Multidimensional Techniques Research Articles

Studying Saudi English as a New Variety: A Multi-variate Analysis

Aim. This multi-dimensional analysis explores prominent lexico-grammatical features in English used in the Kingdom of Saudi Arabia (KSA). The research focuses on the linguistic variation in editorials in Saudi English newspapers. The study’s goals were to create a specialised corpus of Saudi English, to determine how Saudi English differs from British English, and to establish Saudi English as an independent form of English, allowing syllabi designers to serve ESP course developers better. Method. Douglas Biber's multi-dimensional analysis (2006), a corpus-based, quantitative, empirical, and comparative technique for studying linguistic variety, was used by the researchers for analysing this language diversity. Quantitative and functional techniques were employed in the research. The authors examined and statistically analysed the acquired data to establish the communicative function of press editorials. Biber’s (2006) tagger was used to tag the data. Then, using the analysis of variance (ANOVA), the linguistic differences between Saudi publications and British newspapers were determined. Results. The results indicate that Saudi Newspaper Editorial (SNE) is more informational, explicit, abstract, and less argumentative than British Newspaper Editorial (BNE). On one dimension, SNE produces non-narrative while BNE produces narrative discourse. Except for D2, the findings of the internal comparison reveal that there are minor variations in the mean score of Saudi newspaper editorials. Saudi Gazette creates a non-narrative discourse on D2, whilst Arab News produces a narrative one. Cognitive value. The findings support the claim that English spoken in Saudi Arabia qualifies as a distinct variation of English.

Carbohydrates as proxies in ombrotrophic peatland: DFRC molecular method coupled with PCA

Understanding organic matter (OM) dynamics in peat remains challenging as molecular-level carbohydrate studies of organic geochemistry are still generally rare. Herein, neutral monosaccharides derived via the newly developed derivatisation followed by reductive cleavage (DFRC) method were investigated for an ombrotrophic peatland in France for assessing their potential application as proxies in peat OM dynamics.The concentrations of major neutral monosaccharides, namely, glucose, xylose, arabinose, galactose, mannose, rhamnose (Rha) and fucose (Fuc), were determined via gas chromatography for 24 depth records collected from an ombrotrophic peatland in Sagnes, France. Depth records were distributed along the ecological layers of a peatland, i.e. acrotelm, mesotelm and catotelm. Mechanistic features of DFRC were employed to develop a new proxy ‘∆’ to decipher the lignocellulosic input in recent sediments. Additionally, principal component analysis (PCA) was applied to the molecular monosaccharide composition. This combination of the multi-dimensional statistical technique and the molecular fingerprint yielded a better understanding of the carbohydrate geochemistry along an ombrotrophic peatland. In case of DFRC, PCA was used to separately cluster deoxyhexoses (Rha and Fuc) from Rib. A comparison of these two groups of monosaccharides with a vegetation indicator (C/N) and microbial indicator (total amount of phospholipids) confirmed that the discrepancy obtained using PCA is due to the fact that deoxyhexoses are more likely a vegetation indicator and ribose is more likely a microbial indicator.

Optical trapping and manipulation of nanowires using multi-hotspot dielectric nanononamers

Semiconductor nanowires have demonstrated great potential in all-photonic integrated circuit applications. However, the development of a controllable multidimensional nanowire assembly technique is still arguably in its infancy. Here, we numerically demonstrate the optical trapping and manipulation of cylindrical zinc oxide nanowires using an all-dielectric silicon nanononamer for designing programmable nanolasers. The nanononamer is composed of nine identical silicon nanocylinders arranged in a square grid on top of a glass substrate. This is a suitable choice, as optical trapping with the proposed silicon nanononamer is envisioned as an effective technique for the contactless manipulation of suspended nanowires with multiple hotspots and with negligible heating generation. We determine optical forces and torques applied to nanowires using the Maxwell stress tensor method. We investigate the influence of light polarization on the field confining and laser tweezing properties. For this work, the simple nanowire-based silicon photonic platform is compatible with the complementary metal–oxide–semiconductor technology, which allows low-cost fabrication of such structures and the integration with other on-chip optical components.

Using machine learning and an electronic tongue for discriminating saliva samples from oral cavity cancer patients and healthy individuals.

The diagnosis of cancer and other diseases using data from non-specific sensors - such as the electronic tongues (e-tongues) - is challenging owing to the lack of selectivity, in addition to the variability of biological samples. In this study, we demonstrate that impedance data obtained with an e-tongue in saliva samples can be used to diagnose cancer in the mouth. Data taken with a single-response microfluidic e-tongue applied to the saliva of 27 individuals were treated with multidimensional projection techniques and non-supervised and supervised machine learning algorithms. The distinction between healthy individuals and patients with cancer on the floor of mouth or oral cavity could only be made with supervised learning. Accuracy above 80% was obtained for the binary classification (YES or NO for cancer) using a Support Vector Machine (SVM) with radial basis function kernel and Random Forest. In the classification considering the type of cancer, the accuracy dropped to ca. 70%. The accuracy tended to increase when clinical information such as alcohol consumption was used in conjunction with the e-tongue data. With the random forest algorithm, the rules to explain the diagnosis could be identified using the concept of Multidimensional Calibration Space. Since the training of the machine learning algorithms is believed to be more efficient when the data of a larger number of patients are employed, the approach presented here is promising for computer-assisted diagnosis.

Distinct Injury Responsive Regulatory T Cells Identified by Multi-Dimensional Phenotyping.

CD4+ regulatory T cells (Tregs) activate and expand in response to different types of injuries, suggesting that they play a critical role in controlling the immune response to tissue and cell damage. This project used multi-dimensional profiling techniques to comprehensively characterize injury responsive Tregs in mice. We show that CD44high Tregs expand in response to injury and were highly suppressive when compared to CD44low Tregs. T cell receptor (TCR) repertoire analysis revealed that the CD44high Treg population undergo TCRαβ clonal expansion as well as increased TCR CDR3 diversity. Bulk RNA sequencing and single-cell RNA sequencing with paired TCR clonotype analysis identified unique differences between CD44high and CD44low Tregs and specific upregulation of genes in Tregs with expanded TCR clonotypes. Gene ontology analysis for molecular function of RNA sequencing data identified chemokine receptors and cell division as the most enriched functional terms in CD44high Tregs versus CD44low Tregs. Mass cytometry (CyTOF) analysis of Tregs from injured and uninjured mice verified protein expression of these genes on CD44high Tregs, with injury-induced increases in Helios, Galectin-3 and PYCARD expression. Taken together, these data indicate that injury triggers the expansion of a highly suppressive CD44high Treg population that is transcriptionally and phenotypically distinct from CD44low Tregs suggesting that they actively participate in controlling immune responses to injury and tissue damage.

Simultaneous determination of eight components in Amomum villosum and its overall qualityconsistency evaluation by four-dimensional fingerprints assisted with antioxidant activity

Amomum villosum (AMV), as a 1300-year-old medicine and food homology, has been lack of comprehensive quality evaluation for a long time. This study established the fingerprints of AMV by chromatographic, spectroscopic and thermal technologies and investigated the antioxidant activity to assess holistically the quality of 22 batches of AMV samples. GC-HPLC tandem fingerprints, FTIR and DSC quantum fingerprints were first developed respectively and fused for evaluating qualitatively and quantitatively, in which the contents of eight compounds were determined simultaneously. Especially, the equal-weight method was used to fuse the different analytical techniques mentioned above and to evaluate the quality of AMV samples with the help of systematically quantified fingerprint method. As a result, all samples were well differentiated and classified into five grades. Moreover, the correlation between the chromatographic fingerprint peaks and antioxidant activity was confirmed by the gray relational analysis. In summary, the proper fusion of multidimensional fingerprint techniques and multidimensional analytical techniques provides a reliable and comprehensive method for the evaluation of AMV and their related products.

Perspectives, Challenges and Opportunities of E-Learning in Higher Education – A Brief Review

Purpose: In this developed world education plays an important role at all stages of an individual. From last one decade the education has been grown and advanced in the form of E-learnings process. The advancement and usage of e-learnings is varied from developing to developed countries. Today in this internet generation, the individuals including teachers and students are connected with various e-learning modes via worldwide internet connection, which makes e-learning process necessary in the field of learning and education. E-learning provides the formal and informal learning atmosphere which encourages the students and learners with live interaction. The recent studies also shows that the e-learning improves the self-assessment, teaching performance and participation of the students. This paper provides the descriptive review of different-learning methodology used in various applications. Methodology: This comprehensive literature analysis includes more than 50 high-quality review articles, research papers, and case studies on e-learning and online education systems. The study takes into account both the teacher's and students' perspectives on the use of e-learning techniques. Findings/Result: The idea behind empirical methods in e-learning was to better comprehend the complicated approach that is employed when it comes to teaching methodology over the internet. Many ways have been created to make this process competent, including on-the-job learning, in-class assignments, and team-based learning. Originality: The e-learning process is still a multi-dimensional and sophisticated technique in terms of study. Although technical advancements have seized this field, research and regular case studies are still in short supply. It is necessary to comprehend topics such as the infrastructure utilised to produce technology, pedagogy, admirative supports, and social usage in order to implement the e-learning process. Paper Type: Literature Review.

RF Fingerprint Extraction Method Based on CEEMDAN and Multidomain Joint Entropy

Specific emitter identification (SEI) can distinguish communication radio emitters with the fingerprint features carried by the received signal, and this technology has been widely used in military and civilian fields. However, in the real electromagnetic environment, the number of communication radio emitters is large and the signal-to-noise ratio (SNR) is low, which leads to poor nonlinear fingerprint analysis of SEI in a single domain. Therefore, combining the exploration of multiple domains of electromagnetic spatial information resources, this paper proposed a radio frequency (RF) fingerprint extraction method based on complementary ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and multidomain joint entropy. The proposed method is an attempt and exploration further extraction of nonlinear fingerprint features in multiple domains. Firstly, considering the nonstationarity of the communication signal, this article adopts the CEEMDAN method to decompose the signal to multiple intrinsic mode functions (IMF). Then, the decomposed signal is represented in multiple spaces by a multidimensional phase space reconstruction technique. Nonlinear analysis of the original signal is performed in multiple spaces: multidimensional differential approximate entropy space, singular spectral entropy space, and power spectral entropy space. Finally, the support vector machine (SVM) is adopted in the classification stage. To demonstrate the robustness of the method, the method is verified on the universal software radio peripheral (USRP) dataset and the Northeastern University public dataset. In terms of the identification accuracy, the proposed method performs with 98.5% accuracy on the 5-class USRP dataset. It also performs with 94.7% accuracy on the 16-class public dataset. The experimental results show that the proposed method has a stable identification performance and has a more than 85% recognition rate in the SNR above 5dB.

Acoustic Descriptors for Characterization of Musical Timbre Using the Fast Fourier Transform

The quantitative assessment of the musical timbre in an audio record is still an open-ended issue. Evaluating the musical timbre allows not only to establish precise musical parameters but also the recognition, classification of musical instruments, and assessment of the musical quality of a sound record. In this paper, we present a minimum set of dimensionless descriptors, motivated by musical acoustics, using the spectra obtained by the Fast Fourier Transform (FFT), which allows describing the timbre of wooden aerophones (Bassoon, Clarinet, Transverse Flute, and Oboe) using individual sound recordings of the musical tempered scale. We postulate that the proposed descriptors are sufficient to describe the timbral characteristics in the aerophones studied, allowing their recognition using the acoustic spectral signature. We believe that this approach can be further extended to use multidimensional unsupervised machine learning techniques, such as clustering, to obtain new insights into timbre characterization.

Integral representation method based efficient rule optimizing framework for anti-money laundering

PurposeThis paper aims to introduce a framework for optimizing rule-based anti-money laundering systems with a clear economic interpretation, and the authors introduce the integral representation method.Design/methodology/approachBy using a microeconomic model, the authors reformulate the threshold optimization problem as a decision problem to gain insights from economics regarding the main properties of the optimum. The authors used algorithmic considerations to find an efficient implementation by using a kind of weak mode estimate of the distribution and the authors extend this approach to classes of alerts or cases.FindingsThe method provides a new and efficient alternative for the sampling method or the multidimensional optimization technique described in the literature to decrease the bias emanating from multiple alerts by smoothing the number of alerts across classes in the optimum and decrease the overlapping between scenarios at the case level. Using the method for real bank data, the authors were able to decrease the number of false positives cases by about 18% while retaining almost 98% of the true-positive cases.Research limitations/implicationsThe model assumes that alerts from different scenarios are indifferent to the bank. To include scenario-specific preferences or constraints demands further research.Originality/valueThe new framework presented in the paper is a flexible extension of the usual above-the-line method, which makes it possible to include bank preferences and use the parallelization capabilities of modern processors.

Targeted glucocorticoid analysis using ion mobility-mass spectrometry (IM-MS)

IntroductionIon mobility-mass spectrometry (IM-MS) is an emerging technique in the -omics fields that has broad potential applicability to the clinical lab. As a rapid, gas-phase structure-based separation technique, IM-MS offers promise in isomer separations and can be easily combined with existing LC-MS methods (i.e., LC-IM-MS). Several experimental conditions, including analyte cation adducts and drift composition further provide a means to tune separations for global and/or targeted applications. ObjectivesThe primary objective of this study was to demonstrate the utility of IM-MS under a range of experimental conditions for detection of glucocorticoids, and specifically for the separation of several isomeric pairs. MethodsLC-IM-MS was used to characterize 16 glucocorticoids including three isomer pairs: cortisone/prednisolone, betamethasone/dexamethasone, and flunisolide/triamcinolone acetonide. Collision cross section (CCS) values were measured for all common adducts (e.g., protonated and sodiated) using both step-field and single-field methods. Alternative alkali, alkaline earth, and transition metals were introduced, such that their adducts could also be measured. Finally, four different drift gases (helium, nitrogen, argon, and carbon dioxide) were compared for their relative separation capability. ResultsLC-IM-MS offered a robust, multidimensional separation technique that allowed for the 16 glucocorticoids to be analyzed and separated in three-dimensions (retention time, CCS, and m/z). Despite the relatively modest resolution of isomer pairs under standard conditions (i.e., nitrogen drift gas, sodiated ions, etc.), improvements were observed for alkaline earth and transition metals (notable barium adducts) and in carbon dioxide drift gas. ConclusionIn summary, LC-IM-MS offers potential as a clinical method due to its ease of coupling with traditional LC-MS methods and its promise for tuning separations to better resolve targeted and/or global isomers in complex biological samples.

D-SEAD: A Novel Multi-Access Multi-Dimensional Transmission Technique for Doubling the Spectral Efficiency per Area and per Device

The authors propose a technique that is capable of doubling the spectral efficiency per area and per device by modulating a recently developed multiple access design called multi-user auxiliary signal superposition transmission through a transmission technique called OFDM-SPM

Disentangling the effects of restriction and exchange with diffusion exchange spectroscopy.

Diffusion exchange spectroscopy (DEXSY) is a multidimensional NMR technique that can reveal how water molecules exchange between compartments within heterogeneous media, such as biological tissue. Data from DEXSY experiments is typically processed using numerical inverse Laplace transforms (ILTs) to produce a diffusion-diffusion spectrum. A tacit assumption of this ILT approach is that the signal behavior is Gaussian - i.e., the spin echo intensity decays exponentially with the degree of diffusion weighting. The assumptions that underlie Gaussian signal behavior may be violated, however, depending on the gradient strength applied and the sample under study. We argue that non-Gaussian signal behavior due to restrictions is to be expected in the study of biological tissue using diffusion NMR. Further, we argue that this signal behavior can produce confounding features in the diffusion-diffusion spectra obtained from numerical ILTs of DEXSY data - entangling the effects of restriction and exchange. Specifically, restricted signal behavior can result in broadening of peaks and in the appearance of illusory exchanging compartments with distributed diffusivities, which pearl into multiple peaks if not highly regularized. We demonstrate these effects on simulated data. That said, we suggest the use of features in the signal acquisition domain that can be used to rapidly probe exchange without employing an ILT. We also propose a means to characterize the non-Gaussian signal behavior due to restrictions within a sample using DEXSY measurements with a near zero mixing time or storage interval. We propose a combined acquisition scheme to independently characterize restriction and exchange with various DEXSY measurements, which we term Restriction and Exchange from Equally-weighted Double and Single Diffusion Encodings (REEDS-DE). We test this method on ex vivo neonatal mouse spinal cord - a sample consisting primarily of gray matter - using a low-field, static gradient NMR system. In sum, we highlight critical shortcomings of prevailing DEXSY analysis methods that conflate the effects of restriction and exchange, and suggest a viable experimental approach to disentangle them.

Immunoassay platform with surface-enhanced resonance Raman scattering for detecting trace levels of SARS-CoV-2 spike protein

The early diagnosis of Coronavirus disease (COVID-19) requires either an accurate detection of genetic material or a sensitive detection of viral proteins. In this work, we designed an immunoassay platform for detecting trace levels of SARS-CoV-2 spike (S) protein. It is based on surface-enhanced resonance Raman scattering (SERRS) of methylene blue (MB) adsorbed onto spherical gold nanoparticles (AuNPs) and coated with a 6 nm silica shell. The latter shell in the SERRS nanoprobe prevented aggregation and permitted functionalization with SARS-CoV-2 antibodies. Specificity of the immunoassay was achieved by combining this functionalization with antibody immobilization on the cover slides that served as the platform support. Different concentrations of SARS-CoV-2 antigen could be distinguished and the lack of influence of interferents was confirmed by treating SERRS data with the multidimensional projection technique Sammon's mapping. With SERRS using a laser line at 633 nm, the lowest concentration of spike protein detected was 10 pg/mL, achieving a limit of detection (LOD) of 0.046 ng/mL (0.60 pM). This value is comparable to the lowest concentrations in the plasma of COVID-19 patients at the onset of symptoms, thus indicating that the SERRS immunoassay platform may be employed for early diagnosis.

Golden-Free Hardware Trojan Detection Using Self-Referencing

The globalization of the semiconductor supply chain has developed a new set of challenges for security researchers. Among them, malicious alterations of hardware designs at an untrusted facility, or Trojan insertion, are considered one of the most difficult challenges. While side-channel analysis-based hardware Trojan detection techniques have shown great potential, most solutions, proposed over the past decade, require the availability of golden (i.e., Trojan-free) chips and are susceptible to process variations. Few techniques that do not require a golden chip depend on simulation-based modeling of the side-channel signature, which may not be reliable for differentiating between process and Trojan induced variations. Furthermore, most of these techniques are evaluated either using very few Trojan inserted chips or simulation-based test setup. Spatial and temporal self-referencing-based detection mechanisms proposed earlier effectively eliminate the need for a golden chip and the impact of process variations. However, these techniques have not been adequately studied to achieve high detection sensitivity. In this article, we propose a golden-free multidimensional self-referencing technique that analyzes the side-channel signatures in both the time and frequency domains to significantly broaden the Trojan coverage and strengthen the detection confidence. We introduce a fully automated detection framework containing systematic methodologies for test generation, signature extraction, signal processing, threshold calculation, and metric-based decision-making that effectively enables the synergistic self-referencing approach. Finally, we evaluate the proposed technique through a comprehensive hardware measurement setup consisting of 96 Trojan-inserted test chips. Along with achieving a high detection coverage, we demonstrate that the analysis of spatial and temporal discrepancies in both frequency and time domains helps to reliably detect small hard-to-detect Trojans under process and measurement induced variations.

Investigation of Chloroquine Resinate Feasibility and In Vitro Taste Masking Evaluation for Pediatric Formulations.

In this study, chloroquine resinates were prepared at a 1:1 (w:w) drug-to-resin ratio using the batch method with polacrilex (PC), sodium polystyrene sulfonate (SPS), and polacrilin potassium (PP) ion exchange resins (IER). The influence of drug/resin ratio and pH of the medium on drug loading efficiency was explored. UV-VIS spectrophotometric analysis showed that SPS resin had high loading efficiency for chloroquine diphosphate (CLP), above 89%, regardless of the pH. PP resin was more effective at pH 5.0 (90.68%) than at pH 1.0 (2.09%), and PC resin had only 27.63% of CLP loading efficiency. CLP complexation with IER yielded amorphous mixtures according to results from differential scanning calorimetry (DSC) and X-ray powder diffraction (XRPD), thus indicating drug-resin interaction. The taste masking efficiency was evaluated with in vitro methods using an adapted dissolution test and an electronic tongue system. During dissolution tests, SPS released only 1.0% of CLP after 300 s, while PP released over 10% after 90 s in simulated saliva solution. The electronic tongue distinguished the samples containing CLP, resins, and resinates by using multidimensional projection techniques that indicated an effective drug taste masking. In an accelerated stability study, the drug contents did not decrease in chloroquine resinates, and there was no physical degradation of the resinates after 60 days. Using chloroquine resinates therefore represents a novel way to evaluate taste masking in vitro which is relevant for the early formulation development process.

Multidimensional Imaging Reveals Mechanisms Controlling Multimodal Label-Free Biosensing in Vertical 2DM-Heterostructures.

Two-dimensional materials and their van der Waals heterostructures enable a large range of applications, including label-free biosensing. Lattice mismatch and work function difference in the heterostructure material result in strain and charge transfer, often varying at a nanometer scale, that influence device performance. In this work, a multidimensional optical imaging technique is developed in order to map subdiffractional distributions for doping and strain and understand the role of those for modulation of the electronic properties of the material. As an example, vertical heterostructures comprised of monolayer graphene and single-layer flakes of transition metal dichalcogenide MoS2 were fabricated and used for biosensing. Herein, the optical label-free detection of doxorubicin, a common cancer drug, is reported via three independent optical detection channels (photoluminescence shift, Raman shift, and graphene enhanced Raman scattering). Non-uniform broadening of components of multimodal signal correlates with the statistical distribution of local optical properties of the heterostructure. Multidimensional nanoscale imaging allows one to reveal the physical origin for such a local response and propose the best strategy for the mitigation of materials variability and future device fabrication, enabling multiplexed biosensing.

Setup-Independent Sensing Architecture With Multiple UHF RFID Sensor Tags

Ultra High Frequency Radio Frequency Identification (UHF RFID)-based sensing technology with antenna-integrated sensor tags offers a cost-effective solution for Internet of Things (IoT) applications. A multi-dimensional differential measurement technique for eliminating the effect of measurement setup, such as unknown mutual distance and orientation between the read/write device (RWD) and sensor tags, and line-of-sight obstruction, on acquiring information from multiple sensor tags simultaneously is proposed. The concept is based on 1) using a channel hopping mechanism to acquire multi-dimensional datasets and 2) conducting a dissimilarity analysis between offline and online datasets to extract sensor information. The differential measurement, which is activated by the channel hopping mechanism, mitigates the impact of the measurement setup, making the pattern of the multi-dimensional datasets tightly relate to the sensing state. A sensor tag for measuring the water filling level of polyvinyl chloride (PVC) pipes is designed and fabricated. Experiments show that the proposed method can accurately acquire information from three sensor tags simultaneously under different measurement setups. The performances using datasets of different dimensions are compared. Results reveal that using low-dimensional datasets can still be capable of getting accurate information from the sensor tags, implying that the acquisition time can be reduced.

Data Analysis of Coronavirus CoVID-19: Study of Spread and Vaccination in European Countries

Humanity has gone since a long time through several pandemics, such as: H1N1 in 2009 and also Spanish flu in 1917. In December 2019, the health authorities of China detected unexplained cases of pneumonia. The WHO World Health Organization has declared the apparition of CoVID-19 (novel Coronavirus) that caused a global pandemic in 2020. In data analysis, multiple approaches and diverse techniques were used to extract useful information from multiple heterogeneous sources and to discover knowledge and new information for decision-making; it is used in different business and science domains. In this context, we propose to use the multidimensional analysis techniques based on two concepts: fact (subject of analysis) and dimensions (axes of analyses). This technique allows decision makers to observe data from various heterogeneous sources and analyze them according several viewpoints or perspectives. More precisely, we propose a multidimensional model for analyzing the Coronavirus CoVID-19 data (spread and vaccination in European countries). This model is based on constellation schema that contains several facts surrounded by common dimensions.

Real-Time Symptom Capture of Hallucinations in Schizophrenia with fMRI: Absence of Duration-Dependent Activity.

While advances in the field of functional magnetic resonance imaging (fMRI) provide new opportunities to study brain networks underlying the experience of hallucinations in psychosis, there are methodological challenges unique to symptom-capture studies. We extracted brain networks activated during hallucination-capture for schizophrenia patients when fMRI data collected from two sites was merged (combined N = 27). A multidimensional analysis technique was applied, which would allow separation of brain networks involved in the hallucinatory experience itself from those involved in the motor response of indicating the beginning and end of the perceived hallucinatory experience. To avoid reverse inference when attributing a function (e.g., a hallucination) to anatomical regions, it was required that longer hallucinatory experiences produce extended brain responses relative to shorter. For radio-speech sound files, an auditory perception brain network emerged, and displayed speech-duration-dependent hemodynamic responses (HDRs). However, in the hallucination-capture blocks, no network showed hallucination-duration-dependent HDRs, but a retrieved network that was anatomically classified as motor response emerged. During symptom capture of hallucinations during fMRI, no HDR showed duration dependence, but a brain network anatomically matching the motor response network was retrieved. Previous reports on brain networks detected by fMRI during hallucination capture are reviewed in this context; namely, that the brain networks interpreted as involved in hallucinations may in fact be involved only in the motor response indicating the onset of the hallucination.