Yi Yu
Frontiers in Neuroscience, Volume 15; doi:10.3389/fnins.2021.651439

Abstract:
At present, lots of studies have tried to apply machine learning to different electroencephalography (EEG) measures for diagnosing schizophrenia (SZ) patients. However, most EEG measures previously used are either a univariate measure or a single type of brain connectivity, which may not fully capture the abnormal brain changes of SZ patients. In this paper, event-related potentials were collected from 45 SZ patients and 30 healthy controls (HCs) during a learning task, and then a combination of partial directed coherence (PDC) effective and phase lag index (PLI) functional connectivity were used as features to train a support vector machine classifier with leave-one-out cross-validation for classification of SZ from HCs. Our results indicated that an excellent classification performance (accuracy = 95.16%, specificity = 94.44%, and sensitivity = 96.15%) was obtained when the combination of functional and effective connectivity features was used, and the corresponding optimal feature number was 15, which included 12 PDC and three PLI connectivity features. The selected effective connectivity features were mainly located between the frontal/temporal/central and visual/parietal lobes, and the selected functional connectivity features were mainly located between the frontal/temporal and visual cortexes of the right hemisphere. In addition, most of the selected effective connectivity abnormally enhanced in SZ patients compared with HCs, whereas all the selected functional connectivity features decreased in SZ patients. The above results showed that our proposed method has great potential to become a tool for the auxiliary diagnosis of SZ.
Yun Yu, Xi Wu, Jiu Chen, Gong Cheng, Xin Zhang, Cheng Wan, Jie Hu, Shumei Miao, Yuechuchu Yin, Zhongmin Wang, et al.
Frontiers in Neuroscience, Volume 15; doi:10.3389/fnins.2021.634926

Abstract:
Purpose To extract texture features from magnetic resonance imaging (MRI) scans of patients with brain tumors and use them to train a classification model for supporting an early diagnosis. Methods Two groups of regions (control and tumor) were selected from MRI scans of 40 patients with meningioma or glioma. These regions were analyzed to obtain texture features. Statistical analysis was conducted using SPSS (version 20.0), including the Shapiro–Wilk test and Wilcoxon signed-rank test, which were used to test significant differences in each feature between the tumor and healthy regions. T-distributed stochastic neighbor embedding (t-SNE) was used to visualize the data distribution so as to avoid tumor selection bias. The Gini impurity index in random forests (RFs) was used to select the top five out of all features. Based on the five features, three classification models were built respectively with three machine learning classifiers: RF, support vector machine (SVM), and back propagation (BP) neural network. Results Sixteen of the 25 features were significantly different between the tumor and healthy areas. Through the Gini impurity index in RFs, standard deviation, first-order moment, variance, third-order absolute moment, and third-order central moment were selected to build the classification model. The classification model trained using the SVM classifier achieved the best performance, with sensitivity, specificity, and area under the curve of 94.04%, 92.3%, and 0.932, respectively. Conclusion Texture analysis with an SVM classifier can help differentiate between brain tumor and healthy areas with high speed and accuracy, which would facilitate its clinical application.
Magdalena Dubik, Joanna Marczynska, Marlene T. Mørch, Gill Webster, Kirstine Nolling Jensen, Agnieszka Wlodarczyk, Reza Khorooshi,
Frontiers in Neuroscience, Volume 15; doi:10.3389/fnins.2021.682451

Abstract:
The pathological hallmark of multiple sclerosis (MS) is the formation of multifocal demyelinating lesions in the central nervous system (CNS). Stimulation of innate receptors has been shown to suppress experimental autoimmune encephalomyelitis (EAE), an MS-like disease in mice. Specifically, targeting Toll-like receptor 9 (TLR9) and NOD-like receptor 2 (NOD2) significantly reduced disease severity. In the present work we have developed a novel focal EAE model to further study the effect of innate signaling on demyelinating pathology. Focal lesions were induced by stereotactic needle insertion into the corpus callosum (CC) of mice previously immunized for EAE. This resulted in focal pathology characterized by infiltration and demyelination in the CC. We find that intrathecal delivery of MIS416, a TLR9 and NOD2 bispecific innate ligand, into the cerebrospinal fluid reduced focal lesions in the CC. This was associated with upregulation of type I and II interferons, interleukin-10, arginase-1, CCL-2 and CXCL-10. Analysis of draining cervical lymph nodes showed upregulation of type II interferons and interleukin 10. Moreover, intrathecal MIS416 altered the composition of early CNS infiltrates, increasing proportions of myeloid and NK cells and reducing T cells at the lesion site. This study contributes to an increased understanding of how innate immune responses can play a protective role, which in turn may lead to additional therapeutic strategies for the prevention and treatment of demyelinating pathologies.
Chaahat S. B. Singh, Brett A. Eyford, Thomas Abraham, Lonna Munro, Kyung Bok Choi, Mark Okon, Timothy Z. Vitalis, Reinhard Gabathuler, Chieh-Ju Lu, Cheryl G. Pfeifer, et al.
Frontiers in Neuroscience, Volume 15; doi:10.3389/fnins.2021.596976

Abstract:
The blood-brain barrier (BBB) hinders the distribution of therapeutics intended for treatment of diseases of the brain. Our previous studies demonstrated that that a soluble form of melanotransferrin (MTf; Uniprot P08582; also known as p97, MFI2, and CD228), a mammalian iron-transport protein, is an effective carrier for delivery of drug conjugates across the BBB into the brain and was the first BBB targeting delivery system to demonstrate therapeutic efficacy within the brain. Here, we performed a screen to identify peptides from MTf capable of traversing the BBB. We identified a highly conserved 12-amino acid peptide, termed MTfp, that retains the ability to cross the intact BBB intact, distributes throughout the parenchyma, and enter endosomes and lysosomes within neurons, astrocytes and microglia in the brain. This peptide may provide a platform for the transport of therapeutics to the CNS, and thereby offers new avenues for potential treatments of neuropathologies that are currently refractory to existing therapies.
Haining Li, Qiuli Zhang, Qianqian Duan, Jiaoting Jin, Fangfang Hu, Jingxia Dang, Ming Zhang
Frontiers in Neuroscience, Volume 15; doi:10.3389/fnins.2021.675444

Abstract:
Introduction The brainstem is an important component in the pathology of amyotrophic lateral sclerosis (ALS). Although neuroimaging studies have shown multiple structural changes in ALS patients, few studies have investigated structural alterations in the brainstem. Herein, we compared the brainstem structure between patients with ALS and healthy controls. Methods A total of 33 patients with ALS and 33 healthy controls were recruited in this study. T1-weighted and diffusion tensor imaging (DTI) were acquired on a 3 Tesla magnetic resonance imaging (3T MRI) scanner. Volumetric and vertex-wised approaches were implemented to assess the differences in the brainstem’s morphological features between the two groups. An atlas-based region of interest (ROI) analysis was performed to compare the white matter integrity of the brainstem between the two groups. Additionally, a correlation analysis was used to evaluate the relationship between ALS clinical characteristics and structural features. Results Volumetric analyses showed no significant difference in the subregion volume of the brainstem between ALS patients and healthy controls. In the shape analyses, ALS patients had a local abnormal surface contraction in the ventral medulla oblongata and ventral pons. Compared with healthy controls, ALS patients showed significantly lower fractional anisotropy (FA) in the left corticospinal tract (CST) and bilateral frontopontine tracts (FPT) at the brainstem level, and higher radial diffusivity (RD) in bilateral CST and left FPT at the brainstem level by ROI analysis in DTI. Correlation analysis showed that disease severity was positively associated with FA in left CST and left FPT. Conclusion These findings suggest that the brainstem in ALS suffers atrophy, and degenerative processes in the brainstem may reflect disease severity in ALS. These findings may be helpful for further understanding of potential neural mechanisms in ALS.
, Giancarlo Carli
Frontiers in Neuroscience, Volume 15; doi:10.3389/fnins.2021.683045

Abstract:
Pharmacological, physical and cognitive treatments reduce pain by addressing all pain dimensions. Nonetheless, drugs may be ineffective, and physical activity is not always viable. In contrast, cognitive therapies have usually good outcomes, a wide range of applicability and no side effects. Their efficacy, however, is influenced by cognitive and psychophysiological traits. In this Opinion article hypnotizability is used as a model to support the view that specific psychophysiological traits and cognitive strategies can not only reduce pain, but also modulate the pain-related autonomic and immune activity, induce cortical plasticity relevant to pain control, and assist in the choice of the most appropriate treatment. Hypnotizability, or hypnotic susceptibility, is a multidimensional trait stable through life (Piccione et al., 1989) and measured by validated scales (Elkins et al., 2015) classifying highly (highs), medium (mediums), and low hypnotizable subjects (lows). It is associated with brain morpho-functional peculiarities (Landry et al., 2017; Picerni et al., 2019) and displays correlates in the sensorimotor (Ibáñez-Marcelo et al., 2019; Santarcangelo and Scattina, 2019), cardiovascular (Jambrik et al., 2004a,b, 2005; Santarcangelo et al., 2012) and cognitive-emotional domain (Diolaiuti et al., 2019). Both highs and lows represent about 15% of the population which consists mainly of mediums (70%). In healthy subjects the ability to control pain through suggestions for analgesia is linearly correlated with hypnotizability scores (Fidanza et al., 2017). Hypnotic treatments, however, are particularly important for patients with neuropathic and musculo-skeletal pain (Castel et al., 2007; Carli et al., 2008; Jensen et al., 2009a,b; Jensen and Patterson, 2014), which are seldom responsive to pharmacological treatments. They have been found more effective than any other psychological intervention (Jensen et al., 2020), although high hypnotizability predicts better outcomes also in patients, owing to the highs' greater high proneness to modify their bodily condition according to suggestions, and, thus, to relax (De Benedittis et al., 1994), to their peculiar imagery abilities (Ibáñez-Marcelo et al., 2019), and to their attitude to be deeply absorbed in their own mental images (Vanhaudenhuyse et al., 2019). The suggestions for analgesia are requests to imagine that the experienced pain is out of the body or limited to a small part of it, or that a glove prevents one to perceive any nociceptive stimulation. They are effective on acute/procedural, post-surgery and chronic pain (Jensen and Patterson, 2014; Facco, 2016) and, as most suggestions (Green and Lynn, 2011; Santarcangelo, 2014), can be efficaciously administered in the ordinary state of consciousness, thus not necessarily following the induction of the hypnotic state (Derbyshire et al., 2009; Paoletti et al., 2010; Santarcangelo et al., 2012). In highs, suggestions-induced analgesia, which can be focused on the sensory and/or emotional dimension of pain, is not accompanied by release of endogenous opiates, but is sustained by the modulation of the activity and connectivity of the pain matrix (Faymonville et al., 2006; Casiglia et al., 2020). Interestingly, the suggestions for analgesia have been found effective also in healthy mediums undergoing nociceptive stimulation (Fidanza et al., 2017) and in chronic pain patients independently from hypnotizability (Elkins et al., 2007; Jensen, 2011; Jensen and Patterson, 2014; Mazzola et al., 2017; Facco et al., 2018; Sandvik et al., 2020). This can be accounted for by expectation of/motivation to analgesia (Milling et al., 2005; Krystek and Kumar, 2016; Montgomery et al., 2018; Perri et al., 2020) leading to placebo responses (Benedetti, 2013) which can reduce pain and pain-related psychological symptoms in the general population (Liossi et al., 2006; Brugnoli, 2016; Wortzel and Spiegel, 2017; Rousseaux et al., 2020). Thus, suggestions may induce non opioid analgesia in highs, opioid placebo responses in lows and, probably, mixed reactions in mediums. It is particularly interesting, in this respect, that, during hypnotic sessions, oxytocin – the hormone promoting social relationships and acquiescent behavior - is released in the hypnotist and the client and that, in the latter, the lower the hypnotizability score the larger the OXT release. A further contribution to the hypnotist-client relation could be the level of intimacy which has been associated with the polymorphism of the serotonin transporter 5-HTTLPR gene. Its variant associated with greater efficiency is not significantly associated with hypnotizability but may enhance the experience of “rapport” independently from it (Katonai et al., 2017). In brief, suggested analgesia occurs in the general population, although through different mechanisms (Santarcangelo and Consoli, 2018). In addition, in contrast to “constructive imagery” (inducing sensory experiences in the absence of actual stimulations), obstructive suggestions such as analgesia and anesthesia aimed at reducing the perception of actual sensory stimulations can be experienced also by lows if they report mental images as vivid as highs do (Santarcangelo et al., 2010). Thus, in lows, imagery and placebo responses could co-operate in the response to suggestions for analgesia. In the absence of explicit suggestions for analgesia, hypnotizability related differences in pain thresholds (Hilgard, 1967; Agargün et al., 1998; Santarcangelo et al., 2013; Kramer et al., 2014) and perceived pain intensity (Santarcangelo et al., 2010) have been seldom reported. Several studies, however, describe hypnotizability-related differences in genetic polymorphisms and brain neurotransmitters content which may be relevant to pain control in the presence of suggestions and/or to the choice of pain treatments. In fact: a. highs display...
Yi Li, Junli Zhao, ZhiHan Lv, Zhenkuan Pan
Frontiers in Neuroscience, Volume 15; doi:10.3389/fnins.2021.638976

Abstract:
This article proposes a multimode medical image fusion with CNN and supervised learning, in order to solve the problem of practical medical diagnosis. It can implement different types of multimodal medical image fusion problems in batch processing mode and can effectively overcome the problem that traditional fusion problems that can only be solved by single and single image fusion. To a certain extent, it greatly improves the fusion effect, image detail clarity, and time efficiency in a new method. The experimental results indicate that the proposed method exhibits state-of-the-art fusion performance in terms of visual quality and a variety of quantitative evaluation criteria. Its medical diagnostic background is wide.
, Morteza Nabavi, Benoit Gosselin, Mohamad Sawan
Frontiers in Neuroscience, Volume 15; doi:10.3389/fnins.2021.667846

Abstract:
Scaling down technology demotes the parameters of AC-coupled neural amplifiers, such as increasing the low-cutoff frequency due to the short-channel effects. To improve the low-cutoff frequency, one solution is to increase the feedback capacitors' value. This solution is not desirable, as the input capacitors have to be increased to maintain the same gain, which increases the area and decreases the input impedance of the neural amplifier. We analytically analyze the small-signal behavior of the neural amplifier and prove that the main reason for the increase of the low-cutoff frequency in advanced CMOS technologies is the reduction of the input resistance of the operational transconductance amplifier (OTA). We also show that the reduction of the input resistance of the OTA is due to the increase in the gate oxide leakage in the input transistors. In this paper, we explore this fact and propose two solutions to reduce the low-cutoff frequency without increasing the value of the feedback capacitor. The first solution is performed by only simulation and is called cross-coupled positive feedback that uses pseudoresistors to provide a negative resistance to increase the input resistance of the OTA. As an advantage, only standard CMOS transistors are used in this method. Simulation results show that a low-cutoff frequency of 1.5 Hz is achieved while the midband gain is 30.4 dB at 1 V. In addition, the power consumption is 0.6 μW. In the second method, we utilize thick-oxide MOS transistors in the input differential pair of the OTA. We designed and fabricated the second method in the 65 nm TSMC CMOS process. Measured results are obtained by in vitro recordings on slices of mouse brainstem. The measurement results show that the bandwidth is between 2 Hz and 5.6 kHz. The neural amplifier has 34.3 dB voltage gain in midband and consumes 3.63 μW at 1 V power supply. The measurement results show an input-referred noise of 6.1 μV rms and occupy 0.04 mm 2 silicon area.
Sergio Frumento, Danilo Menicucci, Paul Kenneth Hitchcott, Andrea Zaccaro,
Frontiers in Neuroscience, Volume 15; doi:10.3389/fnins.2021.654170

Abstract:
We systematically review 26 papers investigating subjective, behavioral, and psychophysiological correlates of subliminal exposure to phobic stimuli in phobic patients. Stimulations were found to elicit: (1) cardiac defense responses, (2) specific brain activations of both subcortical (e.g., amygdala) and cortical structures, (3) skin conductance reactions, only when stimuli lasted >20 ms and were administered with intertrial interval >20 s. While not inducing the distress caused by current (supraliminal) exposure therapies, exposure to subliminal phobic stimuli still results in successful extinction of both psychophysiological and behavioral correlates: however, it hardly improves subjective fear. We integrate those results with recent bifactorial models of emotional regulation, proposing a new form of exposure therapy whose effectiveness and acceptability should be maximized by a preliminary subliminal stimulation. Systematic Review Registration: identifier [CRD42021129234].
Qianqian Du, , Guo Cheng, Yinyin Zhao,
Frontiers in Neuroscience, Volume 15; doi:10.3389/fnins.2021.673684

Abstract:
Golgi defects including Golgi fragmentation are pathological features of Alzheimer’s disease (AD). As a pathogenic factor in AD, amyloid precursor protein (APP) induces Golgi fragmentation in the soma. However, how APP regulates Golgi outposts (GOs) in dendrites remains unclear. Given that APP resides in and affects the movements of GOs, and in particular, reverses the distribution of multi-compartment GOs (mcGOs), we investigated the regulatory mechanism of mcGO movements in the Drosophila larvae. Knockdown experiments showed that the bidirectional mcGO movements were cooperatively controlled by the dynein heavy chain (Dhc) and kinesin heavy chain subunits. Notably, only Dhc mediated APP’s regulation of mcGO movements. Furthermore, by loss-of-function screening, the adaptor protein Sunday driver (Syd) was identified to mediate the APP-induced alteration of the direction of mcGO movements and dendritic defects. Collectively, by elucidating a model of bidirectional mcGO movements, we revealed the mechanism by which APP regulates the direction of mcGO movements. Our study therefore provides new insights into AD pathogenesis.
Hong-Jun Zou, Shi-Wu Guo, Lin Zhu, Xu Xu, Jin-Bo Liu
Frontiers in Neuroscience, Volume 15; doi:10.3389/fnins.2021.628917

Abstract:
Traumatic spinal cord injury (TSCI) leads to pathological changes such as inflammation, edema, and neuronal apoptosis. Methylprednisolone (MP) is a glucocorticoid that has a variety of beneficial effects, including decreasing inflammation and ischemic reaction, as well as inhibiting lipid peroxidation. However, the efficacy and mechanism of MP in TSCI therapy is yet to be deciphered. In the present study, MP significantly attenuated the apoptotic effects of H2O2 in neuronal cells. Western blot analysis demonstrated that the levels of apoptotic related proteins, Bax and cleaved caspase-3, were reduced while levels of anti-apoptotic Bcl-2 were increased. In vivo TUNEL assays further demonstrated that MP effectively protected neuronal cells from apoptosis after TSCI, and was consistent with in vitro studies. Furthermore, we demonstrated that MP could decrease expression levels of IBA1, Il-1α, TNFα, and C3 and suppress A1 neurotoxic reactive astrocyte activation in TSCI mouse models. Neurological function was evaluated using the Basso Mouse Scale (BMS) and Footprint Test. Results demonstrated that the neurological function of MP-treated injured mice was significantly increased. In conclusion, our study demonstrated that MP could attenuate astrocyte cell death, decrease microglia activation, suppress A1 astrocytes activation, and promote functional recovery after acute TSCI in mouse models.
Xuanhao Wang, Yan Luo, Yuwen Chen, Chaoyi Chen, Lu Yin, Tengfei Yu, ,
Frontiers in Neuroscience, Volume 15; doi:10.3389/fnins.2021.673740

Abstract:
Ultrasound and photoacoustic imaging are emerging as powerful tools to study brain structures and functions. The skull introduces significant distortion and attenuation of the ultrasound signals deteriorating image quality. For biological studies employing rodents, craniotomy is often times performed to enhance image qualities. However, craniotomy is unsuitable for longitudinal studies, where a long-term cranial window is needed to prevent repeated surgeries. Here, we propose a mouse model to eliminate sound blockage by the top portion of the skull, while minimum physiological perturbation to the imaged object is incurred. With the new mouse model, no craniotomy is needed before each imaging experiment. The effectiveness of our method was confirmed by three imaging systems: photoacoustic computed tomography, ultrasound imaging, and photoacoustic mesoscopy. Functional photoacoustic imaging of the mouse brain hemodynamics was also conducted. We expect new applications to be enabled by the new mouse model for photoacoustic and ultrasound imaging.
, Carolina Frankl-Vilches, Antje Bakker, Manfred Gahr
Frontiers in Neuroscience, Volume 15; doi:10.3389/fnins.2021.680530

Abstract:
Singing occurs in songbirds of both sexes, but some species show typical degrees of sex-specific performance. We studied the transcriptional sex differences in the HVC, a brain nucleus critical for song pattern generation, of the forest weaver (Ploceus bicolor), the blue-capped cordon-bleu (Uraeginthus cyanocephalus), and the canary (Serinus canaria), which are species that show low, medium, and high levels of sex-specific singing, respectively. We observed persistent sex differences in gene expression levels regardless of the species-specific sexual singing phenotypes. We further studied the HVC transcriptomes of defined phenotypes of canary, known for its testosterone-sensitive seasonal singing. By studying both sexes of canaries during both breeding and non-breeding seasons, non-breeding canaries treated with testosterone, and spontaneously singing females, we found that the circulating androgen levels and sex were the predominant variables associated with the variations in the HVC transcriptomes. The comparison of natural singing with testosterone-induced singing in canaries of the same sex revealed considerable differences in the HVC transcriptomes. Strong transcriptional changes in the HVC were detected during the transition from non-singing to singing in canaries of both sexes. Although the sex-specific genes of singing females shared little resemblance with those of males, our analysis showed potential functional convergences. Thus, male and female songbirds achieve comparable singing behaviours with sex-specific transcriptomes.
Qingguo Ren, Yihua Wang, Shanshan Leng, Xiaomin Nan, Bin Zhang, Xinyan Shuai, Jianyuan Zhang, Xiaona Xia, Ye Li, YaQiong Ge, et al.
Frontiers in Neuroscience, Volume 15; doi:10.3389/fnins.2021.646617

Abstract:
Background It is reported that radiomic features extracted from quantitative susceptibility mapping (QSM) had promising clinical value for the diagnosis of Parkinson’s disease (PD). We aimed to explore the usefulness of radiomics features based on magnitude images to distinguish PD from non-PD controls. Methods We retrospectively recruited PD patients and controls who underwent brain 3.0T MR including susceptibility-weighted imaging (SWI). A total of 396 radiomics features were extracted from the SN of 95 PD patients and 95 non-PD controls based on SWI. Intra-/inter-observer correlation coefficients (ICCs) were applied to measure the observer agreement for the radiomic feature extraction. Then the patients were randomly grouped into training and validation sets in a ratio of 7:3. In the training set, the maximum correlation minimum redundancy algorithm (mRMR) and the least absolute shrinkage and selection operator (LASSO) were conducted to filter and choose the optimized subset of features, and a radiomics signature was constructed. Moreover, radiomics signatures were constructed by different machine learning models. Area under the ROC curves (AUCs) were applied to evaluate the predictive performance of the models. Then correlation analysis was performed to evaluate the correlation between the optimized features and clinical factors. Results The intro-observer CC ranged from 0.82 to 1.0, and the inter-observer CC ranged from 0.77 to 0.99. The LASSO logistic regression model showed good prediction efficacy in the training set [AUC = 0.82, 95% confidence interval (CI, 0.74–0.88)] and the validation set [AUC = 0.81, 95% CI (0.68–0.91)]. One radiomic feature showed a moderate negative correlation with Hoehn-Yahr stage (r = −0.49, P = 0.012). Conclusion Radiomic predictive features based on SWI magnitude images could reflect the Hoehn-Yahr stage of PD to some extent.
Jaeouk Cho, Geunchang Seong, Yonghee Chang,
Frontiers in Neuroscience, Volume 15; doi:10.3389/fnins.2021.667447

Abstract:
Miniaturized implantable devices play a crucial role in neural interfaces by monitoring and modulating neural activities on the peripheral and central nervous systems. Research efforts toward a compact wireless closed-loop system stimulating the nerve automatically according to the user's condition have been maintained. These systems have several advantages over open-loop stimulation systems such as reduction in both power consumption and side effects of continuous stimulation. Furthermore, a compact and wireless device consuming low energy alleviates foreign body reactions and risk of frequent surgical operations. Unfortunately, however, the miniaturized closed-loop neural interface system induces several hardware design challenges such as neural activity recording with severe stimulation artifact, real-time stimulation artifact removal, and energy-efficient wireless power delivery. Here, we will review recent approaches toward the miniaturized closed-loop neural interface system with integrated circuit (IC) techniques.
, Thomas Eibl, Alexander Hammer, Markus Holtmannspötter, Nicolai Savaskan, Hans-Herbert Steiner
Frontiers in Neuroscience, Volume 15; doi:10.3389/fnins.2021.666679

Abstract:
Little progress has been made in the long-term management of malignant brain tumors, leaving patients with glioblastoma, unfortunately, with a fatal prognosis. Glioblastoma remains the most aggressive primary brain cancer in adults. Similar to other cancers, glioblastoma undergoes a cellular metabolic reprogramming to form an oxidative tumor microenvironment, thereby fostering proliferation, angiogenesis and tumor cell survival. Latest investigations revealed that micronutrients, such as selenium, may have positive effects in glioblastoma treatment, providing promising chances regarding the current limitations in surgical treatment and radiochemotherapy outcomes. Selenium is an essential micronutrient with anti-oxidative and anti-cancer properties. There is additional evidence of Se deficiency in patients suffering from brain malignancies, which increases its importance as a therapeutic option for glioblastoma therapy. It is well known that selenium, through selenoproteins, modulates metabolic pathways and regulates redox homeostasis. Therefore, selenium impacts on the interaction in the tumor microenvironment between tumor cells, tumor-associated cells and immune cells. In this review we take a closer look at the current knowledge about the potential of selenium on glioblastoma, by focusing on brain edema, glioma-related angiogenesis, and cells in tumor microenvironment such as glioma-associated microglia/macrophages.
, Jannes Knychalla, Sonja Annerer-Walcher, Mathias Benedek, Felix Putze
Frontiers in Neuroscience, Volume 15; doi:10.3389/fnins.2021.664490

Abstract:
It has been shown that conclusions about the human mental state can be drawn from eye gaze behavior by several previous studies. For this reason, eye tracking recordings are suitable as input data for attentional state classifiers. In current state-of-the-art studies, the extracted eye tracking feature set usually consists of descriptive statistics about specific eye movement characteristics (i.e., fixations, saccades, blinks, vergence, and pupil dilation). We suggest an Imaging Time Series approach for eye tracking data followed by classification using a convolutional neural net to improve the classification accuracy. We compared multiple algorithms that used the one-dimensional statistical summary feature set as input with two different implementations of the newly suggested method for three different data sets that target different aspects of attention. The results show that our two-dimensional image features with the convolutional neural net outperform the classical classifiers for most analyses, especially regarding generalization over participants and tasks. We conclude that current attentional state classifiers that are based on eye tracking can be optimized by adjusting the feature set while requiring less feature engineering and our future work will focus on a more detailed and suited investigation of this approach for other scenarios and data sets.
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