Source Reconstruction Research Articles

Medieval Rabbinic Literature Seen in a New Light: The Sources of Yalkut Shimoni on the Book of the Twelve Prophets

Abstract The Yalkut Shimoni compiles important annotations from rabbinic literature to each book of the Hebrew Bible. The voluminous commentary cites more than 50 traditional rabbinic texts including sources which have been lost. Most academic research on the Yalkut Shimoni has focused on reconstructing these lost sources such as Midrash Yelamdenu, other research issues about Yalkut Shimoni have been neglected. This paper will focus on the Yalkut Shimoni on the Book of the Twelve Prophets. The author of the Yalkut Shimoni was not able to rely on consecutively commentaries on the Twelve Prophets such as for example the commentaries on the book of Numbers. This paper will therefore focus on the question of compilation techniques. Consequently, the author had to use pieces of commentaries on the Torah or other biblical books where Minor Prophets are cited used these to compile them to a new commentary. Furthermore, he puts these fragments together without naming the sources or introducing them to the text. This paper will show how the author combines the Palestinian and the Babylonian tradition. The author uses different techniques to cut and inweave different commentaries. Furthermore, the focus lays on the ongoing discussion whether the Yalkut Shimoni can be seen as a compilation at all. If this important work can be proofed to be no compilation, it has severe consequences especially for the reconstruction of lost sources quoted inside the Yalkut Shimoni.

Investigating the Impact of the Regularization Parameter on EEG Resting-State Source Reconstruction and Functional Connectivity Using Real and Simulated Data

Accurate EEG source localization is crucial for mapping resting-state network dynamics and it plays a key role in estimating source-level functional connectivity. However, EEG source estimation techniques encounter numerous methodological challenges, with a key one being the selection of the regularization parameter in minimum norm estimation. This choice is particularly intricate because the optimal amount of regularization for EEG source estimation may not align with the requirements of EEG connectivityanalysis, highlighting a nuanced trade-off. In this study, we employed a methodological approach to determine the optimal regularization coefficient that yields the most effective reconstruction outcomes across all simulations involving varying signal-to-noise ratios for synthetic EEG signals. To this aim, we considered three resting state networks: the Motor Network, the Visual Network, and the Dorsal Attention Network. The performance was assessed using three metrics, at different regularization parameters: the Region Localization Error, source extension, and source fragmentation. The results were validated using real functional connectivity data. We show that the best estimate of functional connectivity is obtained using 10−2, while 10−1 has to be preferred when source localization only is at target.

Improving EEG Forward Modeling Using High-Resolution Five-Layer BEM-FMM Head Models: Effect on Source Reconstruction Accuracy

Electroencephalographic (EEG) source localization is a fundamental tool for clinical diagnoses and brain-computer interfaces. We investigate the impact of model complexity on reconstruction accuracy by comparing the widely used three-layer boundary element method (BEM) as an inverse method against a five-layer BEM accelerated by the fast multipole method (BEM-FMM) and coupled with adaptive mesh refinement (AMR) as forward solver. Modern BEM-FMM with AMR can solve high-resolution multi-tissue models efficiently and accurately. We generated noiseless 256-channel EEG data from 15 subjects in the Connectome Young Adult dataset, using four anatomically relevant dipole positions, three conductivity sets, and two head segmentations; we mapped localization errors across the entire grey matter from 4,000 dipole positions. The average location error among our four selected dipoles is ∼5mm (±2mm) with an orientation error of ∼12∘ (±7∘). The average source localization error across the entire grey matter is ∼9mm (±4mm), with a tendency for smaller errors on the occipital lobe. Our findings indicate that while three-layer models are robust under noiseless conditions, substantial localization errors (10–20mm) are common. Therefore, models of five or more layers may be needed for accurate source reconstruction in critical applications involving noisy EEG data.

Time courses of brain plasticity underpinning visual motion perceptual learning

Visual perceptual learning (VPL) refers to a long-term improvement of visual task performance through training or experience, reflecting brain plasticity even in adults. In human subjects, VPL has been mostly studied using functional magnetic resonance imaging (fMRI). However, due to the low temporal resolution of fMRI, how VPL affects the time course of visual information processing is largely unknown. To address this issue, we trained human subjects to perform a visual motion direction discrimination task. Their behavioral performance and magnetoencephalography (MEG) signals responding to the motion stimuli were measured before, immediately after, and two weeks after training. Training induced a long-lasting behavioral improvement for the trained direction. Based on the MEG signals from occipital sensors, we found that, for the trained motion direction, VPL increased the motion direction decoding accuracy, reduced the motion direction decoding latency, enhanced the direction-selective channel response, and narrowed the tuning profile. Following the MEG source reconstruction, we showed that VPL enhanced the cortical response in early visual cortex (EVC) and strengthened the feedforward connection from EVC to V3A. These VPL-induced neural changes co-occurred in 160–230 ms after stimulus onset. Complementary to previous fMRI findings on VPL, this study provides a comprehensive description on the neural mechanisms of visual motion perceptual learning from a temporal perspective and reveals how VPL shapes the time course of visual motion processing in the adult human brain.

Distinct functions for beta and alpha bursts in gating of human working memory

Multiple neural mechanisms underlying gating to working memory have been proposed with divergent results obtained in human and animal studies. Previous findings from non-human primates suggest prefrontal beta frequency bursts as a correlate of transient inhibition during selective encoding. Human studies instead suggest a similar role for sensory alpha power fluctuations. To cast light on these discrepancies we employed a sequential working memory task with distractors for human participants. In particular, we examined their whole-brain electrophysiological activity in both alpha and beta bands with the same single-trial burst analysis earlier performed on non-human primates. Our results reconcile earlier findings by demonstrating that both alpha and beta bursts in humans correlate with the filtering and control of memory items, but with region and task-specific differences between the two rhythms. Occipital beta burst patterns were selectively modulated during the transition from sensory processing to memory retention whereas prefrontal and parietal beta bursts tracked sequence order and were proactively upregulated prior to upcoming target encoding. Occipital alpha bursts instead increased during the actual presentation of unwanted sensory stimuli. Source reconstruction additionally suggested the involvement of striatal and thalamic alpha and beta. Thus, specific whole-brain burst patterns correlate with different aspects of working memory control.

Analysis and computation of an inverse source problem for the biharmonic wave equation

Abstract In this paper, we study the inverse source problem for the biharmonic wave equation. Mathematically, we characterize the radiating and non-radiating sources at a fixed wavenumber. We show that a general source can be decomposed into a radiating source and a non-radiating source. The radiating source can be uniquely determined by Dirichlet boundary measurements at a fixed wavenumber. Moreover, we derive a Lipschitz stability estimate for determining the radiating source. On the other hand, the non-radiating source does not produce any scattered fields outside the support of the source function. Numerically, we propose a novel source reconstruction method based on the Fourier series expansion by multi-wavenumber boundary measurements. The stability of the proposed method is analyzed and numerical experiments are presented to verify the accuracy and efficiency.

Scalable quantum detector tomography by high-performance computing

Abstract At large scales, quantum systems may become advantageous over their classical counterparts at performing certain tasks. Developing tools to analyze these systems at the relevant scales, in a manner consistent with quantum mechanics, is therefore critical to benchmarking performance and characterizing their operation. While classical computational approaches cannot perform like-for-like computations of quantum systems beyond a certain scale, classical high-performance computing (HPC) may nevertheless be useful for precisely these characterization and certification tasks. By developing open-source customized algorithms using high-performance computing, we perform quantum tomography on a megascale quantum photonic detector covering a Hilbert space of 106. This requires finding 108 elements of the matrix corresponding to the positive operator valued measure (POVM), the quantum description of the detector, and is achieved in minutes of computation time. Moreover, by exploiting the structure of the problem, we achieve highly efficient parallel scaling, paving the way for quantum objects up to a system size of 1012 elements to be reconstructed using this method. In general, this shows that a consistent quantum mechanical description of quantum phenomena is applicable at everyday scales. More concretely, this enables the reconstruction of large-scale quantum sources, processes and detectors used in computation and sampling tasks, which may be necessary to prove their nonclassical character or quantum computational advantage.

A Kalman-filter based strategy for space-frequency force reconstruction

This contribution presents a Kalman filtering strategy for space-frequency reconstruction of mechanical sources. The proposed approach is based on the definition of an appropriate state-space model, where the state equation assumes that the force vector follows a random walk behavior, while the observation equation is the classical linear relationship between the force and the measured response through the transfer function matrix of the considered structure. One of the main challenges of the proposed approach is the fine-tuning of the covariance matrix associated with the process noise. In this work, it is estimated and adapted at each frequency during the filtering procedure. A numerical experiment is performed on a simply supported beam excited by a broadband point mechanical force to evaluate the reconstruction performance of the proposed approach. Further comparisons with other regularization strategies are also proposed to provide a fair overview of the results obtained.

Reconstruction of directional sources using physics-informed neural networks

In the context of augmented/virtual reality applications, replicating the directivity of sound sources is a critical step to ensure a high-quality immersive experience. However, accurate directivity measurements require complex experimental procedures involving a high number of microphones. In these scenarios, spatial interpolation allows to reconstruct the sound field with fewer sensors, which can be sparsely distributed around the source. The literature offers several interpolation schemes for sound field reconstruction, although they exhibit limited accuracy due to the lack of knowledge on the underlying physics. Recently, Physics-Informed Neural Networks (PINN) have been used to directly solve partial differential equations and thus better predict the evolution of dynamical systems. In this manuscript, we propose a spatial interpolation scheme based on PINN for the reconstruction of sound source directivity. PINN are used to solve the Helmholtz equation starting from acquisitions of the directivity over a limited set of sparsely distributed points. Results are compared with the outcomes of well-established methods based on Spherical Harmonics Decomposition (SH), Thin Plate Pseudo-Splines Interpolation (SPLI) and Piece-wise Linear Spherical Triangular Interpolation (TRI). PINN prove to provide better reconstruction of the directivity with respect to the proposed comparison methods, even when dealing with highly sparse sampling grids.

Assessment of planar array accuracy for 3D sound source reconstruction with Bayesian Focusing

As planar microphone arrays were initially designed for bidimensional sound source localisation, their use for three-dimensional acoustic imaging consistently suffers from a poor resolution along the normal axis. Such a statement is already well documented for beamforming approaches and their declensions. Bearing this in mind, this paper delves into the possibilities offered by inverse methods and especially iterative Bayesian Focusing to tackle this issue and deliver accurate source positions in three dimensions based on planar array measurements only. To this end, an experimental set up is designed: omnidirectional sources are mounted in a wooden mock-up featuring faces at different distances from an 81 MEMs planar array. Based on this measurements, a benchmark study of sound source reconstruction on a 3D mesh of the mock-up is conducted and compared to classical 2D acoustic maps set at different depths. The latter process is declined for various source correlation values and at extended frequency ranges. By doing so, the current paper delivers an overview of the ability to discriminate acoustic sources along the normal axis of a planar array, on a simple and well controlled test case, with a view to provide guidelines for further applications of 3D acoustic imaging on complex geometries.

Music literacy shapes the specialization of a right hemispheric word reading area

This study aimed to examine differences in the brain activity of professional musicians and non-musicians, particularly in relation to neuroplastic changes that may be associated with musical training. Specifically, we investigated whether the ability to read complex musical notation might be linked to neurofunctional adaptations that could influence word reading mechanisms. The study involved 80 participants (half of which were musicians). High-density EEG recordings and swLORETA inverse solutions were employed to analyze brain activity related to word processing and orthographic analysis. The electromagnetic signals were analyzed in the temporal window corresponding to the latency of N170 component (150–190 ms). Musicians and musically naïve people (controls) were matched based on native language, sociocultural and educational status, age, and laterality preference. Behavioural data and reading proficiency tests demonstrated higher reading skills (for words, non-words and text), and faster RTs to target letters embedded in words, in musicians. Source reconstruction showed fundamental differences in word reading mechanisms between musicians and non-musicians, including a larger involvement of the right occipitotemporal cortex, in the former than the latter. In particular, musicians showed a bilateral activation of the middle occipital gyrus (BA19, Visual Word Form Area), which was strongly lateralized to the left hemisphere in controls, during word orthographic analysis. A relationship is proposed between music literacy, enhanced reading skills and the development of a right-sided reading area for notation recognition in musicians, which could serve as a potential protective factor for ‘surface’ dyslexia.

Reconstruction of a Singular Source in a Fractional Subdiffusion Problem from a Single Point Measurement

Reconstruction of a Singular Source in a Fractional Subdiffusion Problem from a Single Point Measurement

Localized estimation of event-related neural source activity from simultaneous MEG-EEG with a recurrent neural network

Estimating intracranial current sources underlying the electromagnetic signals observed from extracranial sensors is a perennial challenge in non-invasive neuroimaging. Established solutions to this inverse problem treat time samples independently without considering the temporal dynamics of event-related brain processes.This paper describes current source estimation from simultaneously recorded magneto- and electro-encephalography (MEEG) using a recurrent neural network (RNN) that learns sequential relationships from neural data. The RNN was trained in two phases: (1) pre-training and (2) transfer learning with L1 regularization applied to the source estimation layer. Performance of using scaled labels derived from MEEG, magnetoencephalography (MEG), or electroencephalography (EEG) were compared, as were results from volumetric source space with free dipole orientation and surface source space with fixed dipole orientation. Exact low-resolution electromagnetic tomography (eLORETA) and mixed-norm L1/L2 (MxNE) source estimation methods were also applied to these data for comparison with the RNN method.The RNN approach outperformed other methods in terms of output signal-to-noise ratio, correlation and mean-squared error metrics evaluated against reference event-related field (ERF) and event-related potential (ERP) waveforms. Using MEEG labels with fixed-orientation surface sources produced the most consistent estimates.To estimate sources of ERF and ERP waveforms, the RNN generates temporal dynamics within its internal computational units, driven by sequential structure in neural data used as training labels. It thus provides a data-driven model of computational transformations from psychophysiological events into corresponding event-related neural signals, which is unique among MEEG source reconstruction solutions.

Frontal midline theta power during the cue-target-interval reflects increased cognitive effort in rewarded task-switching

Cognitive performance largely depends on how much effort is invested during task-execution. This also means that we rarely perform as good as we could. Cognitive effort is adjusted to the expected outcome of performance, meaning that it is driven by motivation. The results from recent studies suggest that the expenditure of cognitive control is particularly prone to being affected by modulations of cognitive effort. Although recent EEG studies investigated the neural underpinnings of the interaction of effort and control, reports on how cognitive effort is reflected by oscillatory activity of the EEG are quite sparse. It is the goal of the present study to bridge this gap by performing an exploratory analysis of high-density EEG data from a switching-task using manipulations of monetary incentives. A beamformer approach is used to localize the sensor-level effects in source-space. The results indicate that the manipulation of cognitive effort was successful. The participants reported significantly higher motivation and cognitive effort in high versus low reward trials. Performance was also significantly increased. The analysis of the EEG data revealed that the increase of cognitive effort was reflected by an increased mid-frontal theta activity during the cue-target interval, suggesting an increased use of proactive control. This interpretation is supported by the result from a regression analysis performed on single-trial data, showing higher mid-frontal theta power prior to target-onset being associated with faster responses. Alpha-desynchronization throughout the trial was also more pronounced in high reward trials, signaling a bias of attention towards the processing of external stimuli. Source reconstruction suggests that these effects are located in areas related to cognitive control, and visual processing.

The neural representation of self, close, and famous others: An electrophysiological investigation on the social brain

ABSTRACT It is well established that the Self has a unique representation in the social brain, as evident from the Self-Referential Effect (SRE). However, the timing and neural mechanisms underlying the representation of individuals with varying degrees of closeness and emotional relevance to the Self remain unclear. Twenty-two participants read 260 personality traits and decided whether they described themselves, a close friend, or an admired celebrity. A strong Self-Referential Effect (SRE) was found at behavioral, ERP, and neuroimaging levels. Three anterior ERP components were identified as sensitive to social information: a P200 (250–350 ms) responding to famous others’ traits, a P600 (500–700 ms) responding to self-trait processing, and a late positivity (800–950 ms) responding to self-trait processing and close traits. Source reconstructions revealed partially overlapping but distinct neural sources for each individual. The right precuneus (bodily self) and inferior frontal areas (inner voice) were active only during self-processing, while the right medial prefrontal cortex (BA10) was consistently active across tasks, showing a robust SRE. These findings provide insights into the neural mechanisms underlying the representation of the Self in social contexts.

Investigation on reconstruction of internal heat source in biological tissue based on multi-island genetic algorithm

With the rapid development of engineering thermophysics, researches on human biological heat transfer phenomena has gradually shifted from qualitative to quantitative. It is a typical inverse problem of heat conduction that deriving the distribution of internal heat sources from the temperature distribution on the body surface. Differing from traditional numerical methods for solving heat conduction, this paper transforms such an inverse problem of bio-heat transfer into a direct one, thereby avoiding complex boundary conditions and regularization processes. To noninvasively reconstruct the internal heat source and its corresponding 3D temperature field in biological tissue, the multi-island genetic algorithm (MIGA) is used in the simulation module, where the position P(x, y, z) of point heat source in biological tissue and its corresponding temperature T are set as the optimization variables. Under a certain optimized sample, one can obtain the simulated temperature distributing on the surface of the module, then subtract the simulated temperature from the measured temperature of the same surface which was measured using a thermal infrared imager. If the absolute value of the difference is smaller, it indicates that the current sample is closer to the true location and temperature of the heat source. When the values of optimization variables are determined, the corresponding 3D temperature field is also confirmed. The simulation results show the experimental and simulation temperature values of 15.5Ω resistor are 60.75°C and 62.15 °C respectively, with the error of 2.31 %, and those of 30.5Ω resistor are 84.40 °C and 86.33°C respectively, with the error of 2.29 %. The simulated positions are very approximate with those of the real experimental module. The method presented in this paper has enormous potential and promising prospects in clinical research and application, such as tumor hyperthermia, disease thermal diagnosis technology, etc.

Modulation of Posterior Default Mode Network Activity During Interoceptive Attention and Relation to Mindfulness

BackgroundInteroceptive attention to internal sensory signals, such as the breath, is fundamental to mindfulness. However, interoceptive attention can be difficult to study, with many studies relying on subjective and retrospective measures. Response consistency is an established method for evaluating variability of attention on exteroceptive attention tasks, but it has rarely been applied to interoceptive attention tasks. MethodsIn this study, we measured consistency of response times on a breath-monitoring task with simultaneous electroencephalography in individuals across the life span (15–91 years of age, N = 324). ResultsWe found that consistency on the breath-monitoring task was positively correlated with attentive performance on an exteroceptive inhibitory control task. Electroencephalography source reconstruction showed that on-task alpha band (8–12 Hz) activity was greater than that measured at rest. Low-consistency/longer breath responses were associated with elevated brain activity compared with high-consistency responses, particularly in posterior default mode network (pDMN) brain regions. pDMN activity was inversely linked with functional connectivity to the frontoparietal network and the cingulo-opercular network on task but not at rest, suggesting a role for these frontal networks in on-task regulation of pDMN activity. pDMN activity within the precuneus region was greater in participants who reported low subjective mindfulness and was adaptively modulated by task difficulty in an independent experiment. ConclusionsElevated pDMN alpha activity serves as an objective neural marker for low-consistency responding during interoceptive breath attention, scales with task difficulty, and is associated with low subjective mindfulness.

Prestimulus Alpha Phase Modulates Visual Temporal Integration.

When presented shortly after another, discrete pictures are naturally perceived as continuous. The neuronal mechanism underlying such continuous or discrete perception is not well understood. While continuous alpha oscillations are a candidate for orchestrating such neuronal mechanisms, recent evidence is mixed. In this study, we investigated the influence of prestimulus alpha oscillation on visual temporal perception. Specifically, we were interested in whether prestimulus alpha phase modulates neuronal and perceptual processes underlying discrete or continuous perception. Participants had to report the location of a missing object in a visual temporal integration task, while simultaneously MEG data were recorded. Using source reconstruction, we evaluated local phase effects by contrasting phase angle values between correctly and incorrectly integrated trials. Our results show a phase opposition cluster between -0.8 and -0.5 s (relative to stimulus presentation) and between 6 and 20 Hz. These momentary phase angle values were correlated with behavioral performance and event-related potential amplitude. There was no evidence that frequency defined a window of temporal integration.

MEG Microstates: An Investigation of Underlying Brain Sources and Potential Neurophysiological Processes

Microstates are transient scalp configurations of brain activity measured by electroencephalography (EEG). The application of microstate analysis in magnetoencephalography (MEG) data remains challenging. In one MEG dataset (N = 113), we aimed to identify MEG microstates at rest, explore their brain sources, and relate them to changes in brain activity during open-eyes (ROE) or closed-eyes resting state (RCE) and an auditory Mismatch Negativity (MMN) task. In another dataset of simultaneously recorded EEG-MEG data (N = 21), we investigated the association between MEG and EEG microstates. Six MEG microstates (mMS) provided the best clustering of resting-state activity, each linked to different brain sources: mMS 1–2: left/right occipito-parietal; mMS 3: fronto-temporal; mMS 4: centro-medial; mMS 5–6: left/right fronto-parietal. Increases in occipital alpha power in RCE relative to ROE correlated with greater mMS 1–2 time coverage (τbs < 0.20, ps > .002), while the lateralization of deviance detection in MMN was associated with mMS 5–6 time coverage (τbs < 0.16, ps > .012). No temporal correlation was found between EEG and MEG microstates (ps > .05), despite some overlap in brain sources and global explained variance between mMS 2–3 and EEG microstates B-C (rs > 0.60, ps < .002). Hence, the MEG signal can be decomposed into microstates, but mMS brain activity clustering captures phenomena different from EEG microstates. Source reconstruction and task-related modulations link mMS to large-scale networks and localized activities. Thus, mMSs offer insights into brain dynamics and task-specific processes, complementing EEG microstates in studying physiological and dysfunctional brain activity.

An Equivalent Radiation Source Reconstruction Method Based on Enhanced Artificial Neural Network

An Equivalent Radiation Source Reconstruction Method Based on Enhanced Artificial Neural Network