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1. Algorithms for Estimating Time-Locked Neural Response Components in Cortical Processing of Continuous Speech

   https://doi.org/10.1109/TBME.2022.3185005  

   Kulasingham, Joshua P. ; Simon, Jonathan Z. ( June 2022 , IEEE Transactions on Biomedical Engineering)

   Objective: The Temporal Response Function (TRF) is a linear model of neural activity time-locked to continuous stimuli, including continuous speech. TRFs based on speech envelopes typically have distinct components that have provided remarkable insights into the cortical processing of speech. However, current methods may lead to less than reliable estimates of single-subject TRF components. Here, we compare two established methods, in TRF component estimation, and also propose novel algorithms that utilize prior knowledge of these components, bypassing the full TRF estimation. Methods: We compared two established algorithms, ridge and boosting, and two novel algorithms based on Subspace Pursuit (SP) and Expectation Maximization (EM), which directly estimate TRF components given plausible assumptions regarding component characteristics. Single-channel, multi-channel, and source-localized TRFs were fit on simulations and real magnetoencephalographic data. Performance metrics included model fit and component estimation accuracy. Results: Boosting and ridge have comparable performance in component estimation. The novel algorithms outperformed the others in simulations, but not on real data, possibly due to the plausible assumptions not actually being met. Ridge had slightly better model fits on real data compared to boosting, but also more spurious TRF activity. Conclusion: Results indicate that both smooth (ridge) and sparse (boosting) algorithms perform comparablymore »at TRF component estimation. The SP and EM algorithms may be accurate, but rely on assumptions of component characteristics. Significance: This systematic comparison establishes the suitability of widely used and novel algorithms for estimating robust TRF components, which is essential for improved subject-specific investigations into the cortical processing of speech.« less
2. Time-locked auditory cortical responses in the high-gamma band: A window into primary auditory cortex

   https://doi.org/10.3389/fnins.2022.1075369  

   Simon, Jonathan Z. ; Commuri, Vrishab ; Kulasingham, Joshua P. ( December 2022 , Frontiers in Neuroscience)

   Primary auditory cortex is a critical stage in the human auditory pathway, a gateway between subcortical and higher-level cortical areas. Receiving the output of all subcortical processing, it sends its output on to higher-level cortex. Non-invasive physiological recordings of primary auditory cortex using electroencephalography (EEG) and magnetoencephalography (MEG), however, may not have sufficient specificity to separate responses generated in primary auditory cortex from those generated in underlying subcortical areas or neighboring cortical areas. This limitation is important for investigations of effects of top-down processing (e.g., selective-attention-based) on primary auditory cortex: higher-level areas are known to be strongly influenced by top-down processes, but subcortical areas are often assumed to perform strictly bottom-up processing. Fortunately, recent advances have made it easier to isolate the neural activity of primary auditory cortex from other areas. In this perspective, we focus on time-locked responses to stimulus features in the high gamma band (70–150 Hz) and with early cortical latency (∼40 ms), intermediate between subcortical and higher-level areas. We review recent findings from physiological studies employing either repeated simple sounds or continuous speech, obtaining either a frequency following response (FFR) or temporal response function (TRF). The potential roles of top-down processing are underscored, and comparisons withmore »invasive intracranial EEG (iEEG) and animal model recordings are made. We argue that MEG studies employing continuous speech stimuli may offer particular benefits, in that only a few minutes of speech generates robust high gamma responses from bilateral primary auditory cortex, and without measurable interference from subcortical or higher-level areas.« less
3. NLGC: Network Localized Granger Causality with Application to MEG Directional Functional Connectivity Analysis

   https://doi.org/10.1016/j.neuroimage.2022.119496  

   Soleimani, B. ; Karunathilake, I. M. ; Kuchinsky, S. E. ; Simon, J. Z. ; Babadi, B. ( January 2022 , NeuroImage)

   Identifying the directed connectivity that underlie networked activity between different cortical areas is critical for understanding the neural mechanisms behind sensory processing. Granger causality (GC) is widely used for this purpose in functional magnetic resonance imaging analysis, but there the temporal resolution is low, making it difficult to capture the millisecond-scale interactions underlying sensory processing. Magne- toencephalography (MEG) has millisecond resolution, but only provides low-dimensional sensor-level linear mixtures of neural sources, which makes GC inference challenging. Conventional methods proceed in two stages: First, cortical sources are estimated from MEG using a source localization technique, followed by GC inference among the estimated sources. However, the spatiotemporal biases in estimating sources propagate into the subsequent GC analysis stage, may result in both false alarms and missing true GC links. Here, we introduce the Network Localized Granger Causality (NLGC) inference paradigm, which models the source dynamics as latent sparse multivariate autoregressive processes and estimates their parameters directly from the MEG measurements, integrated with source localization, and employs the resulting parameter estimates to produce a precise statistical characterization of the detected GC links. We offer several theoretical and algorithmic innovations within NLGC and further examine its utility via comprehensive simulations and application to MEGmore »data from an auditory task involving tone processing from both younger and older participants. Our simulation studies reveal that NLGC is markedly robust with respect to model mismatch, network size, and low signal-to-noise ratio, whereas the conventional two-stage methods result in high false alarms and mis-detections. We also demonstrate the advantages of NLGC in revealing the cortical network- level characterization of neural activity during tone processing and resting state by delineating task- and age-related connectivity changes.« less
4. tRFtarget: a database for transfer RNA-derived fragment targets

   https://doi.org/10.1093/nar/gkaa831  

   Li, Ningshan ; Shan, Nayang ; Lu, Lingeng ; Wang, Zuoheng ( October 2020 , Nucleic Acids Research)

   Abstract Transfer RNA-derived fragments (tRFs) are a new class of small non-coding RNAs and play important roles in biological and physiological processes. Prediction of tRF target genes and binding sites is crucial in understanding the biological functions of tRFs in the molecular mechanisms of human diseases. We developed a publicly accessible web-based database, tRFtarget (http://trftarget.net), for tRF target prediction. It contains the computationally predicted interactions between tRFs and mRNA transcripts using the two state-of-the-art prediction tools RNAhybrid and IntaRNA, including location of the binding sites on the target, the binding region, and free energy of the binding stability with graphic illustration. tRFtarget covers 936 tRFs and 135 thousand predicted targets in eight species. It allows researchers to search either target genes by tRF IDs or tRFs by gene symbols/transcript names. We also integrated the manually curated experimental evidence of the predicted interactions into the database. Furthermore, we provided a convenient link to the DAVID® web server to perform downstream functional pathway analysis and gene ontology annotation on the predicted target genes. This database provides useful information for the scientific community to experimentally validate tRF target genes and facilitate the investigation of the molecular functions and mechanisms of tRFs.
5. tatDB: a database of Ago1-mediated targets of transfer RNA fragments

   https://doi.org/10.1093/nar/gkac1018  

   Guan, Lingyu ; Grigoriev, Andrey ( November 2022 , Nucleic Acids Research)

   tRNA-derived fragments (tRFs) are a class of emerging post-transcriptional regulators of gene expression likely binding to the transcripts of target genes. However, only a few tRFs targets have been experimentally validated, making it hard to extrapolate the functions or binding mechanisms of tRFs. The paucity of resources supporting the identification of the targets of tRFs creates a bottleneck in the fast-developing field. We have previously analyzed chimeric reads in crosslinked Argonaute1-RNA complexes to help infer the guide-target pairs and binding mechanisms of multiple tRFs based on experimental data in human HEK293 cells. To efficiently disseminate these results to the research community, we designed a web-based database tatDB (targets of tRFs DataBase) populated with close to 250 000 experimentally determined guide-target pairs with ∼23 000 tRF isoforms. tatDB has a user-friendly interface with flexible query options/filters allowing one to obtain comprehensive information on given tRFs (or targets). Modes of interactions are supported by secondary structures of potential guide-target hybrids and binding motifs, essential for understanding the targeting mechanisms of tRFs. Further, we illustrate the value of the database on an example of hypothesis-building for a tRFs potentially involved in the lifecycle of the SARS-CoV-2 virus. tatDB is freely accessible atmore »https://grigoriev-lab.camden.rutgers.edu/tatdb.

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