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Lopez Valdes, A.

Publications and source records attributed to Lopez Valdes, A..

3 recordsLinked to original sources

PolyGraph - Flexible, Biocompatible & Electrically Optimised Graphene-Polymer Composites for Next-Generation Neural Interfaces

Neural interfacing materials must deliver exceptional electrochemical performance, while integrating safely with the central nervous system. In this study we develop PolyGraph, a flexible, conductive, and biocompatible graphene-polycaprolactone (PCL) nanocomposite designed to strike this balance, which enables fabrication of conformable multichannel microelectrode arrays. Optimised liquid-phase exfoliation produces conductive, biocompatible PVP-stabilised graphene nanosheets, which are incorporated into PCL to form flexible, processable composites - PolyGraph. This material demonstrates bio- and immuno-compatibility with sensitive primary and iPSC-derived neuronal and glial cells. PolyGraph achieves low impedance ([~]1.6 {Omega} cm2 @ 1 kHz) and high charge injection capacity (11.7 mC/cm2 for a 100 ms pulse), enhanced by NaOH surface roughening and AuPd coating. Leveraging their processability, PolyGraph composites are fabricated into flexible, individually isolated microneedle electrode arrays with biomimetic soft hyaluronic acid backings. These arrays demonstrate bidirectional neural interfacing capabilities, enabling both the delivery of controlled stimulation pulses in physiological buffer and high-resolution neuronal recording in murine brain slices, with machine learning-based event classification. Together, these advances establish PolyGraph as an optimal material platform for next-generation brain-computer interfaces and soft bioelectronic devices. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=109 SRC="FIGDIR/small/673516v1_ufig1.gif" ALT="Figure 1"> View larger version (53K): org.highwire.dtl.DTLVardef@9beea5org.highwire.dtl.DTLVardef@150be08org.highwire.dtl.DTLVardef@1ec24d2org.highwire.dtl.DTLVardef@658209_HPS_FORMAT_FIGEXP M_FIG C_FIG Graphical Abstract & TOC Text PolyGraph, a flexible graphene-polycaprolactone nanocomposite, unites conductivity, biocompatibility, and processability for next-generation neural interfaces. Fabricated into microneedle arrays with ultra-flexible backings, PolyGraph enables bidirectional neuronal recording and stimulation in brain tissue, advancing brain-computer interface (BCI) and soft bioelectronic applications.

bioengineering↗

Simultaneous cortical tracking of competing speech streams during attention switching

Successful speech communication in multi-talker scenarios requires a skilful combination of sustained attention and rapid attention switching. While the neurophysiology literature offers detailed insights into the neural underpinnings of sustained attention, there remains considerable uncertainty on how attention switching takes place. In this study, using EEG recordings from normal-hearing adults in an immersive multi-talker environment, we measured the neural encoding of two competing speech streams amid background babble. Participants were cued to switch attention between streams every 15-30 seconds. Neural tracking was assessed via Temporal Response Functions (TRF), confirming reliable decoding of attentional focus. Our results indicate asymmetric disengagement and engagement processes during attention switches, where the neural tracking of the new target stream emerges before disengaging from the previous target, revealing a transient simultaneous encoding of two speech streams. That transition was closely mirrored by a reduction in EEG alpha power, informing on the cognitive effort during different phases of the attention switch. We then isolated cortical activity reflecting lexical prediction mechanisms to determine how lexical context is updated after an attention switch, comparing four numerical hypotheses that were constructed using Large Language Models. Our findings elucidate both the temporal and contextual mechanisms underlying auditory attention shifts, pointing to the possibility that listeners carry out a reset in lexical context after switching attention. By focusing on dynamic attentional reallocation, this study offers insights into the brains capacity for flexible speech processing in complex listening environments.

neuroscience↗

Cortical over-representation of phonetic onsets of ignored speech in hearing impaired individuals

Hearing impairment alters the sound input received by the human auditory system, reducing speech comprehension in noisy multi-talker auditory scenes. Despite such challenges, attentional modulation on the envelope tracking in multi-talker scenarios is comparable between normal hearing (NH) and hearing impaired (HI) participants, with previous research suggesting an over-representation of the speech envelopes in HI individuals (see, e.g., Fuglsang et al. 2020 and Presacco et al. 2019), even though HI participants reported difficulties in performing the task. This result raises an important question: What speech-processing stage could reflect the difficulty in attentional selection, if not envelope tracking? Here, we use scalp electroencephalography (EEG) to test the hypothesis that such difficulties are underpinned by an over-representation of phonological-level information of the ignored speech sounds. To do so, we carried out a re-analysis of an EEG dataset where EEG signals were recorded as HI participants fitted with hearing aids attended to one speaker (target) while ignoring a competing speaker (masker) and spatialised multi-talker background noise. Multivariate temporal response function analyses revealed that EEG signals reflect stronger phonetic-feature encoding for target than masker speech streams. Interestingly, robust EEG encoding of phoneme onsets emerged for both target and masker streams, in contrast with previous work on NH participants and in line with our hypothesis of an over-representation of the masker. Stronger phoneme-onset encoding emerged for the masker, pointing to a possible neural basis for the higher distractibility experienced by HI individuals. Significance StatementThis study investigated the neural underpinnings of attentional selection in multi-talker scenarios in hearing-impaired participants. The impact of attentional selection on phonological encoding was assessed with electroencephalography (EEG) in an immersive multi-talker scenario. EEG signals encoded the phonetic features of the target (attended) speech more strongly than those of the masker (ignored) speech; but interestingly, they encoded the phoneme onsets of both target and masker speech. This suggests that the cortex of hearing-impaired individuals may over-represent higher-level features of ignored speech sounds, which could contribute to their higher distractibility in noisy environments. These findings provide insight into the neural mechanisms underlying speech comprehension in hearing-impaired individuals and could inform the development of novel approaches to improve speech perception in noisy environments.

neuroscience↗