Results: The 39-potassium flexible twisted projection imaging imaging had a signal-to-noise ratio of 5.2 in brain paranchyma. This qualitative imaging showed the expected features when compared to co-registered high- and low-resolution sodium imaging of the same subject.
the signal and the noise epub 39
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Conclusion: Potassium MR images may offer complementary information to that of sodium MR images by sampling the intracellular rather that interstitial environment. Quantification will require additional improvement in signal-to-noise ratio to produce clinically useful bioscales as are developing for sodium MR imaging.
A method for obtaining strongly polarized nuclear spins in solution has been developed. The method uses low temperature, high magnetic field, and dynamic nuclear polarization (DNP) to strongly polarize nuclear spins in the solid state. The solid sample is subsequently dissolved rapidly in a suitable solvent to create a solution of molecules with hyperpolarized nuclear spins. The polarization is performed in a DNP polarizer, consisting of a super-conducting magnet (3.35 T) and a liquid-helium cooled sample space. The sample is irradiated with microwaves at approximately 94 GHz. Subsequent to polarization, the sample is dissolved by an injection system inside the DNP magnet. The dissolution process effectively preserves the nuclear polarization. The resulting hyperpolarized liquid sample can be transferred to a high-resolution NMR spectrometer, where an enhanced NMR signal can be acquired, or it may be used as an agent for in vivo imaging or spectroscopy. In this article we describe the use of the method on aqueous solutions of [13C]urea. Polarizations of 37% for 13C and 7.8% for 15N, respectively, were obtained after the dissolution. These polarizations correspond to an enhancement of 44,400 for 13C and 23,500 for 15N, respectively, compared with thermal equilibrium at 9.4 T and room temperature. The method can be used generally for signal enhancement and reduction of measurement time in liquid-state NMR and opens up for a variety of in vitro and in vivo applications of DNP-enhanced NMR.
8/2/2021Fan L, Henry KS, Carney LH. "Responses to diotic tone-in-noise stimuli in the inferior colliculus: stimulus envelope and neural fluctuation cues." Hearing research.. 2021 Aug 2; 409:108328. Epub 2021 Aug 02.
7/15/2021Wang Y, Abrams KS, Carney LH, Henry KS. "Midbrain-level neural correlates of behavioral tone-in-noise detection: dependence on energy and envelope cues." The Journal of neuroscience : the official journal of the Society for Neuroscience.. 2021 Jul 15; Epub 2021 Jul 15.
11/10/2020Henry KS, Abrams KS. "Normal tone-in-noise sensitivity in trained budgerigars despite substantial auditory-nerve injury: no evidence of hidden hearing loss." The Journal of neuroscience : the official journal of the Society for Neuroscience.. 2020 Nov 10; Epub 2020 Nov 10.
2/2020Henry KS, Amburgey KN, Abrams KS, Carney LH. "Identifying cues for tone-in-noise detection using decision variable correlation in the budgerigar (Melopsittacus undulatus)." The Journal of the Acoustical Society of America.. 2020 Feb; 147(2):984.
4/2015Lucas JR, Vélez A, Henry KS. "Habitat-related differences in auditory processing of complex tones and vocal signal properties in four songbirds." Journal of comparative physiology. A, Neuroethology, sensory, neural, and behavioral physiology.. 2015 Apr; 201(4):395-410. Epub 2015 Feb 15.
9/2013Henry KS, Heinz MG. "Effects of sensorineural hearing loss on temporal coding of narrowband and broadband signals in the auditory periphery." Hearing research.. 2013 Sep; 303:39-47. Epub 2013 Jan 29.
10/2012Henry KS, Heinz MG. "Diminished temporal coding with sensorineural hearing loss emerges in background noise." Nature neuroscience.. 2012 Oct; 15(10):1362-4. Epub 2012 Sep 09.
Bioinformatics analysis of ONT data has undergone continued improvement (Fig. 4). In addition to in-house data collection and specific data formats, many ONT-specific analyses focus on better utilizing the ionic current signal for purposes such as base calling, base modification detection and postassembly polishing. Other tools use long read length while accounting for high error rate. Many of these, such as tools for error correction, assembly and alignment, were developed for PacBio data but are also applicable to ONT data (Table 1).
ONT enables the direct detection of some DNA and RNA modifications by distinguishing their current shifts from those of unmodified bases52,71,72,73,74 (Fig. 4, middle center), although the resolution varies from the bulk level to the single-molecule level. A handful of DNA and RNA modification detection tools have been developed over the years (Table 1). Nanoraw (integrated into the Tombo software package) was the first tool to identify the DNA modifications 5mC, 6mA and N4-methylcytosine (4mC) from ONT data74. Several other DNA modification detection tools followed, including Nanopolish (5mC)75, signalAlign (5mC, 5-hydroxymethylcytosine (5hmC) and 6mA)71, mCaller (5mC and 6mA)76, DeepMod (5mC and 6mA)76, DeepSignal (5mC and 6mA)77 and NanoMod (5mC and 6mA)78. Nanpolish, Megalodon and DeepSignal were recently benchmarked and confirmed to have high accuracy for 5mC detection with single-nucleotide resolution at the single-molecule level79,80. Compared to PacBio, ONT performs better in detecting 5mC but has lower accuracy in detecting 6mA68,75,81.
The possibility of directly detecting N6-methyladenosine (m6A) modifications in RNA molecules was demonstrated using PacBio in 2012 (ref. 82), although few follow-up applications were published. Recently, ONT direct RNA sequencing has yielded robust data of reasonable quality, and several pilot studies have detected bulk-level RNA modifications by examining either error distribution profiles (for example, EpiNano (m6A)73 and ELIGOS (m6A and 5-methoxyuridine (5moU))83) or current signals (for example, Tombo extension (m6A and m5C)74 and MINES (m6A)84). However, detection of RNA modifications with single-nucleotide resolution at the single-molecule level has yet to be demonstrated.
As early as 2013, independent reports demonstrated that methylated cytosines (5mC and 5hmC) in DNA could be distinguished from native cytosine by the characteristic current signals measured using the MspA nanopore172,173. Later, bioinformatics tools were developed to identify three kinds of DNA modifications (6mA, 5mC and 5hmC) from ONT data71,75. Recently, ONT was applied to characterize the methylomes from different biological samples, such as 6mA in a microbial reference community174 as well as 5mC and 6mA in E. coli, Chlamydomonas reinhardtii and human genomes76.
Compared to existing antibody-based approaches (which are usually followed by short-read sequencing), ONT direct RNA sequencing opens opportunities to directly identify RNA modifications (for example, m6A) and RNA editing (for example, inosine), which have critical biological functions. In 2018, distinct ionic current signals for unmodified and modified bases (for example, m6A and m5C) in ONT direct RNA-sequencing data were reported52. Since then, epitranscriptome analyses using ONT sequencing have progressed rapidly, including detection of 7-methylguanosine (m7G) and pseudouridine in 16S rRNAs of E. coli183, m6A in mRNAs of S. cerevisiae73 and A. thaliana168 and m6A130 and pseudouridine104 in human RNAs. Recent independent research (K.F.A., unpublished data, and refs. 184,185) has revealed that it is possible to probe RNA secondary structure using a combination of ONT direct RNA sequencing and artificial chemical modifications (Table 1). The dynamics of RNA metabolism were also analyzed by labeling nascent RNAs with base analogs (for example, 5-ethynyluridine186 and 4-thiouridine187) followed by ONT direct RNA sequencing (Table 1).
The brain exhibits widespread endogenous responses in the absence of visual stimuli, even at the earliest stages of visual cortical processing. Such responses have been studied in monkeys using optical imaging with a limited field of view over visual cortex. Here, we used functional MRI (fMRI) in human participants to study the link between arousal and endogenous responses in visual cortex. The response that we observed was tightly entrained to task timing, was spatially extensive, and was independent of visual stimulation. We found that this response follows dynamics similar to that of pupil size and heart rate, suggesting that task-related activity is related to arousal. Finally, we found that higher reward increased response amplitude while decreasing its trial-to-trial variability (i.e., the noise). Computational simulations suggest that increased temporal precision underlies both of these observations. Our findings are consistent with optical imaging studies in monkeys and support the notion that arousal increases precision of neural activity.
The goal of our analysis was to test the link between arousal and task-related fMRI activity. However, the changes in heart rate that we observed created an important obstacle for making inferences regarding fMRI activity, for the following reason: it is conceivable that arousal affects physiological processes, which in turn impact the BOLD signal. Although multi-echo independent components analysis (ME-ICA) considerably reduces physiological noise in the fMRI data [33, 34], it does not eliminate it [35]. Any change in fMRI activity with reward could, in theory, reflect peripheral physiological changes rather than neuronal changes. Indeed, we found that heart rate influenced the BOLD signal in a systematic way, yielding a pulse-to-BOLD kernel [36, 37] (Fig 2C). This observation suggests that reward influences heart rate, which in turn affected the BOLD signal.
Could the task-related response reflect modulations in physiological covariates rather than changes in the brain? To address this question, we removed the impact of physiological signals from the fMRI time series by regressing out the global mean fMRI time series, a procedure that is thought to be the most effective means of removing the impact of heart rate and respiration on fMRI measurements [4, 38]. This procedure reduced the mean pulse-to-BOLD kernel amplitude by 92% (Fig 2C), confirming global signal regression is effective at mitigating the impact of heart-rate effects in fMRI. Critically, task-related fMRI activity remained robust after regressing out the global signal (Fig 3), indicating that task-related activity is not a secondary consequence of the respiratory and pulse changes that occur with the task. 2ff7e9595c
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