Octave Etard

SignificanceAchieving adequate brain sensitivity remains a significant obstacle to noninvasive optical measurements of pulsatile cerebral blood flow. Increasing source-detector separation (SDS), utilizing time-of-flight (ToF) information, and/or increasing acquisition frequency can increase brain sensitivity. However, optimizing these parameters is nontrivial and must balance the need to achieve sufficient signal-to-noise ratio.AimWe aim to guide hardware optimization by evaluating the benefits of ToF gating, autocorrelation time-lag gating, and increased source-detector separation in recovering pulsatile cerebral blood flow.ApproachMonte Carlo simulations of ToF-resolved pulsatile blood flow index at 1064 nm were performed. A simulation and analysis pipeline is presented and validated against phantom and in vivo measurements.ResultsBrain sensitivity deteriorates for autocorrelation time-lags >1 to 10 μs. Continuous-wave diffuse correlation spectroscopy (CW-DCS) at 40 mm SDS achieves equivalent brain sensitivity to time-resolved measurements with a 1.2 ns ToF gate. For median adult brain depths, a 31 mm SDS (CW-DCS), 35 mm SDS (speckle contrast optical spectroscopy), or 0.9 ns ToF are needed for brain-dominated signals. For the 85th percentile of brain depth, these thresholds increase to 40 mm, 46 mm, and 1.2 ns, respectively.ConclusionsWe quantitatively compare source-detector separation, ToF gating, and acquisition frequency and identify crucial performance requirements for clinically viable optical monitors of cerebral blood flow.

SignificanceContinuous, noninvasive monitoring of cerebral blood flow (CBF) at the bedside is a critical unmet clinical need and a long-standing goal of biomedical optics. Despite extensive development, no optical measurement of CBF has achieved routine clinical use.AimWe aim to demonstrate and validate CoMind R1, a time-resolved interferometric neuromonitoring system designed to noninvasively measure CBF at pulsatile rates.ApproachCoMind R1 integrates numerous advances, including a high-power, linearly swept 1064 nm laser, multimode collection, parallelized detection, and real-time processing. Performance is evaluated with homogeneous and dynamic bi-layer phantoms and in vivo studies of adults at rest and during visual stimulation.ResultsCoMind R1 achieves an instrument response function width of 120 ps and exhibits no significant drift over 24 h. In vivo data from 25 adults show consistent pulsatile blood flow waveforms at times-of-flight averaging 1.2 ns, exceeding the prior art. Visual stimulation produces significant, time-of-flight-dependent hyperaemic responses, demonstrating brain sensitivity and selectivity.ConclusionsCoMind R1 consistently achieves time-of-flight resolved measurements of pulsatile blood flow at the late times-of-flight necessary to ensure a brain-dominated signal in the typical adult. CoMind R1 thus provides cerebral sensitivity and tissue specificity in a scalable, cost-effective architecture and advances optical monitoring of CBF toward clinical translation.

Speech and music are spectro-temporally complex acoustic signals that a highly relevant for humans. Both contain a temporal fine structure that is encoded in the neural responses of subcortical and cortical processing centres. The subcortical response to the temporal fine structure of speech has recently been shown to be modulated by selective attention to one of two competing voices. Music similarly often consists of several simultaneous melodic lines, and a listener can selectively attend to a particular one at a time. However, the neural mechanisms that enable such selective attention remain largely enigmatic, not least since most investigations to date have focussed on short and simplified musical stimuli. Here we study the neural encoding of classical musical pieces in human volunteers, using scalp electroencephalography (EEG) recordings. We presented volunteers with continuous musical pieces composed of one or two instruments. In the latter case, the participants were asked to selectively attend to one of the two competing instruments and to perform a vibrato identification task. We used linear encoding and decoding models to relate the recorded EEG activity to the stimulus waveform. We show that we can measure neural responses to the temporal fine structure of melodic lines played by one single instrument, at the population level as well as for most individual subjects. The neural response peaks at a latency of 7.6 ms and is not measurable past 15 ms. When analysing the neural responses elicited by competing instruments, we find no evidence of attentional modulation. Our results show that, much like speech, the temporal fine structure of music is tracked by neural activity. In contrast to speech, however, this response appears unaffected by selective attention in the context of our experiment.

Humans excel at understanding speech even in adverse conditions such as background noise. Speech processing may be aided by cortical activity in the delta and theta frequency bands, which have been found to track the speech envelope. However, the rhythm of non-speech sounds is tracked by cortical activity as well. It therefore remains unclear which aspects of neural speech tracking represent the processing of acoustic features, related to the clarity of speech, and which aspects reflect higher-level linguistic processing related to speech comprehension. Here we disambiguate the roles of cortical tracking for speech clarity and comprehension through recording EEG responses to native and foreign language in different levels of background noise, for which clarity and comprehension vary independently. We then use a both a decoding and an encoding approach to relate clarity and comprehension to the neural responses. We find that cortical tracking in the theta frequency band is mainly correlated to clarity, whereas the delta band contributes most to speech comprehension. Moreover, we uncover an early neural component in the delta band that informs on comprehension and that may reflect a predictive mechanism for language processing. Our results disentangle the functional contributions of cortical speech tracking in the delta and theta bands to speech processing. They also show that both speech clarity and comprehension can be accurately decoded from relatively short segments of EEG recordings, which may have applications in future mind-controlled auditory prosthesis. SIGNIFICANCE STATEMENT Speech is a highly complex signal whose processing requires analysis from lower-level acoustic features to higher-level linguistic information. Recent work has shown that neural activity in the delta and theta frequency bands track the rhythm of speech, but the role of this tracking for speech processing remains unclear. Here we disentangle the roles of cortical entrainment in different frequency bands and at different temporal lags for speech clarity, reflecting the acoustics of the signal, and speech comprehension, related to linguistic processing. We show that cortical speech tracking in the theta frequency band encodes mostly speech clarity, and thus acoustic aspects of the signal, whereas speech tracking in the delta band encodes the higher-level speech comprehension.

Humans are highly skilled at analysing complex acoustic scenes. The segregation of different acoustic streams and the formation of corresponding neural representations is mostly attributed to the auditory cortex. Decoding of selective attention from neuroimaging has therefore focussed on cortical responses to sound. However, the auditory brainstem response to speech is modulated by selective attention as well, as recently shown through measuring the brainstem’s response to running speech. Although the response of the auditory brainstem has a smaller magnitude than that of the auditory cortex, it occurs at much higher frequencies and therefore has a higher information rate. Here we develop statistical models for extracting the brainstem response from multi-channel scalp recordings and for analysing the attentional modulation according to the focus of attention. We demonstrate that the attentional modulation of the brainstem response to speech can be employed to decode the attentional focus of a listener from short measurements of ten seconds or less in duration. The decoding remains accurate when obtained from three EEG channels only. We further show how out-of-the-box decoding that employs subject-independent models, as well as decoding that is independent of the specific attended speaker is capable of achieving similar accuracy. These results open up new avenues for investigating the neural mechanisms for selective attention in the brainstem and for developing efficient auditory brain-computer interfaces.

Humans excel at selectively listening to a target speaker in background noise such as competing voices. While the encoding of speech in the auditory cortex is modulated by selective attention, it remains debated whether such modulation occurs already in subcortical auditory structures. Investigating the contribution of the human brainstem to attention has, in particular, been hindered by the tiny amplitude of the brainstem response. Its measurement normally requires a large number of repetitions of the same short sound stimuli, which may lead to a loss of attention and to neural adaptation. Here we develop a mathematical method to measure the auditory brainstem response to running speech, an acoustic stimulus that does not repeat and that has a high ecological validity. We employ this method to assess the brainstem's activity when a subject listens to one of two competing speakers, and show that the brainstem response is consistently modulated by attention.

Humans excel at selectively listening to a target speaker in background noise such as competing voices. While the encoding of speech in the auditory cortex is modulated by selective attention, it remains debated whether such modulation occurs already in subcortical auditory structures. Investigating the contribution of the human brainstem to attention has, in particular, been hindered by the tiny amplitude of the brainstem response. Its measurement normally requires a large number of repetitions of the same short sound stimuli, which may lead to a loss of attention and to neural adaptation. Here we develop a mathematical method to measure the auditory brainstem response to running speech, an acoustic stimulus that does not repeat and that has a high ecological validity. We employ this method to assess the brainstem’s activity when a subject listens to one of two competing speakers, and show that the brainstem response is consistently modulated by attention.

We consider a plant’s local leaf area index as a spatially continuous variable, subject to particular reaction–diffusion dynamics of allocation, senescence and spatial propagation. The latter notably incorporates the plant’s tendency to form new leaves in bright rather than shaded locations. Applying a generalized Beer–Lambert law allows to link existing foliage to production dynamics. The approach allows for inter-individual variability and competition for light while maintaining robustness—a key weakness of comparable existing models. The analysis of the single plant case leads to a significant simplification of the system’s key equation when transforming it into the well studied porous medium equation. Confronting the theoretical model to experimental data of sugar beet populations, differing in configuration density, demonstrates its accuracy.