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Signal-to-noise ratio, the ratio between signal and noise, is a quantity that has been well established for MRI data but is still subject of ongoing debate and confusion when it comes to fMRI data. fMRI data are characterised by small activation fluctuations in a background of noise. Depending on how the signal of interest and the noise are identified, signal-to-noise ratio for fMRI data is reported by using many different definitions. Since each definition comes with a different scale, interpreting and comparing signal-to-noise ratio values for fMRI data can be a very challenging job. In this paper, we provide an overview of existing definitions. Further, the relationship with activation detection power is investigated. Reference tables and conversion formulae are provided to facilitate comparability between fMRI studies.
The absolute sensitivity of vertebrate retinas is set by a background noise, called dark noise, which originates from several different cell types and is generated by different molecular mechanisms. The major share of dark noise is produced by photoreceptors and consists of two components, discrete and continuous. Discrete noise is generated by spontaneous thermal activations of visual pigment. These events are undistinguishable from real single-photon responses (SPRs) and might be considered an equivalent of the signal. Continuous noise is produced by spontaneous fluctuations of the catalytic activity of the cGMP phosphodiesterase. This masks both SPR and spontaneous SPR-like responses. Circadian rhythms affect photoreceptors, among other systems by periodically increasing intracellular cAMP levels ([cAMP]in), which increases the size and changes the shape of SPRs. Here, we show that forskolin, a tool that increases [cAMP]in, affects the magnitude and frequency spectrum of the continuous and discrete components of dark noise in photoreceptors. By changing both components of rod signaling, the signal and the noise, cAMP is able to increase the photoreceptor signal-to-noise ratio by twofold. We propose that this results in a substantial improvement of signal detection, without compromising noise rejection, at the rod bipolar cell synapse.
Automated sound recorders are a popular sampling tool in ecology. However, the microphones themselves received little attention so far, and specifications that determine the recordings' sound quality are seldom mentioned. Here, we demonstrate the importance of microphone signal-to-noise ratio for sampling sonant animals.
Rolling circle amplification (RCA) for generation of distinct fluorescent signals in situ relies upon the self-collapsing properties of single-stranded DNA in commonly used RCA-based methods. By introducing a cross-hybridizing DNA oligonucleotide during rolling circle amplification, we demonstrate that the fluorophore-labeled RCA products (RCPs) become smaller. The reduced size of RCPs increases the local concentration of fluorophores and as a result, the signal intensity increases together with the signal-to-noise ratio. Furthermore, we have found that RCPs sometimes tend to disintegrate and may be recorded as several RCPs, a trait that is prevented with our cross-hybridizing DNA oligonucleotide. These effects generated by compaction of RCPs improve accuracy of visual as well as automated in situ analysis for RCA based methods, such as proximity ligation assays (PLA) and padlock probes.
Diffusion-ordered NMR spectroscopy (DOSY) constructs multidimensional spectra displaying signal strength as a function of Larmor frequency and of diffusion coefficient from experimental measurements using pulsed field gradient spin or stimulated echoes. Peak positions in the diffusion domain are determined by diffusion coefficients estimated by fitting experimental data to some variant of the Stejskal-Tanner equation, with the peak widths determined by the standard error estimated in the fitting process. The accuracy and reliability of the diffusion domain in DOSY spectra are therefore determined by the uncertainties in the experimental data and thus in part by the signal-to-noise ratio of the experimental spectra measured. Here the Cramér-Rao lower bound, Monte Carlo methods, and experimental data are used to investigate the relationship between signal-to-noise ratio, experimental parameters, and diffusion domain accuracy in 2D DOSY experiments. Experimental results confirm that sources of error other than noise put an upper limit on the improvement in diffusion domain accuracy obtainable by time averaging.
Real-time molecular imaging to guide curative cancer surgeries is critical to ensure removal of all tumor cells; however, visualization of microscopic tumor foci remains challenging. Wide variation in both imager instrumentation and molecular labeling agents demands a common metric conveying the ability of a system to identify tumor cells. Microscopic disease, comprised of a small number of tumor cells, has a signal on par with the background, making the use of signal (or tumor) to background ratio inapplicable in this critical regime. Therefore, a metric that incorporates the ability to subtract out background, evaluating the signal itself relative to the sources of uncertainty, or noise is required. Here we introduce the signal to noise ratio (SNR) to characterize the ultimate sensitivity of an imaging system and optimize factors such as pixel size. Variation in the background (noise) is due to electronic sources, optical sources, and spatial sources (heterogeneity in tumor marker expression, fluorophore binding, and diffusion). Here, we investigate the impact of these noise sources and ways to limit its effect on SNR. We use empirical tumor and noise measurements to procedurally generate tumor images and run a Monte Carlo simulation of microscopic disease imaging to optimize parameters such as pixel size.
Functional magnetic resonance imaging has been used to investigate the signal representation in human auditory cortex for a sinusoidal signal in the presence of a noise masker. This paradigm is widely used in auditory research to study auditory processing. Five-note tonal melodies were presented in a masking noise for signal-to-noise ratios (S/N) from -18 dB to+24 dB in 6 dB-steps. For small S/N (-18 dB, -12 dB, -6 dB) the overall level of the sound is nearly constant, but the audibility of the tone varies with S/N. For S/N of 0 dB and above, the tone is always clearly audible, and the perceived change is mainly the increase in overall level. This interaction between S/N, overall level and perception is reflected by a spatial dissociation of the respective activation in auditory cortex. Brain regions mainly sensitive to level changes were found in various parts of the superior temporal lobes, including primary auditory cortex and Planum temporale, while those regions mainly sensitive to S/N changes were located at or close to lateral Heschl's gyrus. The overlap between these two regions is small. The results are interpreted as indicating that the coding of overall level and, thus, loudness is different from the coding of audibility of a periodic signal. The S/N-sensitive region largely overlaps with the pitch-sensitive regions in lateral Heschl's gyrus found in previous studies. The results from the present study further suggest that the audibility of a tone in noise is related to the overall pitch strength.
Dopamine modulates medial prefrontal cortex (mPFC) activity to mediate diverse behavioural functions1,2; however, the precise circuit computations remain unknown. One potentially unifying model by which dopamine may underlie a diversity of functions is by modulating the signal-to-noise ratio in subpopulations of mPFC neurons3-6, where neural activity conveying sensory information (signal) is amplified relative to spontaneous firing (noise). Here we demonstrate that dopamine increases the signal-to-noise ratio of responses to aversive stimuli in mPFC neurons projecting to the dorsal periaqueductal grey (dPAG). Using an electrochemical approach, we reveal the precise time course of pinch-evoked dopamine release in the mPFC, and show that mPFC dopamine biases behavioural responses to aversive stimuli. Activation of mPFC-dPAG neurons is sufficient to drive place avoidance and defensive behaviours. mPFC-dPAG neurons display robust shock-induced excitations, as visualized by single-cell, projection-defined microendoscopic calcium imaging. Finally, photostimulation of dopamine terminals in the mPFC reveals an increase in the signal-to-noise ratio in mPFC-dPAG responses to aversive stimuli. Together, these data highlight how dopamine in the mPFC can selectively route sensory information to specific downstream circuits, representing a potential circuit mechanism for valence processing.
For the first time, single-entity electrochemistry (SEE) was demonstrated in a hydrogel matrix. SEE involves the investigation of the electrochemical characteristics of individual nanoparticles (NPs) by observing the signal generated when a single NP, suspended in an aqueous solution, collides with an electrode and triggers catalytic reactions. Challenges associated with SEE in electrolyte-containing solutions such as signal variation due to NP aggregation and noise fluctuation caused by convection phenomena can be addressed by employing a hydrogel matrix. The polymeric hydrogel matrix acts as a molecular sieve, effectively filtering out unexpected signals generated by aggregated NPs, resulting in more uniform signal observations compared to the case in a solution. Additionally, the hydrogel environment can reduce the background current fluctuations caused by natural convection and other factors such as impurities, facilitating easier signal analysis. Specifically, we performed SEE of platinum (Pt) NPs for hydrazine oxidation within the agarose hydrogel to observe the electrocatalytic reaction at a single NP level. The consistent porous structure of the agarose hydrogel leads to differential diffusion rates between individual NPs and reactants, resulting in variations in signal magnitude, shape, and frequency. The changes in the signal were analyzed in response to gel concentration variations.
Dynamic range compression is a compensation strategy commonly used in modern hearing aids. Fast-acting systems respond relatively quickly to the fluctuations in the input level. This allows for more effective compression of the dynamic range of speech and hence enhanced the audibility of its low-intensity components. However, such processing also amplifies the background noise, distorts the modulation spectra of both the speech and the background, and can reduce the output signal-to-noise ratio (SNR). Recently, May et al. proposed a novel SNR-aware compression strategy, in which the compression speed is adapted depending on whether speech is present or absent. Fast-acting compression is applied to speech-dominated time-frequency (T-F) units, while noise-dominated T-F units are processed using slow-acting compression. It has been shown that this strategy provides a similar effective compression of the speech dynamic range as conventional fast-acting compression, while introducing fewer distortions of the modulation spectrum of the background and providing an improved output SNR. In this study, this SNR-aware compression strategy was compared with conventional fast- and slow-acting compression in terms of speech intelligibility and subjective preference in a group of 17 hearing-impaired listeners with varying degree of hearing loss. The results show a speech intelligibility benefit of the SNR-aware compression strategy over the conventional slow-acting system. Furthermore, the SNR-aware approach demonstrates an increased subjective preference compared with both conventional fast- and slow-acting systems.
Norepinephrine (NE) has been shown to influence sensory, and specifically olfactory processing at the behavioral and physiological levels, potentially by regulating signal-to-noise ratio (S/N). The present study is the first to look at NE modulation of olfactory bulb (OB) in regards to S/N in vivo We show, in male rats, that locus ceruleus stimulation and pharmacological infusions of NE into the OB modulate both spontaneous and odor-evoked neural responses. NE in the OB generated a non-monotonic dose-response relationship, suppressing mitral cell activity at high and low, but not intermediate, NE levels. We propose that NE enhances odor responses not through direct potentiation of the afferent signal per se, but rather by reducing the intrinsic noise of the system. This has important implications for the ways in which an animal interacts with its olfactory environment, particularly as the animal shifts from a relaxed to an alert behavioral state.SIGNIFICANCE STATEMENT Sensory perception can be modulated by behavioral states such as hunger, fear, stress, or a change in environmental context. Behavioral state often affects neural processing via the release of circulating neurochemicals such as hormones or neuromodulators. We here show that the neuromodulator norepinephrine modulates olfactory bulb spontaneous activity and odor responses so as to generate an increased signal-to-noise ratio at the output of the olfactory bulb. Our results help interpret and improve existing ideas for neural network mechanisms underlying behaviorally observed improvements in near-threshold odor detection and discrimination.
Quantitative comparison of epigenomic data across multiple cell types or experimental conditions is a promising way to understand the biological functions of epigenetic modifications. However, differences in sequencing depth and signal-to-noise ratios in the data from different experiments can hinder our ability to identify real biological variation from raw epigenomic data. Proper normalization is required prior to data analysis to gain meaningful insights. Most existing methods for data normalization standardize signals by rescaling either background regions or peak regions, assuming that the same scale factor is applicable to both background and peak regions. While such methods adjust for differences in sequencing depths, they do not address differences in the signal-to-noise ratios across different experiments. We developed a new data normalization method, called S3norm, that normalizes the sequencing depths and signal-to-noise ratios across different data sets simultaneously by a monotonic nonlinear transformation. We show empirically that the epigenomic data normalized by our method, compared to existing methods, can better capture real biological variation, such as impact on gene expression regulation.
We have investigated the effectiveness of three noise-reduction algorithms, namely an adaptive monaural beamformer (MB), a fixed binaural beamformer (BB), and a single-microphone stationary-noise reduction algorithm (SNRA) by assessing the speech reception threshold (SRT) in a group of 15 bimodal cochlear implant users. Speech was presented frontally towards the listener and background noise was established as a homogeneous field of long-term speech-spectrum-shaped (LTSS) noise or 8-talker babble. We pursued four research questions, namely: whether the benefits of beamforming on the SRT differ between LTSS noise and 8-talker babble; whether BB is more effective than MB; whether SNRA improves the SRT in LTSS noise; and whether the SRT benefits of MB and BB are comparable to their improvement of the signal-to-noise ratio (SNR). The results showed that MB and BB significantly improved SRTs by an average of 2.6 dB and 2.9 dB, respectively. These benefits did not statistically differ between noise types or between the two beamformers. By contrast, physical SNR improvements obtained with a manikin revealed substantially greater benefits of BB (6.6 dB) than MB (3.3 dB). SNRA did not significantly affect SRTs per se in omnidirectional microphone settings, nor in combination with MB and BB. We conclude that in the group of bimodal listeners tested, BB had no additional benefits on speech recognition over MB in homogeneous noise, despite the finding that BB had a substantial larger benefit on the SNR than MB. SNRA did not improve speech recognition.
Recent advances in MRI receiver and coil technologies have significantly improved image signal-to-noise ratios (SNR) and thus temporal SNR (TSNR). These gains in SNR and TSNR have allowed the detection of fMRI signal changes at higher spatial resolution and therefore have increased the potential to localize small brain structures such as cortical layers and columns. The majority of current fMRI processing strategies employ multi-subject averaging and therefore require spatial smoothing and normalization, effectively negating these gains in spatial resolution higher than about 10 mm3. Reliable detection of activation in single subjects at high resolution is becoming a more common desire among fMRI researchers who are interested in comparing individuals rather than populations. Since TSNR decreases with voxel volume, detection of activation at higher resolutions requires longer scan durations. The relationship between TSNR, voxel volume and detectability is highly non-linear. In this study, the relationship between TSNR and the necessary fMRI scan duration required to obtain significant results at varying P values is determined both experimentally and theoretically. The results demonstrate that, with a TSNR of 50, detection of activation of above 2% requires at most 350 scan volumes (when steps are taken to remove the influence of physiological noise from the data). Importantly, these results also demonstrate that, for activation magnitude on the order of 1%, the scan duration required is more sensitive to the TSNR level than at 2%. This study showed that with voxel volumes of approximately 10 mm3 at 3 T, and a corresponding TSNR of approximately 50, the required number of time points that guarantees detection of signal changes of 1% is about 860, but if TSNR increases by only 20%, the time for detection decreases by more than 30%. More than just being an exercise in numbers, these results imply that imaging of columnar resolution (effect size=1% and assuming a TR of 1 s) at 3 T will require either 10 min for a TSNR of 60 or 40 min for a TSNR of 30. The implication is that at these resolutions, TSNR is likely to be critical for determining success or failure of an experiment.
Localization microscopy and multiple signal classification algorithm use temporal stack of image frames of sparse emissions from fluorophores to provide super-resolution images. Localization microscopy localizes emissions in each image independently and later collates the localizations in all the frames, giving same weight to each frame irrespective of its signal-to-noise ratio. This results in a bias towards frames with low signal-to-noise ratio and causes cluttered background in the super-resolved image. User-defined heuristic computational filters are employed to remove a set of localizations in an attempt to overcome this bias. Multiple signal classification performs eigen-decomposition of the entire stack, irrespective of the relative signal-to-noise ratios of the frames, and uses a threshold to classify eigenimages into signal and null subspaces. This results in under-representation of frames with low signal-to-noise ratio in the signal space and over-representation in the null space. Thus, multiple signal classification algorithms is biased against frames with low signal-to-noise ratio resulting into suppression of the corresponding fluorophores. This paper presents techniques to automatically debias localization microscopy and multiple signal classification algorithm of these biases without compromising their resolution and without employing heuristics, user-defined criteria. The effect of debiasing is demonstrated through five datasets of invitro and fixed cell samples.
The importance of dopamine (DA) for prefrontal cortical (PFC) cognitive functions is widely recognized, but its mechanisms of action remain controversial. DA is thought to increase signal gain in active networks according to an inverted U dose-response curve, and these effects may depend on both tonic and phasic release of DA from midbrain ventral tegmental area (VTA) neurons.
The quality of gene expression data can vary dramatically from platform to platform, study to study, and sample to sample. As reliable statistical analysis rests on reliable data, determining such quality is of the utmost importance. Quality measures to spot problematic samples exist, but they are platform-specific, and cannot be used to compare studies.
Raman spectroscopy is a label-free and non-destructive spectroscopic technique that has been explored for bacterial identification. However, noise often interferes with the interesting Raman peaks because the Raman signal is inherently weak, especially for bacterial samples. Although this problem can be solved by increasing the exposure time or the power of the excitation laser, a longer acquisition time is required or the risk of sample damage is increased. In contrast, short exposure time and low laser power often lead to inadequate acquisition of Raman scattering, in which the Raman spectra with low signal-to-noise ratio (SNR) is difficult to be further analyzed. In order to quickly and accurately characterize biological samples by using low SNR Raman measurements, a weighted spectral reconstruction based method was developed and tested on Raman spectra with low SNR from 20 bacterial samples of two species. Principal component analysis followed by support vector machine was applied on the reference Raman spectra and the spectra recovered from the low SNR Raman measurements by the proposed method, the traditional spectral reconstruction method, and four other commonly used de-noising methods for the discrimination of bacterial species. The results showed that a classification accuracy of 90% was achieved based on our method, which was comparable to that of the reference Raman spectra and showed significant advantages over other spectral recovery methods. Therefore, the weighted spectral reconstruction method can preserve the most biochemical information for the bacterial species' identification while removing the noise from the low SNR Raman spectra, in which the advantages of lesser sample damage and shorter acquisition time would promote wider biomedical applications of Raman spectroscopy.
Visibility of low-contrast details in fluoroscopy and interventional radiology is important. Assessing detail visibility with human observers typically suffers from large observer variances. Objective, quantitative measurement of low-contrast detail visibility using a model observer, such as the square of the signal-to-noise ratio rate (SNR2rate), was implemented in MATLAB™ and evaluated. The expected linear response of SNR2rate based on predictions by the so-called Rose model and frame statistics was verified. The uncertainty in the measurement of SNR2rate for a fixed imaging geometry was 6% based on 16 repeated measurements. The results show that, as expected, reduced object thickness and x-ray field size substantially improved SNR2rate/PKA,rate with PKA,rate being the air kerma area product rate. The measurement precision in SNR2rate/PKA,rate (8-9%) is sufficient to detect small but important improvements, may guide the selection of better imaging settings and provides a tool for teaching good radiological imaging techniques to clinical staff.
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