Cancer cells require exogenous methionine for survival and therefore methionine restriction is a promising avenue for treatment. The basis for methionine dependence in cancer cells is still not entirely clear. While the lack of the methionine salvage enzyme methylthioadenosine phosphorylase (MTAP) is associated with methionine auxotrophy in cancer cells, there are other causes for tumors to require exogenous methionine. Restricting methionine by diet or by enzyme depletion, alone or in combination with certain chemotherapeutics, is a promising antitumor strategy.
While univariate functional magnetic resonance imaging (fMRI) data analysis methods have been utilized successfully to map brain areas associated with cognitive and emotional functions during viewing of naturalistic stimuli such as movies, multivariate methods might provide the means to study how brain structures act in concert as networks during free viewing of movie clips. Here, to achieve this, we generalized the partial least squares (PLS) analysis, based on correlations between voxels, experimental conditions, and behavioral measures, to identify large-scale neuronal networks activated during the first time and repeated watching of three ∼5-min comedy clips. We identified networks that were similarly activated across subjects during free viewing of the movies, including the ones associated with self-rated experienced humorousness that were composed of the frontal, parietal, and temporal areas acting in concert. In conclusion, the PLS method seems to be well suited for the joint analysis of multi-subject neuroimaging and behavioral data to quantify a functionally relevant brain network activity without the need for explicit temporal models.
Objective: Brain-computer interfaces (BCIs) decode information from neural activity and send it to external devices. The use of Deep Learning approaches for decoding allows for automatic feature engineering within the specific decoding task. Physiologically plausible interpretation of the network parameters ensures the robustness of the learned decision rules and opens the exciting opportunity for automatic knowledge discovery. Approach: We describe a compact convolutional network-based architecture for adaptive decoding of electrocorticographic (ECoG) data into finger kinematics. We also propose a novel theoretically justified approach to interpreting the spatial and temporal weights in the architectures that combine adaptation in both space and time. The obtained spatial and frequency patterns characterizing the neuronal populations pivotal to the specific decoding task can then be interpreted by fitting appropriate spatial and dynamical models. Main results: We first tested our solution using realistic Monte-Carlo simulations. Then, when applied to the ECoG data from Berlin BCI competition IV dataset, our architecture performed comparably to the competition winners without requiring explicit feature engineering. Using the proposed approach to the network weights interpretation we could unravel the spatial and the spectral patterns of the neuronal processes underlying the successful decoding of finger kinematics from an ECoG dataset. Finally we have also applied the entire pipeline to the analysis of a 32-channel EEG motor-imagery dataset and observed physiologically plausible patterns specific to the task. Significance: We described a compact and interpretable CNN architecture derived from the basic principles and encompassing the knowledge in the field of neural electrophysiology. For the first time in the context of such multibranch architectures with factorized spatial and temporal processing we presented theoretically justified weights interpretation rules. We verified our recipes using simulations and real data and demonstrated that the proposed solution offers a good decoder and a tool for investigating motor control neural mechanisms.
Magnetoencephalography (MEG) is a neuroimaging method ideally suited for non-invasive studies of brain dynamics. MEG’s spatial resolution critically depends on the approach used to solve the ill-posed inverse problem in order to transform sensor signals into cortical activation maps. Over recent years non-globally optimized solutions based on the use of adaptive beamformers (BF) gained popularity.
When operating in the environment with a small number of uncorrelated sources the BFs perform optimally and yield high spatial resolution. However, the BFs are known to fail when dealing with correlated sources acting like poorly tuned spatial filters with low signal-to-noise ratio (SNR) of the output timeseries and often meaningless cortical maps of power distribution.
This fact poses a serious limitation on the broader use of this promising technique especially since fundamental mechanisms of brain functioning, its inherent symmetry and task-based experimental paradigms result into a great deal of correlation in the activity of cortical sources. To cope with this problem, we developed a novel data covariance modification approach that allows for building beamformers that maintain high spatial resolution when operating in the environments with correlated sources.
At the core of our method is a projection operation applied to the vectorized sensor-space covariance matrix. This projection does not remove the activity of the correlated sources from the sensor-space covariance matrix but rather selectively handles their contributions to the covariance matrix and creates a sufficiently accurate approximation of an ideal data covariance that could hypothetically be observed should these sources be uncorrelated. Since the projection operation is reciprocal to the PSIICOS method developed by us earlier (Ossadtchi et al., 2018) we refer to the family of algorithms presented here as ReciPSIICOS.
We assess the performance of the novel approach using realistically simulated MEG data and show its superior performance in comparison to the classical BF approaches and well established MNE as a method immune to source synchrony by design. We have also applied our approach to the MEG datasets from the two experiments involving two different auditory tasks.
The analysis of experimental MEG datasets showed that beamformers from ReciPSIICOS family, but not the classical BF, discovered the expected bilateral focal sources in the primary auditory cortex and detected motor cortex activity associated with the audio-motor task. In most cases MNE managed well but as expected produced more spatially diffuse source distributions. Notably, ReciPSIICOS beamformers yielded cortical activity estimates with SNR several times higher than that obtained with the classical BF, which may indirectly indicate the severeness of the signal cancellation problem when applying classical beamformers to MEG signals generated by synchronous sources.
Question:Paired pulse transcranial magnetic stimulation(ppTMS) is a common approach to probe cortical excitatory and inhi-bitory processes [a]. ppTMS paradigms can be classified by the inter-stimulus interval (ISI) to long and short interval ppTMS. It isassumed, that short interval ppTMS phenomena such as short-inter-val cortical inhibition phenomenon (SICI) are rather focal and arediscussed in terms of surround inhibition in the motor cortex [b].Longer interval ppTMS phenomena are believed to be based on morewidespread trans-synaptic mechanisms[c]. The topographic aspectsof these ppTMS phenomena are not fully understood yet. The associ-ation of the various ppTMS phenomena studied in different musclesof the same limb remains unexplored. The aim of this study was toassess the interaction of ppTMS phenomena, probed in differentupper limb muscles. We hypothesized that the correlation amongthe different hand muscles within the ppTMS phenomenon willdepend on the ppTMS ISI.Methods:19 healthy right-handed volunteers participated in thestudy (15 females, 18–30 y.o.). Four ppTMS phenomena (SICI/LICI –short-interval/long-interval intracortical inhibition, SICF – short nterval intracortical facilitation and ICF – intracortical facilitation)were probed using navigated TMS (MagPro X100, Localite TMSNavigator) applied at the APB hotspot of the left primary motor cor-tex. We registered motor evoked potentials (MEPs) from four rightupper limb muscles: abductor pollicis brevis (APB), extensor digito-rum communis (EDC), abductor digiti minimi (ADM) and biceps bra-chii (BB). Spearman’s rank correlation coefficient was calculated toevaluate an association of inter-muscle ppTMS phenomena.Multiple comparisons were controlled with the FDR. For the finalanalysis we considered only correlations between spatially sepa-rated muscles with sufficiently high amplitudes of MEPs during sin-gle pulse TMS.Results:SICI, ICF and LICI phenomena correlated significantlyamong all the considered muscles‘ pairs. SICI for APBEDC:r=0.793,p= 0.000; for ADMEDC:r= 0.905,p= 0.000. ICF for APBEDC:r= 0.639,p= 0.000; for ADMEDC:r= 0.639,p= 0.000.SICF for APBEDC:r= 0.423,p> 0.05; for ADMEDC:r= 0.779,p= 0.000. LICI for APBEDC:r= 0.575,p= 0.000; for ADMEDC:r= 0.704,p= 0.004.Conclusion:In agreement with the previous data, we did not finda clear association among different ppTMS phenomena. In contrastto our hypothesis, there is no link between ISI and the correlationof the same ppTMS phenomenon among muscles. The lack of thecorrelation between APB and EDC in SICF might be explained bySICF peculiar mechanism through the superposition of D-and I-waves [d]; and by a specialized functionality of the thenar, compar-ing to hypothenar muscles, – the idea which should be further ver-ified in the following experiments.
Individuals with ASD have been shown to have different pattern of functional connectivity. In this study, brain activity of participants with many and few autistic traits, was recorded using an fNIRS device, as participants preformed an interpersonal synchronization task. This type of task involves synchronization and functional connectivity of different brain regions. A novel method for assessing signal complexity, using ε-complexity coefficients, applied for the first i.e. on fNIRS recording, was used to classify brain recording of participants with many/few autistic traits. Successful classification was achieved implying that this method may be useful for classification of fNIRS recordings and that there is a difference in brain activity between participants with low and high autistic traits as they perform an interpersonal synchronization task.
Objective: Feedback latency was shown to be a critical parameter in a range of applications that imply learning. The therapeutic effects of neurofeedback (NFB) remain controversial. We hypothesized that often encountered unreliable results of NFB intervention could be associated with large feedback latency values that are often uncontrolled and may preclude the efficient learning. Approach: We engaged our subjects into a parietal alpha power unpregulating paradigm faciliated by visual neurofeedback based on the invidually extracted envelope of the alpha-rhythm at P4 electrode. NFB was displayed either as soon as EEG envelope was processed, or with an extra 250 or 500-ms delay. The feedback training consisted of 15 two-minute long blocks interleaved with 15s pauses. We have also recorded two minute long baselines immediately before and after the training. Main results: The time course of NFB-induced changes in the alpha rhythm power clearly depended on NFB latency, as shown with the adaptive Neyman test. NFB had a strong effect on the alpha-spindle incidence rate, but not on their duration or amplitude. The sustained changes in alpha activity measured after the completion of NFB training were negatively correlated to latency, with the maximum change for the shortest tested latency and no change for the longest. Significance: Here we for the first time show that visual NFB of parietal electroencephalographic (EEG) alpha-activity is efficient only when delivered to human subjects at short latency, which guarantees that NFB arrives when an alpha spindle is still ongoing. Such a considerable effect of NFB latency on the alpha-activity temporal structure could explain some of the previous inconsistent results, where latency was neither controlled nor documented. Clinical practitioners and manufacturers of NFB equipment should add latency to their specifications while enabling latency monitoring and supporting short-latency operations.
Hybrid materials based on perfluorinated sulfonic acid Nafion-type membranes and poly-3,4-ethylenedioxythiophene (PEDOT) with a gradient distribution of the latter along the film length were synthesized by in situ oxidative polymerization. The initial monomer concentration (0.01 and 0.002 M) and the concentration ratio of the monomer to the oxidant (1/1.25 and 1/2.5) were varied. We studied the effect of the equilibrium and transport properties of the obtained materials on the characteristics of cross-sensitive DP-sensors (analytical signal is the Donnan potential) in aqueous solutions of procaine, lidocaine, and bupivacaine hydrochlorides, including those containing sodium chloride, in a concentration range from 1.0 × 10–4 to 1.0 × 10–2 M and pH from 2 to 6. The relative error in determining the active substance in the Novokain preparation using a DP-sensor based on the Nafion/PEDOT membrane (0.002 M, 1/2.5) was 0.4%. An array of DP-sensors based on Nafion and Nafion/PEDOT (0.002 M, 1/1.25) membranes was used to determine bupivacaine hydrochloride and sodium chloride in the Markain® Spinal preparation with an error of 11 and 6%, respectively.
The analogy between knots in mathematical knot theory and “knots” in medicine is studied for the example of a certain invariant of Legendrian knots. The connection between the “Chern class” and the “Maslov class” is analyzed. A conjecture on the connection between the “Connes–Chern character” and the “Maslov class” is proposed.
Cerebrovascular imaging of rodents is one of the trending applications of optoacoustics aimed at studying brain activity and pathology. Imaging of deep brain structures is often hindered by sub-optimal arrangement of the light delivery and acoustic detection systems. In our work we revisit the physics behind opto-acoustic signal generation for theoretical evaluation of optimal laser wavelengths to perform cerebrovascular optoacoustic angiography of rodents beyond the penetration barriers imposed by light diffusion in highly scattering and absorbing brain tissues. A comprehensive model based on diffusion approximation was developed to simulate optoacoustic signal generation using optical and acoustic parameters closely mimicking a typical murine brain. The model revealed three characteristic wavelength ranges in the visible and near-infrared spectra optimally suited for imaging cerebral vasculature of different size and depth. The theoretical conclusions are confirmed by numerical simulations while in vivo imaging experiments further validated the ability to accurately resolve brain vasculature at depths ranging between 0.7 and 7 mm.