Sensory Networks Research Articles

Electrophysiological (EEG) Correlates of Reward Effects on Early Sensory Perception in Humans

Existing research suggests that reward system and sensory perception networks function in concert and that activation in one may influence the other; however, the dynamics of these influences remains poorly understood. There is general agreement regarding the existence of an interaction between bottom-up and top-down signals in perception and attentional processing. While it’s not absolutely sure what stages of perception are influenced by cognition, it has been assumed that cognitive input influences later categorization stages of visual processing, and that earlier stages are solely involved in the pure bottom up extraction of basic features of sensory signals. Several recent experiments have challenged this idea by showing that top-down modulation by cognition may extend to early visual stages of perception. Recent Electrophysiological studies have begun to probe the neural correlates of the interaction between attention/reward, perception and cognitive control in humans. . We posit that selection of the value of our choices and actions from multiple alternatives may cause suppression of the sensory representations of unselected, low value stimuli while the selected, high value stimuli are enhanced. The present project has been proposed to study the dynamics of this selective process where effects of various reward categories on attention and early sensory perception are tracked using behavioral and electrophysiological (EEG) techniques. Methods from neuroscience, signal processing, psychophysics and EEG tools will be used in the project.

Air pollution seasons in urban moderate climate areas through big data analytics

High particulate matter (PM) concentrations have a negative impact on the overall quality of life and health. The annual trends of PM can vary greatly depending on factors such as a country’s energy mix, development level, and climatic zone. In this study, we aimed to understand the annual cycle of PM concentrations in a moderate climate zone using a dense grid of low-cost sensors located in central Europe (Krakow). Over one million unique records of PM, temperature, humidity, pressure and wind speed observations were analyzed to gain a detailed, high-resolution understanding of yearly fluctuations. The comprehensive big-data workflow was presented with the statistical analysis of the meteorological factors. A big data-driven approach revealed the existence of two main PM seasons (warm and cold) in Europe’s moderate climate zone, which do not correspond directly with the traditional four main seasons (Autumn, Winter, Spring, and Summer) with two side periods (early spring and early winter). Our findings also highlighted the importance of high-resolution time and space data for sustainable spatial planning. The observations allowed for distinguishing whether the source of air pollution is related to coal burning for heating in cold period or to agricultural lands burning during the warm period.

Parent–child couples display shared neural fingerprints while listening to stories

Neural fingerprinting is a method to identify individuals from a group of people. Here, we established a new connectome-based identification model and used diffusion maps to show that biological parent–child couples share functional connectivity patterns while listening to stories. These shared fingerprints enabled the identification of children and their biological parents from a group of parents and children. Functional patterns were evident in both cognitive and sensory brain networks. Defining “typical” shared biological parent–child brain patterns may enable predicting or even preventing impaired parent–child connections that develop due to genetic or environmental causes. Finally, we argue that the proposed framework opens new opportunities to link similarities in connectivity patterns to behavioral, psychological, and medical phenomena among other populations. To our knowledge, this is the first study to reveal the neural fingerprint that represents distinct biological parent–child couples.

Pons-to-Cerebellum Hypoconnectivity Along the Psychosis Spectrum and Associations With Sensory Prediction and Hallucinations in Schizophrenia

BackgroundSensory prediction allows the brain to anticipate and parse incoming self-generated sensory information from externally generated signals. Sensory prediction breakdowns may contribute to perceptual and agency abnormalities in psychosis (hallucinations, delusions). The pons, a central node in a cortico-ponto-cerebellar-thalamo-cortical circuit, is thought to support sensory prediction. Examination of pons connectivity in schizophrenia and its role in sensory prediction abnormalities is lacking. MethodsWe examined these relationships using resting-state functional magnetic resonance imaging and the electroencephalography-based auditory N1 event-related potential in 143 participants with psychotic spectrum disorders (PSPs) (with schizophrenia, schizoaffective disorder, or bipolar disorder); 63 first-degree relatives of individuals with psychosis; 45 people at clinical high risk for psychosis; and 124 unaffected comparison participants. This unique sample allowed examination across the psychosis spectrum and illness trajectory. Seeding from the pons, we extracted average connectivity values from thalamic and cerebellar clusters showing differences between PSPs and unaffected comparison participants. We predicted N1 amplitude attenuation during a vocalization task from pons connectivity and group membership. We correlated participant-level connectivity in PSPs and people at clinical high risk for psychosis with hallucination and delusion severity. ResultsCompared to unaffected comparison participants, PSPs showed pons hypoconnectivity to 2 cerebellar clusters, and first-degree relatives of individuals with psychosis showed hypoconnectivity to 1 of these clusters. Pons-to-cerebellum connectivity was positively correlated with N1 attenuation; only PSPs with heightened pons-to-postcentral gyrus connectivity showed this pattern, suggesting a possible compensatory mechanism. Pons-to-cerebellum hypoconnectivity was correlated with greater hallucination severity specifically among PSPs with schizophrenia. ConclusionsDeficient pons-to-cerebellum connectivity linked sensory prediction network breakdowns with perceptual abnormalities in schizophrenia. Findings highlight shared features and clinical heterogeneity across the psychosis spectrum.

Altered dynamic functional network connectivity in rheumatoid arthritis associated with peripheral inflammation and neuropsychiatric disorders

ObjectiveThis study explored the dynamic functional connective (DFC) alterations in patients with rheumatoid arthritis (RA) and investigated the correlation between the neuropsychiatric symptoms, peripheral inflammation and DFC alterations.MethodUsing resting-state functional...

SGS method applied to damage location and uncertainty modeling for sensor grid in the ISHM

SGS method applied to damage location and uncertainty modeling for sensor grid in the ISHM

Changes in Intrinsic Connectivity Networks Topology Across Levels of Dexmedetomidine-Induced Alteration of Consciousness.

Human consciousness is generally thought to emerge from the activity of intrinsic connectivity networks (resting-state networks [RSNs]) of the brain, which have topological characteristics including, among others, graph strength and efficiency. So far, most functional brain imaging studies in anesthetized subjects have compared wakefulness and unresponsiveness, a state considered as corresponding to unconsciousness. Sedation and general anesthesia not only produce unconsciousness but also phenomenological states of preserved mental content and perception of the environment (connected consciousness), and preserved mental content but no perception of the environment (disconnected consciousness). Unresponsiveness may be seen during unconsciousness, but also during disconnectedness. Deep dexmedetomidine sedation is frequently a state of disconnected consciousness. In this study, we were interested in characterizing the RSN topology changes across 4 different and steady-state levels of dexmedetomidine-induced alteration of consciousness, namely baseline (Awake, drug-free state), Mild sedation (drowsy, still responding), Deep sedation (unresponsive), and Recovery, with a focus on changes occurring between a connected consciousness state and an unresponsiveness state. A functional magnetic resonance imaging database acquired in 14 healthy volunteers receiving dexmedetomidine sedation was analyzed using a method combining independent component analysis and graph theory, specifically looking at changes in connectivity strength and efficiency occurring during the 4 above-mentioned dexmedetomidine-induced altered consciousness states. Dexmedetomidine sedation preserves RSN architecture. Unresponsiveness during dexmedetomidine sedation is mainly characterized by a between-networks graph strength alteration and within-network efficiency alteration of lower-order sensory RSNs, while graph strength and efficiency in higher-order RSNs are relatively preserved. The differential dexmedetomidine-induced RSN topological changes evidenced in this study may be the signature of inadequate processing of sensory information by lower-order RSNs, and of altered communication between lower-order and higher-order networks, while the latter remain functional. If replicated in an experimental paradigm distinguishing, in unresponsive subjects, disconnected consciousness from unconsciousness, such changes would sustain the hypothesis that disconnected consciousness arises from altered information handling by lower-order sensory networks and altered communication between lower-order and higher-order networks, while the preservation of higher-order networks functioning allows for an internally generated mental content (or dream).

Optimal Image Characterization for In-Bed Posture Classification by Using SVM Algorithm

Identifying patient posture while they are lying in bed is an important task in medical applications such as monitoring a patient after a surgical intervention, sleep supervision to identify behavioral and physiological markers, or for bedsore prevention. An acceptable strategy to identify the patient’s position is the classification of images created from a grid of pressure sensors located in the bed. These samples can be arranged based on supervised learning methods. Usually, image conditioning is required before images are loaded into a learning method to increase classification accuracy. However, continuous monitoring of a person requires large amounts of time and computational resources if complex pre-processing algorithms are used. So, the problem is to classify the image posture of patients with different weights, heights, and positions by using minimal sample conditioning for a specific supervised learning method. In this work, it is proposed to identify the patient posture from pressure sensor images by using well-known and simple conditioning techniques and selecting the optimal texture descriptors for the Support Vector Machine (SVM) method. This is in order to obtain the best classification and to avoid image over-processing in the conditioning stage for the SVM. The experimental stages are performed with the color models Red, Green, and Blue (RGB) and Hue, Saturation, and Value (HSV). The results show an increase in accuracy from 86.9% to 92.9% and in kappa value from 0.825 to 0.904 using image conditioning with histogram equalization and a median filter, respectively.

Nonvolatile Memristive Materials and Physical Modeling for In‐Memory and In‐Sensor Computing

Separate memory and processing units are utilized in conventional von Neumann computational architectures. However, regarding the energy and the time, it is costly to shuffle data between the memory and the processing entity, and for data‐intensive applications associated with artificial intelligence, the demand is ever increasing. A paradigm shift in traditional architectures is required, and in‐memory computing is one of the non‐von‐Neumann computing strategies. By harnessing physical signatures of the memory, computing workloads are administered in the same memory element. For in‐memory computing, a wide range of memristive material (MM) systems have been examined. Moreover, developing computing schemes that perform in the same sensory network and that minimize the data shuffle between the processing unit and the sensing element is a requirement, to process large volumes of data efficiently and decrease the energy consumption. In this review, an overview of the switching character and system signature harnessed in three archetypal MM systems is rendered, along with an integrated application survey for developing in‐sensor and in‐memory computing, viz., brain‐inspired or analogue computing, physical unclonable functions, and random number generators. The recent progress in theoretical studies that reveal the structural origin of the fast‐switching ability of the MM system is further summarized.

Label-based meta-analysis of functional brain dysconnectivity across mood and psychotic disorders

BackgroundResting-state functional magnetic resonance imaging (rsfMRI) studies have revealed patterns of functional brain dysconnectivity in psychiatric disorders such as major depression disorder (MDD), bipolar disorder (BD) and schizophrenia (SZ). Although these disorders have been mostly studied in isolation, there is mounting evidence of shared neurobiological alterations across them. MethodsTo uncover the nature of the relatedness between these psychiatric disorders, we conducted an innovative meta-analysis of dysconnectivity findings reported separately in MDD, BD and SZ. Rather than relying on a classical voxel level coordinate-based approach, our procedure extracted relevant neuroanatomical labels from text data and examined findings at the whole brain network level. Data were drawn from 428 rsfMRI studies investigating MDD (158 studies, 7429 patients/7414 controls), BD (81 studies, 3330 patients/4096 patients) and/or SZ (223 studies, 11,168 patients/11,754 controls). Permutation testing revealed commonalities and differences in hypoconnectivity and hyperconnectivity patterns across disorders. ResultsHypoconnectivity and hyperconnectivity patterns of higher-order cognitive (default-mode, fronto-parietal, cingulo-opercular) networks were similarly observed across the three disorders. By contrast, dysconnectivity of lower-order (somatomotor, visual, auditory) networks in some cases differed between disorders, notably dissociating SZ from BD and MDD. ConclusionsFindings suggest that functional brain dysconnectivity of higher-order cognitive networks is largely transdiagnostic in nature while that of lower-order networks may best discriminate between mood and psychotic disorders, thus emphasizing the relevance of motor and sensory networks to psychiatric neuroscience.

Sensory Gating Networks in Normal-Hearing Adults With Minimal Tinnitus.

The goal of this study was to observe sensory gating-related networks underlying cortical auditory evoked potential (CAEP) peak components in individuals with and without minimal tinnitus, as measured using the Tinnitus Handicap Inventory (THI). This analysis was performed on previously published sensory gating responses in normal-hearing adults with and without minimal tinnitus. Independent component analysis was performed for each individual CAEP gating component (Pa, P50, N1, and P2). Significant components were retained for source localization analyses within the following groups: no tinnitus, tinnitus with a THI score ≤ 6, and tinnitus with a THI score > 6. Brain source localization was performed on the gating difference wave for each component using standardized low-resolution brain electromagnetic tomography. Gating-related networks were identified within each group. Different regional sources were observed between groups, with parietal sources underlying the Pa and P50 components as tinnitus severity increased. A larger prefrontal regional activation was also shown for the N1 gating component as tinnitus severity increased. These results expand upon the functional gating responses via CAEP waveforms in a previously published study. The auditory gating response, as measured via CAEPs, has previously been shown to significantly correlate with an increase in tinnitus severity in adults with normal hearing. The corresponding changes in the gating response appear to be supported by different cortical regions in those without tinnitus, those with a THI score ≤ 6, and those with a THI score > 6. Next, functional differences between localized cortical regions should be tested.

Time-varying phase synchronization of resting-state functional magnetic resonance imaging reveals a shift toward self-referential processes during sustained pain.

Growing evidence has suggested that time-varying functional connectivity between different brain regions might underlie the dynamic experience of pain. This study used a novel, data-driven framework to characterize the dynamic interactions of large-scale brain networks during sustained pain by estimating recurrent patterns of phase-synchronization. Resting-state functional magnetic resonance imaging signals were collected from 50 healthy participants before (once) and after (twice) the onset of sustained pain that was induced by topical application of capsaicin cream. We first decoded the instantaneous phase of neural activity and then applied leading eigenvector dynamic analysis on the time-varying phase-synchronization. We identified 3 recurrent brain states that show distinctive phase-synchronization. The presence of state 1, characterized by phase-synchronization between the default mode network and auditory, visual, and sensorimotor networks, together with transitions towards this brain state, increased during sustained pain. These changes can account for the perceived pain intensity and reported unpleasantness induced by capsaicin application. In contrast, state 3, characterized by phase-synchronization between the cognitive control network and sensory networks, decreased after the onset of sustained pain. These results are indicative of a shift toward internally directed self-referential processes (state 1) and away from externally directed cognitive control processes (state 3) during sustained pain.

Reproducible Spinal Cord Quantitative MRI Analysis with the Spinal Cord Toolbox.

The spinal cord plays a pivotal role in the central nervous system, providing communication between the brain and the body and containing critical motor and sensory networks. Recent advancements in spinal cord MRI data acquisition and image analysis have shown a potential to improve the diagnostics, prognosis, and management of a variety of pathological conditions. In this review, we first discuss the significance of standardized spinal cord MRI acquisition protocol in multi-center and multi-manufacturer studies. Then, we cover open-access spinal cord MRI datasets, which are important for reproducible science and validation of new methods. Finally, we elaborate on the recent advances in spinal cord MRI data analysis techniques implemented in the open-source software package Spinal Cord Toolbox (SCT).

Research on Fault Detection and Localization Techniques for Distribution Networks Based on Edge Clustering

Abstract With the intelligent transformation of the power grid, the number and type of various terminals, sensors, and new types of loads in the distribution network increase, and the huge amount of information and noise information accessing the power grid brings great challenges for fault detection and localization. In this paper, we propose to combine wavelet transform and LSTM unit to form a novel neural network basic unit TFM and examine the fault detection and localization capability of TFM by combining them with a typical example system, IEEE33. Spectral clustering and the K-Means algorithm are utilized to cluster the edge nodes, and the number of nodes in the three partitions after partition correction is 11, 10, and 12 nodes in order. The data shows that the TFM system performs the fault diagnosis task with better test accuracy than LSTM in every dimension, and its accuracy improvement for fault localization is the largest, at 5.39%. Introducing two reconfiguration scenarios for three edge node partitioning models and retrograde fault localization detection, compared with the no reconfiguration scenario, their localization accuracies all produce different degrees of decline, but always not less than 80%. The localization accuracy of the MⅡ model is still not less than 98% in the fault resistance range of 500~1000Ω, which proves that the TFM system can effectively extract high-resistance fault features.

How Interacting Bottom-up Sensory and Top-Down Context Neural Networks Facilitate Word Recognition

How Interacting Bottom-up Sensory and Top-Down Context Neural Networks Facilitate Word Recognition

Design of a sensor structure for linear displacement measurement based on a capacitive grid sensor

Design of a sensor structure for linear displacement measurement based on a capacitive grid sensor

The Effect of Transcutaneous Vagus Nerve Stimulation on Chinese Lexical Tone Imitation: A Single-Subject Design

Transcutaneous vagus nerve stimulation (tVNS) has been reported on its effect to reorganize the sensory neural network in the central nervous system, similar as the observations in the traditional invasive vagus nerve stimulation.

Cross-hemicord spinal fiber reorganization associates with cortical sensory and motor network expansion in the rat model of hemicontusion cervical spinal cord injury

Magnetic resonance imaging plays an important role in characterizing microstructural changes and reorganization after traumatic injuries to the nervous system. In this study, we tested the feasibility of ex-vivo spinal cord diffusion tensor imaging (DTI) in combination with in vivo brain functional MRI to characterize spinal reorganization and its supraspinal association after a hemicontusion cervical spinal cord injury (SCI). DTI parameters (fractional anisotropy [FA], mean diffusion [MD]) and fiber orientation changes related to reorganization in the contused cervical spinal cord were compared to sham specimens. Altered fiber density and fiber directions occurred across the ipsilateral and contralateral hemicords but with only ipsilateral FA and MD changes. The hemicontusion SCI resulted in ipsilateral fiber breaks, voids and vivid fiber reorientations along the injury epicenter. Fiber directional changes below the injury level were primarily inter-hemispheric, indicating prominent below-level cross-hemispheric reorganization. In vivo resting state functional connectivity of the brain from the respective rats before obtaining the spinal cord samples indicated spatial expansion and increased connectivity strength across both the sensory and motor networks after SCI. The consistency of the neuroplastic changes along the neuraxis (both brain and spinal cord) at the single-subject level, indicates that distinctive reorganizational relationships exist between the spinal cord and the brain post-SCI.

Functional network dynamics revealed by EEG microstates reflect cognitive decline in amyotrophic lateral sclerosis.

Recent electroencephalography (EEG) studies have shown that patterns of brain activity can be used to differentiate amyotrophic lateral sclerosis (ALS) and control groups. These differences can be interrogated by examining EEG microstates, which are distinct, reoccurring topographies of the scalp's electrical potentials. Quantifying the temporal properties of the four canonical microstates can elucidate how the dynamics of functional brain networks are altered in neurological conditions. Here we have analysed the properties of microstates to detect and quantify signal-based abnormality in ALS. High-density resting-state EEG data from 129 people with ALS and 78 HC were recorded longitudinally over a 24-month period. EEG topographies were extracted at instances of peak global field power to identify four microstate classes (labelled A-D) using K-means clustering. Each EEG topography was retrospectively associated with a microstate class based on global map dissimilarity. Changes in microstate properties over the course of the disease were assessed in people with ALS and compared with changes in clinical scores. The topographies of microstate classes remained consistent across participants and conditions. Differences were observed in coverage, occurrence, duration, and transition probabilities between ALS and control groups. The duration of microstate class B and coverage of microstate class C correlated with lower limb functional decline. The transition probabilities A to D, C to B and C to B also correlated with cognitive decline (total ECAS) in those with cognitive and behavioural impairments. Microstate characteristics also significantly changed over the course of the disease. Examining the temporal dependencies in the sequences of microstates revealed that the symmetry and stationarity of transition matrices were increased in people with late-stage ALS. These alterations in the properties of EEG microstates in ALS may reflect abnormalities within the sensory network and higher-order networks. Microstate properties could also prospectively predict symptom progression in those with cognitive impairments.

Synchrony in auditory 40-Hz gamma oscillations increases in older age and correlates with hearing abilities and cortical GABA levels

Abstract Synchronized 40-Hz gamma oscillations in specific sensory and higher-order thalamocortical networks provide a neural mechanism for feature binding. Aging-related changes in gamma oscillations may cause deficits in auditory feature binding, contributing to impaired speech-in-noise perception. Gamma synchrony is controlled through inhibitory mechanisms mediated by the neurotransmitter γ-aminobutyric acid (GABA), which has been shown to decline in aging. This study investigated aging-related changes in gamma oscillations and how they relate to auditory function and cortical GABA levels. Magnetoencephalograms of 40-Hz auditory steady-state responses (ASSRs) were recorded in young and older adults by presenting amplitude-modulated tones in quiet and mixed with concurrent multi-talker babble noise. Responses in the quiet condition had longer latencies and more prominent amplitudes, indicating the 40-Hz ASSRs in noise were dominated by a sensory component and in quiet by a component involved in higher-order processing. The ASSR amplitudes increased in older adults under both stimulus conditions. However, larger ASSR amplitudes were associated with more severe hearing and speech-in-noise loss only in the noise condition. This suggests the aging-related increase in synchrony of sensory gamma oscillations has a detrimental effect on auditory processing. It may cause increased interference between competing sounds in the central auditory system, making it difficult for the aging auditory system to separate speech features from noise and bind them into a distinct perceptual object. Also in older adults, larger amplitudes of the 40-Hz ASSRs in the quiet condition were associated with higher left auditory cortex GABA concentrations measured with magnetic resonance spectroscopy, supporting GABA’s role in internally generated gamma synchrony in aging.