Causal interactions in brain networks predict pain levels in trigeminal neuralgia

Trigeminal neuralgia (TN) is a highly debilitating facial pain condition. Magnetic resonance imaging (MRI) is the main method for generating insights into the central mechanisms of TN pain in humans. Studies have found both structural and functional abnormalities in various brain structures in TN patients as compared with healthy controls. Whereas studies have also examined aberrations in brain networks in TN, no studies have to date investigated causal interactions in these brain networks and related these causal interactions to the levels of TN pain. We recorded fMRI data from 39 TN patients who either rested comfortably in the scanner during the resting state session or tracked their pain levels during the pain tracking session. Applying Granger causality to analyze the data and requiring consistent findings across the two scanning sessions, we found 5 causal interactions, including: (1) Thalamus → dACC, (2) Caudate → Inferior temporal gyrus, (3) Precentral gyrus → Inferior temporal gyrus, (4) Supramarginal gyrus → Inferior temporal gyrus, and (5) Bankssts → Inferior temporal gyrus, that were consistently associated with the levels of pain experienced by the patients. Utilizing these 5 causal interactions as predictor variables and the pain score as the predicted variable in a linear multiple regression model, we found that in both pain tracking and resting state sessions, the model was able to explain ∼36 % of the variance in pain levels, and importantly, the model trained on the 5 causal interaction values from one session was able to predict pain levels using the 5 causal interaction values from the other session, thereby cross-validating the models. These results, obtained by applying novel analytical methods to neuroimaging data, provide important insights into the pathophysiology of TN and could inform future studies aimed at developing innovative therapies for treating TN.

Emergence of Emotion Selectivity in Deep Neural Networks Trained to Recognize Visual Objects

Recent neuroimaging studies have shown that the visual cortex plays an important role in representing the affective significance of visual input. The origin of these affect-specific visual representations is debated: they are intrinsic to the visual system versus they arise through reentry from frontal emotion processing structures such as the amygdala. We examined this problem by combining convolutional neural network (CNN) models of the human ventral visual cortex pre-trained on ImageNet with two datasets of affective images. Our results show that (1) in all layers of the CNN models, there were artificial neurons that responded consistently and selectively to neutral, pleasant, or unpleasant images and (2) lesioning these neurons by setting their output to 0 or enhancing these neurons by increasing their gain led to decreased or increased emotion recognition performance respectively. These results support the idea that the visual system may have the intrinsic ability to represent the affective significance of visual input and suggest that CNNs offer a fruitful platform for testing neuroscientific theories.

Emergence of Emotion Selectivity in Deep Neural Networks Trained to Recognize Visual Objects

Recent neuroimaging studies have shown that the visual cortex plays an important role in representing the affective significance of visual input. The origin of these affect-specific visual representations is debated: they are intrinsic to the visual system versus they arise through reentry from frontal emotion processing structures such as the amygdala. We examined this problem by combining convolutional neural network (CNN) models of the human ventral visual cortex pre-trained on ImageNet with two datasets of affective images. Our results show that in all layers of the CNN models, there were artificial neurons that responded consistently and selectively to neutral, pleasant, or unpleasant images and lesioning these neurons by setting their output to zero or enhancing these neurons by increasing their gain led to decreased or increased emotion recognition performance respectively. These results support the idea that the visual system may have the intrinsic ability to represent the affective significance of visual input and suggest that CNNs offer a fruitful platform for testing neuroscientific theories.

Decoding fMRI data with support vector machines and deep neural networks

BACKGROUND

Multivoxel pattern analysis (MVPA) examines fMRI activation patterns associated with different cognitive conditions. Support vector machines (SVMs) are the predominant method in MVPA. While SVM is intuitive and easy to apply, it is mainly suitable for analyzing data that are linearly separable. Convolutional neural networks (CNNs) are known to have the ability to approximate nonlinear relationships. Applications of CNN to fMRI data are beginning to appear with increasing frequency, but our understanding of the similarities and differences between CNN models and SVM models is limited.

NEW METHOD

We compared the two methods when they are applied to the same datasets. Two datasets were considered: (1) fMRI data collected from participants during a cued visual spatial attention task and (2) fMRI data collected from participants viewing natural images containing varying degrees of affective content.

RESULTS

We found that (1) both SVM and CNN are able to achieve above-chance decoding accuracies for attention control and emotion processing in both the primary visual cortex and the whole brain, (2) the CNN decoding accuracies are consistently higher than that of the SVM, (3) the SVM and CNN decoding accuracies are generally not correlated, and (4) the heatmaps derived from SVM and CNN are not significantly overlapping.

COMPARISON WITH EXISTING METHODS

By comparing SVM and CNN we pointed out the similarities and differences between the two methods.

CONCLUSIONS

SVM and CNN rely on different neural features for classification. Applying both to the same data may yield a more comprehensive understanding of neuroimaging data.

Transcranial stimulation of alpha oscillations modulates brain state dynamics in sustained attention

The brain operates an advanced complex system to support mental activities. Cognition is thought to emerge from dynamic states of the complex brain system, which are organized spatially through large-scale neural networks and temporally via neural synchrony. However, specific mechanisms underlying these processes remain obscure. Applying high-definition alpha-frequency transcranial alternating-current stimulation (HD α-tACS) in a continuous performance task (CPT) during functional resonance imaging (fMRI), we causally elucidate these major organizational architectures in a key cognitive operation-sustained attention. We demonstrated that α-tACS enhanced both electroencephalogram (EEG) alpha power and sustained attention, in a correlated fashion. Akin to temporal fluctuations inherent in sustained attention, our hidden Markov modeling (HMM) of fMRI timeseries uncovered several recurrent, dynamic brain states, which were organized through a few major neural networks and regulated by the alpha oscillation. Specifically, during sustain attention, α-tACS regulated the temporal dynamics of the brain states by suppressing a Task-Negative state (characterized by activation of the default mode network/DMN) and Distraction state (with activation of the ventral attention and visual networks). These findings thus linked dynamic states of major neural networks and alpha oscillations, providing important insights into systems-level mechanisms of attention. They also highlight the efficacy of non-invasive oscillatory neuromodulation in probing the functioning of the complex brain system and encourage future clinical applications to improve neural systems health and cognitive performance.

Top-Down Biasing of Visual Cortical Activity Encodes Attended Information and Facilitates Behavioral Performance in Visual Spatial Attention

Top-down attention plays a vital role in selecting relevant stimuli and suppressing distracting information. During top-down visual-spatial attention, control signals from the dorsal attention network modulate the baseline neuronal activity in the visual cortex in favor of task-relevant stimuli. While several studies have demonstrated that baseline shift during anticipatory attention occurs in multiple visual areas, such effects have not been systematically investigated across the visual hierarchy, especially when different attention conditions are matched for stimulus and task factors. In this fMRI study, we investigated anticipatory attention signals using univariate and multivariate (MVPA) analysis in multiple visual cortical areas. First, the univariate analysis yielded significant activation differences in higher-order visual areas, with the effect weaker in early visual areas. Second, however, in contrast, MVPA decoding was significant in predicting attention conditions in all visual areas and IPS, with lower-order visual areas (e.g., V1) having greater decoding accuracy than higher-order visual areas (e.g., LO1). Third, the strength of decoding accuracy predicted the behavioral performance in the discrimination task. All the results were highly replicable and consistent across two datasets with same experimental paradigms but recorded at two research sites, and two experimental conditions where the direction of spatial attention was driven either by external instructions (cue-instructed attention) or from internal decisions (free-choice attention). Our results provide clear evidence, not available in past univariate investigations, that top-down attentional control signals selectively bias neuronal processing throughout the visual hierarchy, and that this biasing is correlated with the task performance.

Dual-functional network regulation underlies the central executive system in working memory

The frontoparietal network (FPN) and cingulo-opercular network (CON) may exert top-down regulation corresponding to the central executive system (CES) in working memory (WM); however, contributions and regulatory mechanisms remain unclear. We examined network interaction mechanisms underpinning the CES by depicting CON- and FPN-mediated whole-brain information flow in WM. We used datasets from participants performing verbal and spatial working memory tasks, divided into encoding, maintenance, and probe stages. We used general linear models to obtain task-activated CON and FPN nodes to define regions of interest (ROI); an online meta-analysis defined alternative ROIs for validation. We calculated whole-brain functional connectivity (FC) maps seeded by CON and FPN nodes at each stage using beta sequence analysis. We used Granger causality analysis to obtain the connectivity maps and assess task-level information flow patterns. For verbal working memory, the CON functionally connected positively and negatively to task-dependent and task-independent networks, respectively, at all stages. FPN FC patterns were similar only in the encoding and maintenance stages. The CON elicited stronger task-level outputs. Main effects were: stable CON→FPN, CON→DMN, CON→visual areas, FPN→visual areas, and phonological areas→FPN. The CON and FPN both up-regulated task-dependent and down-regulated task-independent networks during encoding and probing. Task-level output was slightly stronger for the CON. CON→FPN, CON→DMN, visual areas→CON, and visual areas→FPN showed consistent effects. The CON and FPN might together underlie the CES's neural basis and achieve top-down regulation through information interaction with other large-scale functional networks, and the CON may be a higher-level regulatory core in WM.

Decoding fMRI Data: A Comparison Between Support Vector Machines and Deep Neural Networks

Multivoxel pattern analysis (MVPA) examines the differences in fMRI activation patterns associated with different cognitive conditions and provides information not possible with the conventional univariate analysis. Support vector machines (SVMs) are the predominant machine learning method in MVPA. SVMs are intuitive and easy to apply. The limitation is that it is a linear method and mainly suitable for analyzing data that are linearly separable. Convolutional neural networks (CNNs), a class of AI models originally developed for object recognition, are known to have the ability to approximate nonlinear relationships. CNNs are rapidly becoming an alternative to SVMs. The purpose of this study is to compare the two methods when they are applied to the same datasets. Two datasets were considered: (1) fMRI data collected from participants during a cued visual spatial attention task (the attention dataset) and (2) fMRI data collected from participants viewing natural images containing varying degrees of affective content (the emotion dataset). We found that (1) both SVM and CNN are able to achieve above chance level decoding accuracies for attention control and emotion processing in both the primary visual cortex and the whole brain with, (2) the CNN decoding accuracies are consistently higher than that of the SVM, (3) the SVM and CNN decoding accuracies are generally not correlated with each other, and (4) the heatmaps derived from SVM and CNN are not significantly overlapping. These results suggest that (1) there are both linearly separable features and nonlinearly separable features in fMRI data that distinguish cognitive conditions and (2) applying both SVM and CNN to the same data may yield a more comprehensive understanding of neuroimaging data.

Key points

We compared the performance and characteristics of SVM and CNN, two major methods in MVPA analysis of neuroimaging data, by applying them to the same two fMRI datasets.Both SVM and CNN achieved decoding accuracies above chance level for both datasets in the chosen ROIs and the CNN decoding accuracies were consistently higher than those of SVM.The heatmaps derived from SVM and CNN, which assess the contribution of voxels or brain regions to MVPA decoding performance, showed no significant overlap, providing evidence that the two methods depend on distinct brain activity patterns for decoding cognitive conditions.

Imaging the neural substrate of trigeminal neuralgia pain using deep learning

Trigeminal neuralgia (TN) is a severe and disabling facial pain condition and is characterized by intermittent, severe, electric shock-like pain in one (or more) trigeminal subdivisions. This pain can be triggered by an innocuous stimulus or can be spontaneous. Presently available therapies for TN include both surgical and pharmacological management; however, the lack of a known etiology for TN contributes to the unpredictable response to treatment and the variability in long-term clinical outcomes. Given this, a range of peripheral and central mechanisms underlying TN pain remain to be understood. We acquired functional magnetic resonance imaging (fMRI) data from TN patients who (1) rested comfortably in the scanner during a resting state session and (2) rated their pain levels in real time using a calibrated tracking ball-controlled scale in a pain tracking session. Following data acquisition, the data was analyzed using the conventional correlation analysis and two artificial intelligence (AI)-inspired deep learning methods: convolutional neural network (CNN) and graph convolutional neural network (GCNN). Each of the three methods yielded a set of brain regions related to the generation and perception of pain in TN. There were 6 regions that were identified by all three methods, including the superior temporal cortex, the insula, the fusiform, the precentral gyrus, the superior frontal gyrus, and the supramarginal gyrus. Additionally, 17 regions, including dorsal anterior cingulate cortex (dACC) and the thalamus, were identified by at least two of the three methods. Collectively, these 23 regions are taken to represent signature centers of TN pain and provide target areas for future studies seeking to understand the central mechanisms of TN.

Top-down control of the left visual field bias in cued visual spatial attention

A left visual field (LVF) bias in perceptual judgments, response speed, and discrimination accuracy has been reported in humans. Cognitive factors, such as visual spatial attention, are known to modulate or even eliminate this bias. We investigated this problem by recording pupillometry together with functional magnetic resonance imaging (fMRI) in a cued visual spatial attention task. We observed that (i) the pupil was significantly more dilated following attend-right than attend-left cues, (ii) the task performance (e.g. reaction time [RT]) did not differ between attend-left and attend-right trials, and (iii) the difference in cue-related pupil dilation between attend-left and attend-right trials was inversely related to the corresponding difference in RT. Neuroscientically, correlating the difference in cue-related pupil dilation with the corresponding cue-related fMRI difference yielded activations primarily in the right hemisphere, including the right intraparietal sulcus and the right ventrolateral prefrontal cortex. These results suggest that (i) there is an asymmetry in visual spatial attention control, with the rightward attention control being more effortful than the leftward attention control, (ii) this asymmetry underlies the reduction or the elimination of the LVF bias, and (iii) the components of the attentional control networks in the right hemisphere are likely part of the neural substrate of the observed asymmetry in attentional control.

Optimizing cognitive neuroscience experiments for separating event- related fMRI BOLD responses in non-randomized alternating designs

Functional magnetic resonance imaging (fMRI) has revolutionized human brain research. But there exists a fundamental mismatch between the rapid time course of neural events and the sluggish nature of the fMRI blood oxygen level-dependent (BOLD) signal, which presents special challenges for cognitive neuroscience research. This limitation in the temporal resolution of fMRI puts constraints on the information about brain function that can be obtained with fMRI and also presents methodological challenges. Most notably, when using fMRI to measure neural events occurring closely in time, the BOLD signals may temporally overlap one another. This overlap problem may be exacerbated in complex experimental paradigms (stimuli and tasks) that are designed to manipulate and isolate specific cognitive-neural processes involved in perception, cognition, and action. Optimization strategies to deconvolve overlapping BOLD signals have proven effective in providing separate estimates of BOLD signals from temporally overlapping brain activity, but there remains reduced efficacy of such approaches in many cases. For example, when stimulus events necessarily follow a non-random order, like in trial-by-trial cued attention or working memory paradigms. Our goal is to provide guidance to improve the efficiency with which the underlying responses evoked by one event type can be detected, estimated, and distinguished from other events in designs common in cognitive neuroscience research. We pursue this goal using simulations that model the nonlinear and transient properties of fMRI signals, and which use more realistic models of noise. Our simulations manipulated: (i) Inter-Stimulus-Interval (ISI), (ii) proportion of so-called null events, and (iii) nonlinearities in the BOLD signal due to both cognitive and design parameters. We offer a theoretical framework along with a python toolbox called deconvolve to provide guidance on the optimal design parameters that will be of particular utility when using non-random, alternating event sequences in experimental designs. In addition, though, we also highlight the challenges and limitations in simultaneously optimizing both detection and estimation efficiency of BOLD signals in these common, but complex, cognitive neuroscience designs.

Time Courses of Attended and Ignored Object Representations

Selective attention prioritizes information that is relevant to behavioral goals. Previous studies have shown that attended visual information is processed and represented more efficiently, but distracting visual information is not fully suppressed, and may also continue to be represented in the brain. In natural vision, to-be-attended and to-be-ignored objects may be present simultaneously in the scene. Understanding precisely how each is represented in the visual system, and how these neural representations evolve over time, remains a key goal in cognitive neuroscience. In this study, we recorded EEG while participants performed a cued object-based attention task that involved attending to target objects and ignoring simultaneously presented and spatially overlapping distractor objects. We performed support vector machine classification on the stimulus-evoked EEG data to separately track the temporal dynamics of target and distractor representations. We found that (1) both target and distractor objects were decodable during the early phase of object processing (∼100 msec to ∼200 msec after target onset), and (2) the representations of both objects were sustained over time, remaining decodable above chance until ∼1000-msec latency. However, (3) the distractor object information faded significantly beginning after about 300-msec latency. These findings provide information about the fate of attended and ignored visual information in complex scene perception.

Free Water Fraction Predicts Cognitive Decline for Individuals with Idiopathic Parkinson's disease

INTRODUCTION

Free water fraction (FWF) is considered a metric of microstructural integrity and may be useful in predicting cognitive decline in idiopathic Parkinson's Disease (PD). We sought to determine if higher FWF within the dorsal portion of the caudate nucleus and basal nucleus of Meynert, two regions associated with cognitive decline in PD, predict change in cognition over a two-year span. Due to the existence of cognitive and neurophysiological subgroups within PD, we statistically categorized participants based on FWF in these regions.

METHODS

At baseline, participants completed a research cognitive protocol followed by MRI structural and diffusion metrics. We used k-means cluster analysis with average FWF values from bilateral basal nucleus of Meynert and dorsal caudate to create data-driven FWF clusters for baseline. Two-year reliable change indices were calculated for metrics of language, visuospatial, memory, cognitive flexibility, and reasoning domains. Reliable change scores were compared between the clusters and non-PD peers.

RESULTS

Baseline participants included 174 participants (112 PD, 62 non-PD). Cluster analysis yielded three clusters: low FWF in both regions of interest (ROIs), high FWF in both ROIs, and moderate FWF in both ROIs. Reliable change analyses were completed on 93 participants (67 PD, 26 non-PD). After controlling for age and education, the High FWF cluster declined more than non-PD peers in every domain except memory.

CONCLUSION

Individuals with high FWF in regions associated with cognitive decline in PD show significant decline across several cognitive domains compared to non-PD peers. Future research should include FWF in additional cortical regions.

Aftereffects of Frontoparietal Theta tACS on Verbal Working Memory: Behavioral and Neurophysiological Analysis

Verbal working memory is supported by a left-lateralized frontoparietal theta oscillatory (4–8 Hz) network. We tested whether stimulating the left frontoparietal network at theta frequency during verbal working memory can produce observable after-stimulation effects in behavior and neurophysiology. Weak theta-band alternating electric currents were delivered via two 4 × 1 HD electrode arrays centered at F3 and P3. Three stimulation configurations, including in-phase, anti-phase, or sham, were tested on three different days in a cross-over (within-subject) design. On each test day, the subject underwent three experimental sessions: pre-, during- and post-stimulation sessions. In all sessions, the subject performed a Sternberg verbal working memory task with three levels of memory load (load 2, 4 and 6), imposing three levels of cognitive demand. Analyzing behavioral and EEG data from the post-stimulation session, we report two main observations. First, in-phase stimulation improved task performance in subjects with higher working memory capacity (WMC) under higher memory load (load 6). Second, in-phase stimulation enhanced frontoparietal theta synchrony during working memory retention in subjects with higher WMC under higher memory loads (load 4 and load 6), and the enhanced frontoparietal theta synchronization is mainly driven by enhanced frontal→parietal theta Granger causality. These observations suggest that (1) in-phase theta transcranial alternating current stimulation (tACS) during verbal working memory can result in observable behavioral and neurophysiological consequences post stimulation, (2) the short-term plasticity effects are state- and individual-dependent, and (3) enhanced executive control underlies improved behavioral performance.

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Frontoparietal network was stimulated at theta frequency (4 - 8Hz) during verbal working memory and aftereffeccts analyzed

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In-phase frontoparietal theta stimulation improved working memory performance in participants with higher working memory capacity

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Enhanced behavioral performance was accompanied by enhanced frontoparietal theta synchrony

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Enhanced frontoparietal theta synchronization was driven by enhanced frontal→parietal theta Granger causality

Lactation time influences the composition of Bifidobacterium and Lactobacillus at species level in human breast milk

Human breast milk is a source of microorganisms for infants that play an important role in building infant gut health and immunity. The bacterial composition in human breast milk is influenced by lactation time. This study aimed to investigate the influence of lactation time on bacteria in breast milk at the genus level and the species levels of Bifidobacterium and Lactobacillus on days 2-4, 8, 14, and 30. Eighteen individuals were recruited and 60 milk samples were collected. The 16S rRNA gene, and the bifidobacterial groEL and lactobacilli groEL genes were used for amplicon sequencing. The results revealed that the alpha diversities of colostrum and transition 1 (day 8) milk were lower than that of transition 2 (day 14) and mature milk. PCoA analysis showed that bacterial composition in colostrum and transition 1 milk differed from transition 2 and mature milk. A lower relative abundance of Blautia was found in colostrum and transition 1 milk compared with mature milk and lower abundances of Ruminococcus, Dorea, and Escherichia-Shigella were found in transition 1 compared with mature milk. Bifidobacterium ruminantium, Limosilactobacillus mucosae, and Ligilactobacillus ruminis were the predominant species across all four lactation stages, while Bifidobacterium bifidum was lower in transition 1, and Bifidobacterium pseudocatenulatum and Bifidobacterium pseudolongum were higher in transition 1 milk. This study indicated that the bacterial composition in colostrum was more similar to that of transition 1 milk, whereas the bacterial community in transition 2 milk was similar to that of mature milk which suggests that bacterial composition in human breast milk shows stage-specific signatures even within a short period at both genus level and Bifidobacterium and Lactobacillus species levels, providing insights into probiotic supplementation for the nursing mother.

Decoding the temporal dynamics of affective scene processing

Natural images containing affective scenes are used extensively to investigate the neural mechanisms of visual emotion processing. Functional fMRI studies have shown that these images activate a large-scale distributed brain network that encompasses areas in visual, temporal, and frontal cortices. The underlying spatial and temporal dynamics, however, remain to be better characterized. We recorded simultaneous EEG-fMRI data while participants passively viewed affective images from the International Affective Picture System (IAPS). Applying multivariate pattern analysis to decode EEG data, and representational similarity analysis to fuse EEG data with simultaneously recorded fMRI data, we found that: (1) ∼80 ms after picture onset, perceptual processing of complex visual scenes began in early visual cortex, proceeding to ventral visual cortex at ∼100 ms, (2) between ∼200 and ∼300 ms (pleasant pictures: ∼200 ms; unpleasant pictures: ∼260 ms), affect-specific neural representations began to form, supported mainly by areas in occipital and temporal cortices, and (3) affect-specific neural representations were stable, lasting up to ∼2 s, and exhibited temporally generalizable activity patterns. These results suggest that affective scene representations in the brain are formed temporally in a valence-dependent manner and may be sustained by recurrent neural interactions among distributed brain areas.

Anticipatory attention is a stable state induced by transient control mechanisms

Anticipatory attention is a neurocognitive state in which attention control regions bias neural activity in sensory cortical areas to facilitate the selective processing of incoming targets. Previous electroencephalographic (EEG) studies have identified event-related potential (ERP) signatures of anticipatory attention, and implicated alpha band (8–12 Hz) EEG oscillatory activity in the selective control of neural excitability in visual cortex. However, the degree to which ERP and alpha band measures reflect related or distinct underlying neural processes remains to be further understood. To investigate this question, we analyzed EEG data from 20 human participants performing a cued object-based attention task. We used support vector machine (SVM) decoding analysis to compare the attentional time courses of ERP signals and alpha band power. We found that ERP signals encoding attentional instructions are dynamic and precede stable attention-related changes in alpha power, suggesting that ERP and alpha power reflect distinct neural processes. We proposed that the ERP patterns reflect transient attentional orienting signals originating in higher order control areas, whereas the patterns of synchronized oscillatory neural activity in the alpha band reflect a sustained attentional state. These findings support the hypothesis that anticipatory attention involves transient top-down control signals that establish more stable neural states in visual cortex, enabling selective sensory processing.

Recommendations and publication guidelines for studies using frequency domain and time-frequency domain analyses of neural time series

Since its beginnings in the early 20th century, the psychophysiological study of human brain function has included research into the spectral properties of electrical and magnetic brain signals. Now, dramatic advances in digital signal processing, biophysics, and computer science have enabled increasingly sophisticated methodology for neural time series analysis. Innovations in hardware and recording techniques have further expanded the range of tools available to researchers interested in measuring, quantifying, modeling, and altering the spectral properties of neural time series. These tools are increasingly used in the field, by a growing number of researchers who vary in their training, background, and research interests. Implementation and reporting standards also vary greatly in the published literature, causing challenges for authors, readers, reviewers, and editors alike. The present report addresses this issue by providing recommendations for the use of these methods, with a focus on foundational aspects of frequency domain and time-frequency analyses. It also provides publication guidelines, which aim to (1) foster replication and scientific rigor, (2) assist new researchers who wish to enter the field of brain oscillations, and (3) facilitate communication among authors, reviewers, and editors.

Portable fluid circuit device containing printed silicone microvessels as a training aid for arterial microanastomosis

Anastomosis of the microvessels requires high-level skills and extensive basic training. This study was performed to introduce and evaluate an inexpensive laboratory device as a training aid. Micro-tubes of 0.8 mm inner diameter and 0.5/0.8 mm wall thickness mimicking human vein/artery were printed from a silicon-containing hydrogel using three-dimensional printing technology. The hydrogel components are optimized to render the printed tubes biomechanical features resembling the blood vessels of a living organism. These artificial vessels were connected to a pump for fluid flow, simulating the blood circulation. Forty medical interns were assigned to two equal groups. The 20 interns in group A practiced anastomosis using the training aid for a total of 10 hours over 5 days. The 20 interns in group B practiced anastomosis using the traditional gum pieces and silicone tubes. Then, all interns performed anastomosis on rat carotid arteries, and their performance was scored by a team of five experienced maxillofacial surgeons. The average success score and time required for anastomosis were compared between the two groups. The mean success score of group A was significantly higher than that of group B (0.83 ± 0.12 vs 0.64 ± 0.10, P < 0.001). The mean anastomosis time of group A was significantly shorter than that of group B (10.2 ± 1.1 vs 17.2 ± 1.4 minutes, P < 0.001). This training device for vessel microanastomosis is an inexpensive, practical, and effective tool for use in laboratories and also reduces the use of animals.

Functional connectivity of key resting state networks and objectively measured physical activity in older adults with joint pain: A pilot study

BACKGROUND

Greater brain network integrity may associate with physically active lifestyles. Three resting state networks may provide unique insights into known physical activity-mediated brain health benefits: the default mode network (involved with self-monitoring), the salience network (involved in orienting oneself to salient external and internal stimuli), and the central executive network (responsible for higher level cognitive task). The current study explored relationships between system-wide neural network integrity measured by functional magnetic resonance imaging and objectively-measured physical activity. We hypothesize connectivity patterns as measured by fMRI networks will relate to actigraphy markers such that 1) there will be higher connectivity within the central executive network in more physically active individuals, and 2) there will be higher connectivity within the default mode network and salience network in those with higher levels of physical activity.

METHODS

Eighteen non-demented older adults with orthopedic pain (age 67.11 ± 5.61, 50% female, education 15.94 ± 2.51 years) completed brain magnetic resonance imaging, and wore an actigraphy device to objectively measure types of physical and sedentary engagement.

RESULTS

Results showed a negative relationship between central executive network connectivity and sedentary time (β = -0.108, p = .039), and a positive relationship with both moderate-to-vigorous physical activity (β = 0.629, p = .029) and total activity time (β = 0.645, p = .039). Results also showed positive relationships for the default mode network (β = 0.588, p = .033) and the salience network (β = 0.608, p = .037) with mean cadence (i.e. steps per minute).

CONCLUSIONS

Our work adds to the existing literature on specific types of activity measurement (i.e. sedentary time, cadence and moderate-to-vigorous physical activity) which will be useful for interventions aimed at improving the integrity of underlying neural networks.

Fear conditioning prompts sparser representations of conditioned threat in primary visual cortex

Repeated exposure to threatening stimuli alters sensory responses. We investigated the underlying neural mechanism by re-analyzing previously published simultaneous electroencephalogram-functional magnetic resonance imaging (EEG-fMRI) data from humans viewing oriented gratings during Pavlovian fear conditioning. In acquisition, one grating (CS+) was paired with a noxious noise, the unconditioned stimulus (US). The other grating (CS-) was never paired with the US. In habituation, which preceded acquisition, and in extinction, the same two gratings were presented without US. Using fMRI multivoxel patterns in primary visual cortex during habituation as reference, we found that during acquisition, aversive learning selectively prompted systematic changes in multivoxel patterns evoked by CS+. Specifically, CS+ evoked voxel patterns in V1 became sparser as aversive learning progressed, and the sparsified pattern appeared to be preserved in extinction. Concomitant with the voxel pattern changes, occipital alpha oscillations were increasingly more desynchronized during CS+ (but not CS-) trials. Across acquisition trials, the rate of change in CS+-related alpha desynchronization was correlated with the rate of change in multivoxel pattern representations of CS+. Furthermore, alpha oscillations co-varied with blood-oxygen-level-dependent (BOLD) data in the ventral attention network, but not with BOLD in the amygdala. Thus, fear conditioning prompts persistent sparsification of voxel patterns evoked by threat, likely mediated by attention-related mechanisms

The Microstructure of Attentional Control in the Dorsal Attention Network

The top-down control of attention involves command signals arising chiefly in the dorsal attention network (DAN) in frontal and parietal cortex and propagating to sensory cortex to enable the selective processing of incoming stimuli based on their behavioral relevance. Consistent with this view, the DAN is active during preparatory (anticipatory) attention for relevant events and objects, which, in vision, may be defined by different stimulus attributes including their spatial location, color, motion, or form. How this network is organized to support different forms of preparatory attention to different stimulus attributes remains unclear. We propose that, within the DAN, there exist functional microstructures (patterns of activity) specific for controlling attention based on the specific information to be attended. To test this, we contrasted preparatory attention to stimulus location (spatial attention) and to stimulus color (feature attention), and used multivoxel pattern analysis to characterize the corresponding patterns of activity within the DAN. We observed different multivoxel patterns of BOLD activation within the DAN for the control of spatial attention (attending left vs. right) and feature attention (attending red vs. green). These patterns of activity for spatial and feature attentional control showed limited overlap with each other within the DAN. Our findings thus support a model in which the DAN has different functional microstructures for distinctive forms of top-down control of visual attention.

[Research progress in the role and mechanism of polysaccharides in regulating glucose and lipid metabolism]

Polysaccharides are a group of compounds composed of multiple monosaccharides of the same or different structures combined by glycosidic bonds, and are widely found in animals and plants and in the cell walls of microorganisms. Polysaccharides possess the advantages of high safety and low toxicity. Recent studies revealed that polysaccharides have a wide range of biological activities including immunoregulation, anti-tumor, antiviral, antioxidant activities, and blood glucose-and lipid- lowering effects. The effects of polysaccharides in improving insulin sensitivity and regulating glucose and lipid metabolism have drawn much attention from researchers. Many polysaccharides can reduce blood glucose and blood lipid by repairing pancreatic islet cells, improving insulin resistance, regulating intestinal flora, enhancing antioxidant capacity, and regulating the activities of key enzymes in glucose and lipid metabolism. This reviews examines the role and mechanism of polysaccharides in regulating glucose and lipid metabolism. The mechanisms of polysaccharide in regulating glucose metabolism include repairing islet cells and increasing insulin content, increasing insulin sensitivity and improving insulin resistance, regulating the activity of key enzymes in glucose metabolism, increasing synthesis of liver glycogen, and regulating intestinal flora. Polysaccharides can also regulate glucose metabolism by improving immune regulation and antagonizing glucagon. Polysaccharide also regulate lipid metabolism by regulating lipid absorption, expression of the related genes such as PPAR-α, enzyme activities in lipid metabolism, improving antioxidant capacity, and modulating intestinal flora and signaling pathways.

Decoding Neural Representations of Affective Scenes in Retinotopic Visual Cortex

The perception of opportunities and threats in complex visual scenes represents one of the main functions of the human visual system. The underlying neurophysiology is often studied by having observers view pictures varying in affective content. It has been shown that viewing emotionally engaging, compared with neutral, pictures (1) heightens blood flow in limbic, frontoparietal, and anterior visual structures and (2) enhances the late positive event-related potential (LPP). The role of retinotopic visual cortex in this process has, however, been contentious, with competing theories predicting the presence versus absence of emotion-specific signals in retinotopic visual areas. Recording simultaneous electroencephalography-functional magnetic resonance imaging while observers viewed pleasant, unpleasant, and neutral affective pictures, and applying multivariate pattern analysis, we found that (1) unpleasant versus neutral and pleasant versus neutral decoding accuracy were well above chance level in retinotopic visual areas, (2) decoding accuracy in ventral visual cortex (VVC), but not in early or dorsal visual cortex, was correlated with LPP, and (3) effective connectivity from amygdala to VVC predicted unpleasant versus neutral decoding accuracy, whereas effective connectivity from ventral frontal cortex to VVC predicted pleasant versus neutral decoding accuracy. These results suggest that affective scenes evoke valence-specific neural representations in retinotopic visual cortex and that these representations are influenced by reentry signals from anterior brain regions.

Statistically Defined Parkinson's Disease Executive and Memory Cognitive Phenotypes: Demographic, Behavioral, and Structural Neuroimaging Comparisons

BACKGROUND

Some individuals with Parkinson's disease (PD) experience working memory and inhibitory difficulties, others learning and memory difficulties, while some only minimal to no cognitive deficits for many years.

OBJECTIVE

To statistically derive PD executive and memory phenotypes, and compare PD phenotypes on disease and demographic variables, vascular risk factors, and specific neuroimaging variables with known associations to executive and memory function relative to non-PD peers.

METHODS

Non-demented individuals with PD (n = 116) and non-PD peers (n = 62) were recruited to complete neuropsychology measures, blood draw, and structural magnetic resonance imaging. Tests representing the cognitive domains of interest (4 executive function, 3 memory) were included in a k-means cluster analysis comprised of the PD participants. Resulting clusters were compared demographic and disease-related variables, vascular risk markers, gray/white regions of interest, and white matter connectivity between known regions involved in executive and memory functions (dorsolateral prefrontal cortices to caudate nuclei; entorhinal cortices to hippocampi).

RESULTS

Clusters showed: 1) PD Executive, n = 25; 2) PD Memory, n = 35; 3) PD Cognitively Well; n = 56. Even after disease variable corrections, PD Executive had less subcortical gray matter, white matter, and fewer bilateral dorsolateral-prefrontal cortex to caudate nucleus connections; PD Memory showed bilaterally reduced entorhinal-hippocampal connections. PD Cognitively Well showed only reduced putamen volume and right entorhinal cortex to hippocampi connections relative to non-PD peers. Groups did not statistically differ on cortical integrity measures or cerebrovascular disease markers.

CONCLUSION

PD cognitive phenotypes showed different structural gray and white matter patterns. We discuss data relative to phenotype demographics, cognitive patterns, and structural brain profiles.

Sarcopenia as a Risk Factor for Future Hip Fracture: A Meta-Analysis of Prospective Cohort Studies

OBJECTIVE

Our study aims to determine whether sarcopenia is a predictive factor of future hip fractures.

DESIGN

Systematic review and meta-analysis. Set: We searched for potentially suitable articles in PubMed, Cochrane library, Medline and EMBASE from inception to March 2020. The quality of the research was assessed by the Newcastle-Ottawa Scale (NOS). Finally, a meta-analysis was conducted with the Stata software.

PARTICIPANTS

Older community-dwelling residents.

MEASUREMENTS

Hip fracture due to sarcopenia.

RESULTS

We retrieved 2129 studies through our search strategy, and five studies with 23,359 individuals were analyzed in our pooled analyses. Sarcopenia increases the risk of future hip fractures with a pooled hazard ratio (HR) of 1.42 (95% CI: 1.18-1.71, P <0.001, I2 = 37.7%). In addition, in subgroup analyses based on different definitions of sarcopenia, sarcopenia was associated with the risk of future hip fractures with the Asian Working Group for Sarcopenia (AWGS) criteria with a pooled HR of 2.13(95% CI: 1.33-3.43). When subgroup analyses were conducted by sex, sarcopenia was associated with the risk for future hip fractures in females with pooled HRs of 1.69 (95% CI: 1.18-2.43). Sarcopenia was associated with the risk of future hip fractures in the group with a follow-up period of more than 5 years, with a pooled HR of 1.32 (95% CI: 1.08-1.61), and in the group with a follow-up period of less than 5 years, with a pooled HR of 2.13 (95% CI: 1.33-3.43).

CONCLUSIONS

Sarcopenia could significantly increase the risk of future hip fracture in old people; thus, it is necessary to prevent hip fractures in individuals with sarcopenia.

Morning brain: real-world neural evidence that high school class times matter

Researchers, parents and educators consistently observe a stark mismatch between biologically preferred and socially imposed sleep-wake hours in adolescents, fueling debate about high school start times. We contribute neural evidence to this debate with electroencephalogram data collected from high school students during their regular morning, mid-morning and afternoon classes. Overall, student alpha power was lower when class content was taught via videos than through lectures. Students' resting state alpha brain activity decreased as the day progressed, consistent with adolescents being least attentive early in the morning. During the lessons, students showed consistently worse performance and higher alpha power for early morning classes than for mid-morning classes, while afternoon quiz scores and alpha levels varied. Together, our findings demonstrate that both class activity and class time are reflected in adolescents' brain states in a real-world setting, and corroborate educational research suggesting that mid-morning may be the best time to learn.

FULLEROL alleviates myocardial ischemia-reperfusion injury by reducing inflammation and oxidative stress in cardiomyocytes via activating the Nrf2/HO-1 signaling pathway

OBJECTIVE

Myocardial ischemia-reperfusion injury (MIRI) is myocardial tissue injury caused by blood supply returning to myocardial tissue after ischemia or hypoxia. The purpose of this study was to investigate the protective effect of FULLEROL on myocardial tissue in MIRI rats and its mechanism.

MATERIALS AND METHODS

We use rats and ligate their left anterior descending coronary artery to make a MIRI model, and we also subcutaneously injected some MIRI rats with FULLEROL daily for two weeks before modeling. We determined the effects of model building and the therapeutic effect of FULLEROL on MIRI by detecting the changes of myocardial tissue morphology, myocardial injury markers and cardiac function in rats. In addition, we detected the expression changes of inflammatory factors and antioxidative molecules in rat myocardial tissue and serum to determine the effect of FULLEROL on inflammation and oxidative stress in myocardial tissue. Finally, we detected the activity of the Nrf2/HO-1 signaling pathway in rat cardiomyocytes to determine the mechanism of action of FULLEROL.

RESULTS

The structure of myocardial tissue in MIRI rats was remarkably damaged, and the range of myocardial infarction was increased. In addition, the concentrations of creatine kinase and lactate dehydrogenase were increased, and the heart function was reduced, while FULLEROL could reverse these conditions. In addition, FULLEROL was found to reduce the concentration of the inflammatory factors in rat myocardial tissue and serum, and to increase the expression of antioxidative molecules in myocardial tissue. The Nrf2/HO-1 signaling pathway was found to be related to MIRI and FULLEROL could increase the activity of the Nrf2/HO-1 signaling pathway in cardiomyocytes.

CONCLUSIONS

FULLEROL can alleviate MIRI by promoting the activity of the Nrf2/HO-1 signaling pathway to reduce the expression of inflammatory factors in rats and increase the antioxidative capacity of cardiomyocytes.

Decoding attention control and selection in visual spatial attention

Event‐related potentials (ERPs) are used extensively to investigate the neural mechanisms of attention control and selection. The univariate ERP approach, however, has left important questions inadequately answered. We addressed two questions by applying multivariate pattern classification to multichannel ERPs in two cued visual spatial attention experiments (N = 56): (a) impact of cueing strategies (instructional vs. probabilistic) on attention control and selection and (b) neural and behavioral effects of individual differences. Following cue onset, the decoding accuracy (cue left vs. cue right) began to rise above chance level earlier and remained higher in instructional cueing (~80 ms) than in probabilistic cueing (~160 ms), suggesting that unilateral attention focus leads to earlier and more distinct formation of the attention control set. A similar temporal sequence was also found for target‐related processing (cued target vs. uncued target), suggesting earlier and stronger attention selection under instructional cueing. Across the two experiments: (a) individuals with higher cue‐related decoding accuracy showed higher magnitude of attentional modulation of target‐evoked N1 amplitude, suggesting that better formation of anticipatory attentional state leads to stronger modulation of target processing, and (b) individuals with higher target‐related decoding accuracy showed faster reaction times (or larger cueing effects), suggesting that stronger selection of task‐relevant information leads to better behavioral performance. Taken together, multichannel ERPs combined with machine learning decoding yields new insights into attention control and selection that complement the univariate ERP approach, and along with the univariate ERP approach, provides a more comprehensive methodology to the study of visual spatial attention.

Theta Oscillations Index Frontal Decision-Making and Mediate Reciprocal Frontal-Parietal Interactions in Willed Attention

Attention can be attracted reflexively by sensory signals, biased by learning or reward, or focused voluntarily based on momentary goals. When voluntary attention is focused by purely internal decision processes (will), rather than instructions via external cues, we call this "willed attention." In prior work, we reported ERP and fMRI correlates of willed spatial attention in trial-by-trial cuing tasks. Here we further investigated the oscillatory mechanisms of willed attention by contrasting the event-related EEG spectrogram between instructional and choice cues. Two experiments were conducted at 2 different sites using the same visuospatial attention paradigm. Consistent between the 2 experiments, we found increases in frontal theta power (starting at ~500 ms post cue) for willed attention relative to instructed attention. This frontal theta increase was accompanied by increased frontal-parietal theta-band coherence and bidirectional Granger causality. Additionally, the onset of attention-related posterior alpha power lateralization was delayed in willed attention relative to instructed attention, and the amount of delay was related to the timing of frontal theta increase. These results, replicated across 2 experiments, suggest that theta oscillations are the neuronal signals indexing decision-making in the frontal cortex, and mediating reciprocal communications between the frontal executive and parietal attentional control regions during willed attention.

Mild Cognitive Impairment and Decline in Resting State Functional Connectivity after Total Knee Arthroplasty with General Anesthesia

BACKGROUND

Research shows that older adults can have a decline in three key resting state networks (default mode network, central executive network, and salience network) after total knee arthroplasty and that patients' pre-surgery brain and cognitive integrity predicts decline.

OBJECTIVES

First, to assess resting state network connectivity decline from the perspective of nodal connectivity changes in a larger older adult surgery sample. Second, to compare pre-post functional connectivity changes in mild cognitive impairment (MCI) versus non-MCI.

METHODS

Surgery (n = 69) and non-surgery (n = 65) peers completed a comprehensive preoperative neuropsychological evaluation and pre- and acute (within 48 hours) post-surgery/pseudo-surgery functional brain magnetic resonance imaging scan. MCI was classified within both (MCI surgery, n = 13; MCI non-surgery, n = 10). Using standard coordinates, we defined default mode network, salience network, central executive network, and the visual network (serving as a control network). The functional connectivity of these networks and brain areas (nodes) that make up these networks were examined for pre-post-surgery changes through paired samples t-test and ANOVA.

RESULTS

There was a decline in RSN connectivity after surgery (p < 0.05) only in the three cognitive networks (not the visual network). The default mode and salience network showed nodal connectivity changes (p < 0.01). MCI surgery had greater functional connectivity decline in DMN and SN. Non-surgery participants showed no significant functional connectivity change.

CONCLUSION

Surgery with general anesthesia selectively alters functional connectivity in major cognitive resting state networks particularly in DMN and SN. Participants with MCI appear more vulnerable to these functional changes.

Protective effects of melatonin on mitochondrial biogenesis and mitochondrial structure and function in the HEK293-APPswe cell model of Alzheimer's disease

OBJECTIVE

The effects and mechanisms of melatonin on Alzheimer's disease (AD) are still not researched thoroughly. 20E2 cells (HEK293-APPswe cells) are a cellular model of AD. The modulation effects of melatonin on the structure and function of mitochondria in 20E2 cells need to be studied.

MATERIALS AND METHODS

The Alzheimer's disease (AD) cell model was assessed for cell viability, expression levels of mitochondrial biogenesis factors (peroxisome proliferator-activated receptor gamma coactivator 1-alpha [PGC-1α], nuclear respiratory factor 1 [NRF1], nuclear respiratory factor 2 [NRF2], mitochondrial transcription factor A [TFAM]), mitochondrial membrane potential, Na+-K+-adenosine triphosphatase (ATPase) and cytochrome C oxidase activity, adenosine triphosphate (ATP) level, mitochondrial DNA/nuclear DNA (mtDNA/nDNA) ratio, and mitochondrial structure with and without melatonin.

RESULTS

Melatonin improved 20E2 cell viability, expression of mitochondrial biogenesis factors (PGC-1α, NRF1, NRF2, TFAM), mitochondrial membrane potential, Na+-K+-ATPase, and cytochrome C oxidase activity, ATP level, mtDNA/nDNA ratio, mitochondrial structure, and decreased amyloidogenic amyloid precursor protein processing.

CONCLUSIONS

Mitochondrial biogenesis disorder is associated with the pathogenesis of AD through PGC-1α-NRF-TFAM pathway, and melatonin improves the mitochondrial structure and function by enhancing mitochondrial biogenesis and decreasing amyloidogenic APP processing in Alzheimer's disease.

Characterization of a P18 protein in the S1 segment of the novel duck reovirus genome

Novel duck reovirus (NDRV), the prototype strain of avian orthoreoviruses, continues to circulate among ducks. Analysis of its genome suggested that a putative second open reading frame in the S1 segment encodes a 162-amino acid nonstructural protein with size of 18 kDa, provisionally designated P18. This protein is different from the 17 kDa nonstructural protein encoded in the same open reading frame in other avian orthoreoviruses, which is designated P17 and consists of 146 amino acids. There is no corresponding protein in Muscovy duck reovirus. Antibodies raised to the purified recombinant protein reacted with viral P18 both in vitro and in vivo. In cells, P18 was located predominantly in the nucleus at 6-12 h post-infection, with negligible levels in the cytoplasm. However, the protein accumulated both in the nucleus and cytoplasm at 24 to 36 h post-infection. Immunohistochemistry indicated that P18 strongly accumulates in spleen tissues of infected ducklings. Collectively, the data provide the direct experimental evidence that P18 is expressed by novel duck reovirus both in vivo and in vitro. Keywords: duck reovirus; expression; characterization; novel P18 protein.

Sequential treatment by polidocanol and radiofrequency ablation of large benign partially cystic thyroid nodules with solid components: Efficacy and safety

PURPOSE

The purpose of this study was to retrospectively evaluate the efficacy and safety of a sequential treatment including percutaneous polidocanol sclerotherapy and radiofrequency ablation (RFA) in terms of volume reduction and complication rate in large, benign, partially cystic thyroid nodules with solid components.

MATERIALS AND METHOD

From April 2017 to April 2019, 46 patients with 47 large benign partially cystic thyroid nodules underwent sequential treatment. There were 14 men and 32 women with a mean age of 49.9±11.5 (SD) years (range: 18-75 years). The volume of initial nodules was 12.7±12.3 (SD) mL (range: 2.16-75.62mL). Volume reduction after percutaneous polidocanol sclerotherapy and further RFA was evaluated respectively. Patients had clinical and ultrasound evaluations at a follow-up time of 12.1±5.3 (SD) months (range: 1.5-23.9 months). Technical success and complications were accessed retrospectively.

RESULTS

After unsatisfying results with polidocanol sclerotherapy alone the 46 patients with 47 large benign partially cystic thyroid nodules had further RFA. Mean volume reduction of 47 nodules was 90.5±11.3 (SD) % (range: 43.9-99.3%) one month after RFA, 94.9±6.2 (SD) % (range: 66.9-99.5%) three months after RFA, and 95.8±5.5 (SD) % (range: 71.0-99.8%) six months after RFA. No recurrence or nodule enlargement after RFA was observed at the last follow-up. The complication rate of RFA was 12.5% (8/46 patients), with minor complications only.

CONCLUSIONS

The sequential treatment regimen, including percutaneous polidocanol sclerotherapy and RFA, is an appropriate and safe treatment strategy for large benign partially cystic thyroid nodules with solid components.