Detection Of Signal Changes Research Articles

A Statistical Framework for Analysis of Trial-Level Temporal Dynamics in Fiber Photometry Experiments.

Fiber photometry has become a popular technique to measure neural activity in vivo, but common analysis strategies can reduce detection of effects because they condense within-trial signals into summary measures, and discard trial-level information by averaging across-trials . We propose a novel photometry statistical framework based on functional linear mixed modeling, which enables hypothesis testing of variable effects at every trial time-point , and uses trial-level signals without averaging. This makes it possible to compare the timing and magnitude of signals across conditions while accounting for between-animal differences. Our framework produces a series of plots that illustrate covariate effect estimates and statistical significance at each trial time-point. By exploiting signal autocorrelation, our methodology yields joint 95% confidence intervals that account for inspecting effects across the entire trial and improve the detection of event-related signal changes over common multiple comparisons correction strategies. We reanalyze data from a recent study proposing a theory for the role of mesolimbic dopamine in reward learning, and show the capability of our framework to reveal significant effects obscured by standard analysis approaches. For example, our method identifies two dopamine components with distinct temporal dynamics in response to reward delivery. In simulation experiments, our methodology yields improved statistical power over common analysis approaches. Finally, we provide an open-source package and analysis guide for applying our framework.

A third-order plate model with surface effect based on the Gurtin–Murdoch surface elasticity

Thin films with micro/nano scale thickness possessing ultra-high specific surface area have been widely used in opto-, thermo-, and electro-mechanical actuators for ultra-sensitive detection of physical signal change and few molecular absorptions. However, there still lacks a rigorous plate model capable of describing the large deflection and, in the meanwhile, the complex surface effects of those films, especially the coupling between surface shear stress and bulk shear deformation. In this paper, via the satisfaction of the surface balance relation in the Gurtin–Murdoch theory, we refine Reddy’s third-order plate theory to accurately characterize the size-dependent deformation and uncover the influences of the surface layer on the second and third order shear deformations of micro/nano plates. It is revealed that the modified displacement field of our plate model can engender changes in local behaviors including the through-thickness distributions of bulk shear strain and shear stress, which bring about the change in global stiffness and stability of the plates, especially those with smaller length-to-thickness ratio. When the magnitude of residual surface stress increases, the stiffness and stability of the plate are enhanced, due to the amplification of bulk shear stress. Our model also indicates that miniature structures do not share entirely the same displacement field with macroscopic ones, and the method of deriving size-dependent displacement field incorporating surface effect can be extended to more complex cases. This work provides a strategy for tuning the static and dynamic behaviors of thin film devices by modifying the surface elastic constants and residual stress.

지속성 수술 중 신경감시의 유용성

Intraoperative neuromonitoring during thyroid surgery has been popularized recently. The majority of this technique represented the direct stimulation to the recurrent laryngeal nerve, so called intermittent intraoperative neuromonitoring (I-IONM). However, continuous monitoring via vagus nerve (C-IONM) was considered more ideal, and has been reported to have more advantage than I-IONM because C-IONM allows the real-time detection of signal changes and prevents the nerve injury before the event. Here, the author reviewed the usefulness of C-IONM.

The Overlap Index: A new means for early detection of serial tau PET signal change

AbstractBackgroundIn longitudinal tau (flortaucipir) positron emission tomography (PET), a reliable method to detect a tau accumulation while minimizing a random variability is crucial. Variability can be a problem especially in the early stages of tau pathology in which only small regions and low levels of accumulation may exist relative to the range of random noise fluctuation.MethodWe recruited 76 participants who had serial scans for a total of 234 tau PET scans. In this cohort we tested a PET signal measurement method we developed called the “overlap index” (OI). The OI was calculated by identifying voxels that have elevated tau PET signal that overlap on two successive tau PET scans in the same participant (Figure 1). This analysis procedure tests the hypothesis that voxel‐wise, elevated‐uptake consistency might be more sensitive for early detection of tau tangles than standard ROI based measures. We compared OI to ROI based measures for early detection of increasing tau PET signal and detection clinical progression. A meta ROI used in this analysis included the amygdala, entorhinal cortex, fusiform, parahippocampal, and inferior temporal and middle temporal gyri.ResultOI showed regional progression of tau in the temporal lobe followed by increases throughout the brain at relatively low standardized uptake value ratio (SUVr) levels (Figure 2A and B). The topographical pattern of OI was in agreement in some respects with the sequential appearance of tau aggregation (Figure 2B and C). The high‐OI group (OI>0.5) had a significantly higher ΔSUVr compared to the low‐OI group (OI<0.5) (Figure 3A and B). OI identified subjects that did or didn’t have tau accumulation within meta SUVr subgroups even when below the meta ROI SUVr threshold. OI was associated with early clinical disease stage progression vs meta‐ROI SUVr measures (Figure 3C‐E).ConclusionIn this study, we found that OI tau PET measurement was able to quantify voxel‐wise consistency using a set of serial scans. OI may detect earlier tau tangle accumulation and earlier disease progression than standard SUVr measurements in tau PET.

Lowering the thermal noise barrier in functional brain mapping with magnetic resonance imaging

Functional magnetic resonance imaging (fMRI) has become an indispensable tool for investigating the human brain. However, the inherently poor signal-to-noise-ratio (SNR) of the fMRI measurement represents a major barrier to expanding its spatiotemporal scale as well as its utility and ultimate impact. Here we introduce a denoising technique that selectively suppresses the thermal noise contribution to the fMRI experiment. Using 7-Tesla, high-resolution human brain data, we demonstrate improvements in key metrics of functional mapping (temporal-SNR, the detection and reproducibility of stimulus-induced signal changes, and accuracy of functional maps) while leaving the amplitude of the stimulus-induced signal changes, spatial precision, and functional point-spread-function unaltered. We demonstrate that the method enables the acquisition of ultrahigh resolution (0.5 mm isotropic) functional maps but is also equally beneficial for a large variety of fMRI applications, including supra-millimeter resolution 3- and 7-Tesla data obtained over different cortical regions with different stimulation/task paradigms and acquisition strategies.

Confidence Sets for Cohen’s d effect size images

Current statistical inference methods for task-fMRI suffer from two fundamental limitations. First, the focus is solely on detection of non-zero signal or signal change, a problem that is exacerbated for large scale studies (e.g. UK Biobank, N=40,000+) where the ‘null hypothesis fallacy’ causes even trivial effects to be determined as significant. Second, for any sample size, widely used cluster inference methods only indicate regions where a null hypothesis can be rejected, without providing any notion of spatial uncertainty about the activation. In this work, we address these issues by developing spatial Confidence Sets (CSs) on clusters found in thresholded Cohen’s d effect size images. We produce an upper and lower CS to make confidence statements about brain regions where Cohen’s d effect sizes have exceeded and fallen short of a non-zero threshold, respectively. The CSs convey information about the magnitude and reliability of effect sizes that is usually given separately in a t-statistic and effect estimate map. We expand the theory developed in our previous work on CSs for %BOLD change effect maps (Bowring et al., 2019) using recent results from the bootstrapping literature. By assessing the empirical coverage with 2D and 3D Monte Carlo simulations resembling fMRI data, we find our method is accurate in sample sizes as low as N=60. We compute Cohen’s d CSs for the Human Connectome Project working memory task-fMRI data, illustrating the brain regions with a reliable Cohen’s d response for a given threshold. By comparing the CSs with results obtained from a traditional statistical voxelwise inference, we highlight the improvement in activation localization that can be gained with the Confidence Sets.

Task-based fMRI of a free-viewing visuo-saccadic network in the marmoset monkey

Saccadic tasks are often used to index aberrations of cognitive function in patient populations, with several neuropsychiatric and neurologic disorders characterized by saccadic dysfunction. The common marmoset (Callithrix jacchus) has received recent attention as an additional primate model for studying the neural basis of these dysfunctions – marmosets are amenable to a host of genetic manipulation techniques and have a lissencephalic cortex, which is well suited for a variety of recording techniques (e.g., calcium imaging, laminar electrophysiology). Because the marmoset cortex is mostly lissencephalic, however, the locations of frontal saccade-related regions (e.g., frontal eye fields (FEF)) are less readily identified than in Old World macaque monkeys. Further, although high quality histology-based atlases do exist for marmosets, identifying these regions based on histology alone is not always accurate, with the cytoarchitectonic boundaries often inconsonant with functional boundaries. As such, there is a need to map the functional location of these regions directly. Task-based functional magnetic resonance imaging (fMRI) is of utility in this regard, allowing for detection of whole-brain signal changes in response to moving stimuli. Here, we conducted task-based fMRI in marmosets at ultra-high field (9.4 T) during a free-viewing visuo-saccadic task. We also conducted the same task in humans at ultra-high field (7 T) to validate that our simple task was indeed evoking the visuo-saccadic circuitry we expected (as defined by a meta-analysis of fMRI saccade studies). In the marmosets, we found that the task evoked a robust visuo-saccadic topology, with visual cortex (V1, V2, V3, V4) activation extending ventrally to MT, MST, FST and dorsally into V6, 19M, 23V. This topology also included putative cingulate eye field (area 32 and 24d), posterior parietal cortex (with strongest activation in lateral intraparietal area (LIP)), and a frontolateral peak in area 8 aV in marmosets, extending into 45, 46, 8aD, 6DR, 8c, 6 aV, 6DC. Overall, these results support the view that marmosets are a promising preclinical modelling species for studying saccadic dysfunction related to neuropsychiatric or neurodegenerative human brain diseases.

Statistical Learning of Discrete States in Time Series.

Time series obtained from time-dependent experiments contain rich information of kinetics and dynamics of the system under investigation. This work describes an unsupervised learning framework, along with the derivation of the necessary analytical expressions, for the analysis of Gaussian-distributed time series that exhibit discrete states. After the time series has been partitioned into segments in a model-free manner using the previously developed change-point (CP) method, this protocol starts with an agglomerative hierarchical clustering algorithm to classify the detected segments into possible states. The initial state clustering is further refined using an expectation-maximization (EM) procedure, and the number of states is determined by a Bayesian information criterion (BIC). Also introduced here is an achievement scalarization function, usually seen in artificial intelligence literature, for quantitatively assessing the performance of state determination. The statistical learning framework, which is comprised of three stages---detection of signal change, clustering, and number-of-state determination---was thoroughly characterized using simulated trajectories with random intensity segments that have no underlying kinetics, and its performance critically evaluated. The application to experimental data is also demonstrated. The results suggested that this general framework, the implementation of which is based on firm theoretical foundations and does not require the imposition of any kinetics model, is powerful in determining the number of states, the parameters contained in each state, as well as the associated statistical significance.

Statistical Learning of Discrete States in Time Series.

Time series obtained from time-dependent experiments contain rich information on kinetics and dynamics of the system under investigation. This work describes an unsupervised learning framework, along with the derivation of the necessary analytical expressions, for the analysis of Gaussian-distributed time series that exhibit discrete states. After the time series has been partitioned into segments in a model-free manner using the previously developed change-point (CP) method, this protocol starts with an agglomerative hierarchical clustering algorithm to classify the detected segments into possible states. The initial state clustering is further refined using an expectation-maximization (EM) procedure, and the number of states is determined by a Bayesian information criterion (BIC). Also introduced here is an achievement scalarization function, usually seen in artificial intelligence literature, for quantitatively assessing the performance of state determination. The statistical learning framework, which is comprised of three stages, detection of signal change, clustering, and number-of-state determination, was thoroughly characterized using simulated trajectories with random intensity segments that have no underlying kinetics, and its performance was critically evaluated. The application to experimental data is also demonstrated. The results suggested that this general framework, the implementation of which is based on firm theoretical foundations and does not require the imposition of any kinetics model, is powerful in determining the number of states, the parameters contained in each state, as well as the associated statistical significance.

Estimation of the minimum detectable phase change of surface coil for neural current MRI.

Neuronal current magnetic resonance imaging (NC-MRI) is a new method in functional imaging of the brain that could cause the alteration in the phase of magnetic resonance signal. The phase variance is defined as the inverse of the signal to noise ratio (SNR). The intrinsic SNR of the MRI signal is characterized by the coil performance. We evaluated the relation between the geometry and the shape of coils in order to find the minimum detectable change in the signal phase and the possibility of direct detection of neuronal activity by MRI. Full wave equations were solved by the finite element method to calculate the SNR for circular, elliptical, and square shape surface coils. The simulation was repeated for Larmor frequencies of 64MHz and 85.2MHz and the coil sizes between 1.5 and 7.5cm. Relative intrinsic signal to noise ratio (rISNR) of coils with a respect to a selected reference coil and a reference point in the sample was estimated. The circular coil had higher rISNR than other shapes. The increase of the strip width in the coils raised the rISNR 5-20%. For typical imaging parameters, rISNR reference was about 66 which led to a minimum detectable change in MRI signal phase of 0.87° (11.4nT). It may also be reduced up to tenfold in a 1.5cm circular coil. Detection of subtle phase signal change due to neuronal activity in surface coils needs a large amount of data acquisition and averaging, but it is intrinsically feasible.

GlucoCEST imaging with on-resonance variable delay multiple pulse (onVDMP) MRI.

To examine the detection sensitivity for the rapidly exchanging hydroxyl protons of D-glucose using the recently developed on-resonance variable delay multi-pulse (onVDMP) chemical exchange saturation transfer (CEST) technique. The onVDMP method was applied for the detection of water signal changes upon venous D-glucose infusion in mice with 9L glioma xenografts. The effect size of onVDMP MRI during infusion was compared with that of conventional continuous wave (CW) CEST MRI. Both methods highlighted the tumor and the blood vessels on D-glucose infusion. In interleaved studies, the mean signal changes detected by onVDMP were found to be 1.8 times higher than those by CW-CEST, attributed to its high labeling efficiency for fast exchanging protons and the labeling of the OH protons over a larger frequency range. The onVDMP method is a more sensitive technique for the detection of exogenous CEST agents with fast-exchanging protons compared to CW-CEST MRI.

Multiscale energy reallocation during low-frequency steady-state brain response.

Traditional task-evoked brain activations are based on detection and estimation of signal change from the mean signal. By contrast, the low-frequency steady-state brain response (lfSSBR) reflects frequency-tagging activity at the fundamental frequency of the task presentation and its harmonics. Compared to the activity at these resonant frequencies, brain responses at nonresonant frequencies are largely unknown. Additionally, because the lfSSBR is defined by power change, we hypothesize using Parseval's theorem that the power change reflects brain signal variability rather than the change of mean signal. Using a face recognition task, we observed power increase at the fundamental frequency (0.05 Hz) and two harmonics (0.1 and 0.15 Hz) and power decrease within the infra-slow frequency band (<0.1 Hz), suggesting a multifrequency energy reallocation. The consistency of power and variability was demonstrated by the high correlation (r > .955) of their spatial distribution and brain-behavior relationship at all frequency bands. Additionally, the reallocation of finite energy was observed across various brain regions and frequency bands, forming a particular spatiotemporal pattern. Overall, results from this study strongly suggest that frequency-specific power and variability may measure the same underlying brain activity and that these results may shed light on different mechanisms between lfSSBR and brain activation, and spatiotemporal characteristics of energy reallocation induced by cognitive tasks.

Prediction of activation patterns preceding hallucinations in patients with schizophrenia using machine learning with structured sparsity.

Despite significant progress in the field, the detection of fMRI signal changes during hallucinatory events remains difficult and time-consuming. This article first proposes a machine-learning algorithm to automatically identify resting-state fMRI periods that precede hallucinations versus periods that do not. When applied to whole-brain fMRI data, state-of-the-art classification methods, such as support vector machines (SVM), yield dense solutions that are difficult to interpret. We proposed to extend the existing sparse classification methods by taking the spatial structure of brain images into account with structured sparsity using the total variation penalty. Based on this approach, we obtained reliable classifying performances associated with interpretable predictive patterns, composed of two clearly identifiable clusters in speech-related brain regions. The variation in transition-to-hallucination functional patterns not only from one patient to another but also from one occurrence to the next (e.g., also depending on the sensory modalities involved) appeared to be the major difficulty when developing effective classifiers. Consequently, second, this article aimed to characterize the variability within the prehallucination patterns using an extension of principal component analysis with spatial constraints. The principal components (PCs) and the associated basis patterns shed light on the intrinsic structures of the variability present in the dataset. Such results are promising in the scope of innovative fMRI-guided therapy for drug-resistant hallucinations, such as fMRI-based neurofeedback.

Nitration of tyrosine and its effect on DNA hybridization

One major marker of nitrosative stress is the formation of 3-Nitrotyrosine (3-NT) from Tyrosine (Tyr) by adding a nitro group (-NO2) with nitrating agents. Nitration of Tyr often causes loss of protein activity and is linked with many diseases. In this article, we detect 3-NT and discriminate it from Tyr with Differential Pulse Voltammetry (DPV) as it is a very important biomarker. We first examined redox (oxidation/reduction) properties and stability of 3-NT in detail. Second, we provided the Tyr and 3-NT discrimination with DPV and compared with the chromatography. We then explored the interaction of 3-NT and DNA oligonucleotides. Our findings demonstrate that 3-NT can be used as a new electrochemical indicator, which is able to detect hybridization of probe (single stranded DNA-ssDNA) and hybrid (double stranded DNA-dsDNA) both via 3-NT reduction and guanine oxidation signal changes at the same time. The signal differences enabled us to distinguish ssDNA and dsDNA without using a label or a tag. Moreover, we achieved to detect hybridization of DNA by using the reduction signal of 3-NT obtained at −0.4V vs. Ag/AgCl. More importantly, we observed the changes of the reduction signals of 3-NT after the interaction of probe and hybrid sequences. We showed that 3-NT signal decreases more with hybrid than the probe. Our platform, for the first time, demonstrates the detection of hybridization both guanine oxidation and indicator reduction signal changes at the same time. Moreover, we, for the first time, demonstrated the interaction between 3-NT and DNA.

Trial-Level Regressor Modulation for Functional Magnetic Resonance Imaging Designs Requiring Strict Periodicity of Stimulus Presentations: Illustrated Using a Go/No-Go Task.

Computer-based assessment of many cognitive processes (eg, anticipatory and response readiness processes) requires the use of invariant stimulus display times (SDT) and intertrial intervals (ITI). Although designs with invariant SDTs and ITIs have been used in functional magnetic resonance imaging (fMRI) research, such designs are problematic for fMRI studies because of collinearity issues. This study examined regressor modulation with trial-level reaction times (RT) as a method for improving signal detection in a go/no-go task with invariant SDTs and ITIs. The effects of modulating the go regressor were evaluated with respect to the detection of BOLD signal-change for the no-go condition. BOLD signal-change to no-go stimuli was examined when the go regressor was based on a (a) canonical hemodynamic response function (HRF), (b) RT-based amplitude-modulated (AM) HRF, and (c) RT-based amplitude and duration modulated (A&DM) HRF. Reaction time–based modulation reduced the collinearity between the go and no-go regressors, with A&DM producing the greatest reductions in correlations between the regressors, and greater reductions in the correlations between regressors were associated with longer mean RTs and greater RT variability. Reaction time–based modulation increased statistical power for detecting group-level no-go BOLD signal-change across a broad set of brain regions. The findings show the efficacy of using regressor modulation to increase power in detecting BOLD signal-change in fMRI studies in which circumstances dictate the use of temporally invariant stimulus presentations.

Effectiveness of 3D T2-Weighted FLAIR FSE Sequences with Fat Suppression for Detection of Brain MR Imaging Signal Changes in Children.

T2-weighted FLAIR can be combined with 3D-FSE sequences with isotropic voxels, yielding higher signal-to-noise ratio than 2D-FLAIR. Our aim was to explore whether a T2-weighted FLAIR-volume isotropic turbo spin-echo acquisition sequence (FLAIR-VISTA) with fat suppression shows areas of abnormal brain T2 hyperintensities with better conspicuity in children than a single 2D-FLAIR sequence. One week after a joint training session with 20 3T MR imaging examinations (8 under sedation), 3 radiologists independently evaluated the presence and conspicuity of abnormal areas of T2 hyperintensities of the brain in FLAIR-VISTA with fat suppression (sagittal source and axial and coronal reformatted images) and in axial 2D-FLAIR without fat suppression in a test set of 100 3T MR imaging examinations (34 under sedation) of patients 2-18 years of age performed for several clinical indications. Their agreement was measured with weighted κ statistics. Agreement was "substantial" (mean, 0.61 for 3 observers; range, 0.49-0.69 for observer pairs) for the presence of abnormal T2 hyperintensities and "fair" (mean, 0.29; range, 0.23-0.38) for the comparative evaluation of lesion conspicuity. In 21 of 23 examinations in which the 3 radiologists agreed on the presence of abnormal T2 hyperintensities, FLAIR-VISTA with fat suppression images were judged to show hyperintensities with better conspicuity than 2D-FLAIR. In 2 cases, conspicuity was equal, and in no case was conspicuity better in 2D-FLAIR. FLAIR-VISTA with fat suppression can replace the 2D-FLAIR sequence in brain MR imaging protocols for children.

Scintillate: An open-source graphical viewer for time-series calcium imaging evaluation and pre-processing

BackgroundCalcium imaging is based on the detection of minute signal changes in an image time-series encompassing pre- and post-stimuli. Depending on the function of the elicited response, change may be pronounced, as in the case of a genetically encoded calcium-reporter protein, or subtle, as is the case in a bath-applied dye system. Large datasets are thus often acquired and appraised only during post-processing where specific Regions of Interest (ROIs) are examined. New methodThe scintillate software provides a platform allowing for near instantaneous viewing of time-sequenced tiffs within a discrete GUI environment. Whole sequences may be evaluated. In its simplest form scintillate provides change in florescence (ΔF) across the entire tiff image matrix. Evaluating image intensity level differences across the whole image allows the user to rapidly establish the value of the preparation, without a priori ROI-selection. Additionally, an implementation of Independent Component Analysis (ICA) provides additional rapid insights into areas of signal change. ResultsWe imaged transgenic flies expressing Calcium-sensitive reporter proteins within projection neurons and moth mushroom bodies stained with a Ca2+ sensitive bath-applied dye. Instantaneous pre-stimulation background subtraction allowed us to appraise strong genetically encoded neuronal Ca2+ responses in flies and weaker, less apparent, responses within moth mushroom bodies. Comparison with existing methodsAt the time of acquisition, whole matrix ΔF analysis alongside ICA is ordinarily not performed. We found it invaluable, minimising time spent with unresponsive samples, and assisting in optimisation of subsequent acquisitions. ConclusionsWe provide a multi-platform open-source system to evaluate time-series images.

Delineation of Alar Ligament Morphology: Comparison of Magnetic Resonance Imaging at 1.5 and 3 Tesla

Rupture of the alar and transverse ligaments due to whiplash injury can lead to upper cervical spine instability and subsequent neurological deterioration. The purpose of this study was to evaluate the normal anatomical variability of the alar ligaments in asymptomatic individuals with 3-T magnetic resonance imaging (MRI) and to compare the findings with standard 1.5-T examinations. Thirty-six participants underwent 3-T and 1.5-T MRIs. Magnetic resonance imaging findings were analyzed by classifying the alar ligaments with regard to the features detectability, signal intensity compared with muscle tissue, homogeneity, shape, spatial orientation, and symmetry. Delineation of the alar ligaments was significantly better on 3-T images, which were subjectively preferred for evaluation. The alar ligaments showed great variability. In the majority of participants, the alar ligaments were hypointense to muscle tissue, inhomogeneous, and different in shape and orientation. A statistically significantly higher number of ligaments appeared symmetric on 3-T imaging, indicating that 1.5-T imaging may underestimate the proportion of patients with normal, symmetric ligaments. This study demonstrates that high-field 3-T MRI provides better visualization of the alar ligaments compared with 1.5-T MRI. The higher signal-to-noise ratio allows detection of small signal changes. A great interindividual variety of the MRI morphology of the alar ligaments was found in participants with no history of neck trauma. Further studies with more participants are necessary to evaluate alar ligament pathologies in patients with a history of whiplash injury.

A 7 Tesla fMRI Study of Amygdala Responses to Fearful Faces

The amygdalae are involved in the perception of emotions such as happiness, anger and fear. Because of their proximity to the sinuses, the image signal intensity in T2* weighted fMRI data is often affected by signal loss due to through-slice dephasing, especially at high field strength. In this study, the feasibility of fMRI in the amygdalae at 7 Tesla was investigated. A paradigm based on the presentation of fearful faces was used for stimulation. Previously, opposite effects have been found for presentation of averted and direct gaze fearful faces. Here, we show that (1) sufficiently high temporal SNR values are reached in the amygdalae for detection of small BOLD signal changes and (2) that the BOLD signal in the amygdalae for presentation of a direct or averted gaze in a fearful face depends on stimulus duration.

PCEST: Positive contrast using Chemical Exchange Saturation Transfer

Chemical Exchange Saturation Transfer (CEST) contrast utilizes selective pre-saturation of a small pool of exchanging protons and subsequent detection of the decrease in bulk water signal. The CEST contrast is negative and requires detection of small signal change in the presence of a strong background signal. Here we develop a Positive CEST (pCEST) detection scheme utilizing the analogous nature of the CEST and off-resonance T1ρ experiments and exploring increased apparent relaxation rates in the presence of the selective pre-saturation. pCEST leads to the positive contrast, i.e., increased signal intensity as the result of the presence of the agent and RF pre-saturation. Simultaneously substantial background suppression is achieved. The contrast can be switched “ON” and “OFF”, similar to the original CEST.