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Herrero, J. L.

Publications and source records attributed to Herrero, J. L..

4 recordsLinked to original sources

Joint population coding and temporal coherence link an attended talker's voice and location features in naturalistic multi-talker scenes.

Listeners effortlessly extract multidimensional auditory objects, such as a localized talker, from complex acoustic scenes. However, the neural mechanisms that enable simultaneous encoding and linking of distinct sound features--such as a talkers voice and location--are not fully understood. Using invasive intracranial recordings in neurosurgical patients, we investigated how the human auditory cortex processes and integrates these features during naturalistic multi-talker scenes. We found that cortical sites exhibit a gradient of feature sensitivity, ranging from single-feature sensitive sites (responsive primarily to voice or location) to dual-feature sensitive sites (responsive to both features). At the population level, neural response patterns from both single- and dual-feature sensitive sites jointly encoded the attended talkers voice and location. Notably, single-feature sensitive sites encoded their primary feature with greater precision but also represented coarse information about the secondary feature. Sites selectively tracking a single, attended speech stream concurrently encoded both voice and location features, demonstrating a link between selective attention and feature integration. Additionally, attention selectively enhanced temporal coherence between voice- and location-sensitive sites, suggesting that temporal synchronization serves as a mechanism for linking these features. Our findings highlight two complementary neural mechanisms--joint population coding and temporal coherence-- that enable the integration of voice and location features in the auditory cortex. These results provide new insights into the distributed, multidimensional nature of auditory object formation during active listening in complex environments. SIGNIFICANCE STATEMENTIn everyday life, listeners effortlessly extract individual sound sources from complex acoustic scenes which contain multiple sound sources. Yet, how the brain links the different features of a particular sound source to each other - such as a talkers voice characteristics and location - is poorly understood. Here, we show that two neural mechanisms contribute to encoding and integrating voice and location features in multi-talker sound scenes: (1) some neuronal sites are sensitive to both voice and location and their activity patterns encode these features jointly; (2) the responses of neuronal sites that process only one sound feature - that is, location or voice - align temporally to form a stream that is segregated from the other talker. HIGHLIGHTSO_LIAuditory cortex exhibits a gradient of feature sensitivity, with some sites encoding only voice or location features, while others encode both simultaneously (dual-feature sensitive sites). C_LIO_LIDual-feature sensitive sites integrate voice and location features of an attended talker with equal accuracy, providing a unified representation in multi-talker scenes. C_LIO_LISingle-feature sensitive sites primarily encode their preferred feature with high precision but also represent coarse information about other features, contributing to population-level integration. C_LIO_LITemporal coherence selectively enhances synchronization between voice- and location-sensitive sites, providing another mechanism for integrating an auditory objects features. C_LIO_LIMulti-dimensional auditory object formation relies on complementary neural mechanisms: joint population coding and temporal coherence. C_LI

neuroscience↗

Enhancing neuronal plasticity through intracranial theta burst stimulation in the human sensorimotor cortex.

The progress of therapeutic neuromodulation greatly depends on improving stimulation parameters to most efficiently induce neuroplasticity effects. Intermittent Theta Burst stimulation (iTBS), a form of electrical stimulation that mimics the natural brain activity patterns, has shown efficacy in inducing such effects in animal studies and rhythmic Transcranial Magnetic Stimulation (rTMS) studies in humans. However, little is known about the potential neuroplasticity effects of iTBS applied through intracranial electrodes in humans which could have implications for deep brain stimulation therapies. This study characterizes the physiological effects of cortical iTBS in the human cortex and compare them with single pulse alpha stimulation, another frequently used paradigm in rTMS research. We applied these stimulation paradigms to well-defined regions in the sensorimotor cortex which elicited contralateral hand or arm muscle contractions during electrical stimulation mapping in epilepsy patients implanted with intracranial electrodes. Treatment effects were evaluated using effective connectivity and beta oscillations coherence measures in areas connected to the treatment site as defined with cortico-cortical evoked potentials. Our results show that iTBS increases beta band synchronization within the sensorimotor network indicating a potential neuroplasticity effect. The effect is specific to the sensorimotor system, the beta frequency band and the stimulation pattern (no effect was found with single-pulse alpha stimulation). The effects outlasted the stimulation by three minutes. By characterizing the neurophysiological effects of iTBS within well-defined cortical networks, we hope to provide an electrophysiological framework that allows clinicians and researchers to optimize brain stimulation protocols which may have translational value.

neuroscience↗

Intracortical dynamics underlying repetitive stimulation predicts changes in network connectivity

Targeted stimulation can be used to modulate the activity of brain networks. Previously we demonstrated that direct electrical stimulation produces predictable post-stimulation changes in brain excitability. However, understanding the neural dynamics during stimulation and its relationship to post-stimulation effects is limited but critical for treatment optimization. Here, we applied 10Hz direct electrical stimulation across several cortical regions in 14 patients implanted with intracranial electrodes for seizure monitoring. The stimulation train was characterized by a consistent increase in high gamma (70-170Hz) power. Immediately post-train, low-frequency (1-8Hz) power increased, resulting in an evoked response that was highly correlated with the neural response during stimulation. Using two measures of network connectivity, cortico-cortical evoked potentials (indexing effective connectivity) and theta coherence (indexing functional connectivity), we found a stronger response to stimulation in regions that were highly connected to the stimulation site. In these regions, repeated cycles of stimulation trains and rest progressively altered the stimulation response. Finally, after just 2 minutes (10%) of repetitive stimulation, we were able to predict post-stimulation connectivity changes with high discriminability. Taken together, this work reveals a relationship between stimulation dynamics and post-stimulation connectivity changes in humans. Thus, measuring neural activity during stimulation can inform future plasticity-inducing protocols.

neuroscience↗

Deep Convolutional modeling of human face selective columns reveals their role in pictorial face representation

Despite the massive accumulation of systems neuroscience findings, their functional meaning remains tentative, largely due to the absence of realistically performing models. The discovery that deep convolutional networks achieve human performance in realistic tasks offers fresh opportunities for such modeling. Here we show that the face-space topography of face-selective columns recorded intra-cranially in 32 patients significantly matches that of a DCNN having human-level face recognition capabilities. Three modeling aspects converge in pointing to a role of human face areas in pictorial rather than person identification: First, the match was confined to intermediate layers of the DCNN. Second, identity preserving image manipulations abolished the brain to DCNN correlation. Third, DCNN neurons matching face-column tuning displayed view-point selective receptive fields. Our results point to a \"convergent evolution\" of pattern similarities in biological and artificial face perception. They demonstrate DCNNs as a powerful modeling approach for deciphering the function of human cortical networks.

neuroscience↗