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Carrying out any everyday task, be it driving in traffic, conversing with friends or playing basketball, requires rapid selection, integration and segregation of stimuli from different sensory modalities. At present, even the most advanced artificial intelligence-based systems are unable to replicate the multisensory processes that the human brain routinely performs, but how neural circuits in the brain carry out these processes is still not well understood. In this Perspective, we discuss recent findings that shed fresh light on the oscillatory neural mechanisms that mediate multisensory integration (MI), including power modulations, phase resetting, phase-amplitude coupling and dynamic functional connectivity. We then consider studies that also suggest multi-timescale dynamics in intrinsic ongoing neural activity and during stimulus-driven bottom-up and cognitive top-down neural network processing in the context of MI. We propose a new concept of MI that emphasizes the critical role of neural dynamics at multiple timescales within and across brain networks, enabling the simultaneous integration, segregation, hierarchical structuring and selection of information in different time windows. To highlight predictions from our multi-timescale concept of MI, real-world scenarios in which multi-timescale processes may coordinate MI in a flexible and adaptive manner are considered.
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Recent research has extensively reported the phenomenon of inter-brain neural coupling between speakers and listeners during speech communication. Yet, the specific speech processes underlying this neural coupling remain elusive. To bridge this gap, this study estimated the correlation between the temporal dynamics of speaker-listener neural coupling with speech features, utilizing two inter-brain datasets accounting for different noise levels and listener's language experiences (native vs. non-native). We first derived time-varying speaker-listener neural coupling, extracted acoustic feature (envelope) and semantic features (entropy and surprisal) from speech, and then explored their correlational relationship. Our findings reveal that in clear conditions, speaker-listener neural coupling correlates with semantic features. However, as noise increases, this correlation is only significant for native listeners. For non-native listeners, neural coupling correlates predominantly with acoustic feature rather than semantic features. These results revealed how speaker-listener neural coupling is associated with the acoustic and semantic features under various scenarios, enriching our understanding of the inter-brain neural mechanisms during natural speech communication. We therefore advocate for more attention on the dynamic nature of speaker-listener neural coupling and its modeling with multilevel speech features.
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Encéfalo , Semántica , Percepción del Habla , Humanos , Percepción del Habla/fisiología , Femenino , Masculino , Adulto , Encéfalo/fisiología , Adulto Joven , Habla/fisiología , Electroencefalografía/métodosRESUMEN
Shannon Information theory has long been a tool of choice to measure empirically how populations of neurons in the brain encode information about cognitive variables. Recently, Partial Information Decomposition (PID) has emerged as principled way to break down this information into components identifying not only the unique information carried by each neuron, but also whether relationships between neurons generate synergistic or redundant information. While it has been long recognized that Shannon information measures on neural activity suffer from a (mostly upward) limited sampling estimation bias, this issue has largely been ignored in the burgeoning field of PID analysis of neural activity. We used simulations to investigate the limited sampling bias of PID computed from discrete probabilities (suited to describe neural spiking activity). We found that PID suffers from a large bias that is uneven across components, with synergy by far the most biased. Using approximate analytical expansions, we found that the bias of synergy increases quadratically with the number of discrete responses of each neuron, whereas the bias of unique and redundant information increase only linearly or sub-linearly. Based on the understanding of the PID bias properties, we developed simple yet effective procedures that correct for the bias effectively, and that improve greatly the PID estimation with respect to current state-of-the-art procedures. We apply these PID bias correction procedures to datasets of 53117 pairs neurons in auditory cortex, posterior parietal cortex and hippocampus of mice performing cognitive tasks, deriving precise estimates and bounds of how synergy and redundancy vary across these brain regions.
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Parkinson's disease (PD) is characterized by the disruption of repetitive, concurrent and sequential motor actions due to compromised timing-functions principally located in cortex-basal ganglia (BG) circuits. Increasing evidence suggests that motor impairments in untreated PD patients are linked to an excessive synchronization of cortex-BG activity at beta frequencies (13-30 Hz). Levodopa and subthalamic nucleus deep brain stimulation (STN-DBS) suppress pathological beta-band reverberation and improve the motor symptoms in PD. Yet a dynamic tuning of beta oscillations in BG-cortical loops is fundamental for movement-timing and synchronization, and the impact of PD therapies on sensorimotor functions relying on neural transmission in the beta frequency-range remains controversial. Here, we set out to determine the differential effects of network neuromodulation through dopaminergic medication (ON and OFF levodopa) and STN-DBS (ON-DBS, OFF-DBS) on tapping synchronization and accompanying cortical activities. To this end, we conducted a rhythmic finger-tapping study with high-density EEG-recordings in 12 PD patients before and after surgery for STN-DBS and in 12 healthy controls. STN-DBS significantly ameliorated tapping parameters as frequency, amplitude and synchrony to the given auditory rhythms. Aberrant neurophysiologic signatures of sensorimotor feedback in the beta-range were found in PD patients: their neural modulation was weaker, temporally sluggish and less distributed over the right cortex in comparison to controls. Levodopa and STN-DBS boosted the dynamics of beta-band modulation over the right hemisphere, hinting to an improved timing of movements relying on tactile feedback. The strength of the post-event beta rebound over the supplementary motor area correlated significantly with the tapping asynchrony in patients, thus indexing the sensorimotor match between the external auditory pacing signals and the performed taps. PD patients showed an excessive interhemispheric coherence in the beta-frequency range during the finger-tapping task, while under DBS-ON the cortico-cortical connectivity in the beta-band was normalized. Ultimately, therapeutic DBS significantly ameliorated the auditory-motor coupling of PD patients, enhancing the electrophysiological processing of sensorimotor feedback-information related to beta-band activity, and thus allowing a more precise cued-tapping performance.
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Ritmo beta , Sincronización Cortical , Estimulación Encefálica Profunda , Dedos , Levodopa , Corteza Motora , Enfermedad de Parkinson , Núcleo Subtalámico , Humanos , Enfermedad de Parkinson/terapia , Enfermedad de Parkinson/fisiopatología , Masculino , Femenino , Persona de Mediana Edad , Estimulación Encefálica Profunda/métodos , Anciano , Ritmo beta/fisiología , Corteza Motora/fisiopatología , Corteza Motora/fisiología , Sincronización Cortical/fisiología , Levodopa/uso terapéutico , Núcleo Subtalámico/fisiopatología , Antiparkinsonianos/uso terapéutico , ElectroencefalografíaRESUMEN
Attentional control over sensory processing has been linked to neural alpha oscillations and related inhibition of cerebral cortex. Despite the wide consensus on the functional relevance of alpha oscillations for attention, precise neural mechanisms of how alpha oscillations shape perception and how this top-down modulation is implemented in cortical networks remain unclear. Here, we tested the hypothesis that alpha oscillations in frontal eye fields (FEFs) are causally involved in the top-down regulation of visual processing in humans (male and female). We applied sham-controlled, intermittent transcranial alternating current stimulation (tACS) over bilateral FEF at either 10â Hz (alpha) or 40â Hz (gamma) to manipulate attentional preparation in a visual discrimination task. Under each stimulation condition, we measured psychometric functions for contrast perception and introduced a novel linear mixed modeling approach for statistical control of neurosensory side effects of the electric stimulation. tACS at alpha frequency reduced the slope of the psychometric function, resulting in improved subthreshold and impaired superthreshold contrast perception. Side effects on the psychometric functions were complex and showed large interindividual variability. Controlling for the impact of side effects on the psychometric parameters by using covariates in the linear mixed model analysis reduced this variability and strengthened the perceptual effect. We propose that alpha tACS over FEF mimicked a state of endogenous attention by strengthening a fronto-occipitoparietal network in the alpha band. We speculate that this network modulation enhanced phasic gating in occipitoparietal cortex leading to increased variability of single-trial psychometric thresholds, measurable as a reduction of psychometric slope.
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Ritmo alfa , Atención , Estimulación Transcraneal de Corriente Directa , Percepción Visual , Humanos , Femenino , Masculino , Atención/fisiología , Estimulación Transcraneal de Corriente Directa/métodos , Adulto , Percepción Visual/fisiología , Adulto Joven , Ritmo alfa/fisiología , Lóbulo Frontal/fisiología , Estimulación Luminosa/métodos , Campos Visuales/fisiologíaRESUMEN
The mechanisms of semantic conflict and response conflict in the Stroop task have mainly been investigated in the visual modality. However, the understanding of these mechanisms in cross-modal modalities remains limited. In this electroencephalography (EEG) study, an audiovisual 2-1 mapping Stroop task was utilized to investigate whether distinct and/or common neural mechanisms underlie cross-modal semantic conflict and response conflict. The response time data showed significant effects on both cross-modal semantic and response conflicts. Interestingly, the magnitude of semantic conflict was found to be smaller in the fast response time bins than in the slow response time bins, whereas no such difference was observed for response conflict. The EEG data demonstrated that cross-modal semantic conflict specifically increased the N450 amplitude. However, cross-modal response conflict specifically enhanced theta band power and theta phase synchronization between the medial frontal cortex (MFC) and lateral prefrontal electrodes as well as between the MFC and motor electrodes. In addition, both cross-modal semantic conflict and response conflict led to a decrease in P3 amplitude. Taken together, these findings provide cross-modal evidence for domain-specific mechanism in conflict detection and suggest both domain-specific and domain-general mechanisms exist in conflict resolution.
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Electroencefalografía , Semántica , Mapeo Encefálico , Lóbulo Frontal/fisiología , Tiempo de Reacción/fisiologíaRESUMEN
Brain signal irreversibility has been shown to be a promising approach to study neural dynamics. Nevertheless, the relation with cortical hierarchy and the influence of different electrophysiological features is not completely understood. In this study, we recorded local field potentials (LFPs) during spontaneous behavior, including awake and sleep periods, using custom micro-electrocorticographic (µECoG) arrays implanted in ferrets. In contrast to humans, ferrets remain less time in each state across the sleep-wake cycle. We deployed a diverse set of metrics in order to measure the levels of complexity of the different behavioral states. In particular, brain irreversibility, which is a signature of non-equilibrium dynamics, captured by the arrow of time of the signal, revealed the hierarchical organization of the ferret's cortex. We found different signatures of irreversibility and functional hierarchy of large-scale dynamics in three different brain states (active awake, quiet awake, and deep sleep), showing a lower level of irreversibility in the deep sleep stage, compared to the other. Irreversibility also allowed us to disentangle the influence of different cortical areas and frequency bands in this process, showing a predominance of the parietal cortex and the theta band. Furthermore, when inspecting the embedded dynamic through a Hidden Markov Model, the deep sleep stage was revealed to have a lower switching rate and lower entropy production. These results suggest functional hierarchies in organization that can be revealed through thermodynamic features and information theory metrics.
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Encéfalo , Hurones , Animales , Humanos , Encéfalo/fisiología , Sueño/fisiología , Mapeo Encefálico/métodos , Vigilia/fisiologíaRESUMEN
When we pay attention to someone, do we focus only on the sound they make, the word they use, or do we form a mental space shared with the speaker we want to pay attention to? Some would argue that the human language is no other than a simple signal, but others claim that human beings understand each other because they form a shared mental ground between the speaker and the listener. Our study aimed to explore the neural mechanisms of speech-selective attention by investigating the electroencephalogram-based neural coupling between the speaker and the listener in a cocktail party paradigm. The temporal response function method was employed to reveal how the listener was coupled to the speaker at the neural level. The results showed that the neural coupling between the listener and the attended speaker peaked 5 s before speech onset at the delta band over the left frontal region, and was correlated with speech comprehension performance. In contrast, the attentional processing of speech acoustics and semantics occurred primarily at a later stage after speech onset and was not significantly correlated with comprehension performance. These findings suggest a predictive mechanism to achieve speaker-listener neural coupling for successful speech comprehension.
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Percepción del Habla , Habla , Humanos , Habla/fisiología , Percepción del Habla/fisiología , Electroencefalografía , Lenguaje , Acústica del LenguajeRESUMEN
Despite the distortion of speech signals caused by unavoidable noise in daily life, our ability to comprehend speech in noisy environments is relatively stable. However, the neural mechanisms underlying reliable speech-in-noise comprehension remain to be elucidated. The present study investigated the neural tracking of acoustic and semantic speech information during noisy naturalistic speech comprehension. Participants listened to narrative audio recordings mixed with spectrally matched stationary noise at three signal-to-ratio (SNR) levels (no noise, 3 dB, -3 dB), and 60-channel electroencephalography (EEG) signals were recorded. A temporal response function (TRF) method was employed to derive event-related-like responses to the continuous speech stream at both the acoustic and the semantic levels. Whereas the amplitude envelope of the naturalistic speech was taken as the acoustic feature, word entropy and word surprisal were extracted via the natural language processing method as two semantic features. Theta-band frontocentral TRF responses to the acoustic feature were observed at around 400 ms following speech fluctuation onset over all three SNR levels, and the response latencies were more delayed with increasing noise. Delta-band frontal TRF responses to the semantic feature of word entropy were observed at around 200 to 600 ms leading to speech fluctuation onset over all three SNR levels. The response latencies became more leading with increasing noise and decreasing speech comprehension and intelligibility. While the following responses to speech acoustics were consistent with previous studies, our study revealed the robustness of leading responses to speech semantics, which suggests a possible predictive mechanism at the semantic level for maintaining reliable speech comprehension in noisy environments.
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Comprensión , Percepción del Habla , Humanos , Comprensión/fisiología , Semántica , Habla/fisiología , Percepción del Habla/fisiología , Electroencefalografía , Acústica , Estimulación AcústicaRESUMEN
Intrinsic coupling modes (ICMs) can be observed in ongoing brain activity at multiple spatial and temporal scales. Two families of ICMs can be distinguished: phase and envelope ICMs. The principles that shape these ICMs remain partly elusive, in particular their relation to the underlying brain structure. Here we explored structure-function relationships in the ferret brain between ICMs quantified from ongoing brain activity recorded with chronically implanted micro-ECoG arrays and structural connectivity (SC) obtained from high-resolution diffusion MRI tractography. Large-scale computational models were used to explore the ability to predict both types of ICMs. Importantly, all investigations were conducted with ICM measures that are sensitive or insensitive to volume conduction effects. The results show that both types of ICMs are significantly related to SC, except for phase ICMs when using measures removing zero-lag coupling. The correlation between SC and ICMs increases with increasing frequency which is accompanied by reduced delays. Computational models produced results that were highly dependent on the specific parameter settings. The most consistent predictions were derived from measures solely based on SC. Overall, the results demonstrate that patterns of cortical functional coupling as reflected in both phase and envelope ICMs are both related, albeit to different degrees, to the underlying structural connectivity in the cerebral cortex.
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Corteza Cerebral , Hurones , Humanos , Animales , Corteza Cerebral/diagnóstico por imagen , Encéfalo , Mapeo Encefálico/métodos , ElectrocorticografíaRESUMEN
BACKGROUND: Covert visuo-spatial attention is marked by the anticipatory lateralization of neuronal alpha activity in the posterior parietal cortex. Previous applications of transcranial alternating current stimulation (tACS) at the alpha frequency, however, were inconclusive regarding the causal contribution of oscillatory activity during visuo-spatial attention. OBJECTIVE: Attentional shifts of behavior and electroencephalography (EEG) after-effects were assessed in a cued visuo-spatial attention paradigm. We hypothesized that parietal alpha-tACS shifts attention relative to the ipsilateral visual hemifield. Furthermore, we assumed that modulations of behavior and neurophysiology are related to individual electric field simulations. METHODS: We applied personalized tACS at alpha and gamma frequencies to elucidate the role of oscillatory neuronal activity for visuo-spatial attention. Personalized tACS montages were algorithmically optimized to target individual left and right parietal regions that were defined by an EEG localizer. RESULTS: Behavioral performance in the left hemifield was specifically increased by alpha-tACS compared to gamma-tACS targeting the left parietal cortex. This hemisphere-specific effect was observed despite the symmetry of simulated electric fields. In addition, visual event-related potential (ERP) amplitudes showed a reduced lateralization over posterior sites induced by left alpha-tACS. Neuronal sources of this effect were localized in the left premotor cortex. Interestingly, accuracy modulations induced by left parietal alpha-tACS were directly related to electric field magnitudes in the left premotor cortex. CONCLUSION: Overall, results corroborate the notion that alpha lateralization plays a causal role in covert visuo-spatial attention and indicate an increased susceptibility of parietal and premotor brain regions of the left dorsal attention network to subtle tACS-neuromodulation.
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Estimulación Transcraneal de Corriente Directa , Estimulación Transcraneal de Corriente Directa/métodos , Lóbulo Parietal/fisiología , Electroencefalografía , Encéfalo , Potenciales EvocadosRESUMEN
OBJECTIVE: Anesthesia and surgery are associated with cognitive impairment, particularly memory deficits. So far, electroencephalography markers of perioperative memory function remain scarce. METHODS: We included male patients >60 years scheduled for prostatectomy under general anesthesia. We obtained neuropsychological assessments and a visual match-to-sample working memory task with simultaneous 62-channel scalp electroencephalography 1 day before and 2 to 3 days after surgery. RESULTS: Twenty-six patients completed both pre- and postoperative sessions. Compared with preoperative performance, verbal learning deteriorated after anesthesia (California Verbal Learning Test total recall; t25 = -3.25, p = 0.015, d = -0.902), while visual working memory performance showed a dissociation between match and mismatch accuracy (match*session F1,25 = 3.866, p = 0.060). Better verbal learning was associated with an increase of aperiodic brain activity (total recall r = 0.66, p = 0.029, learning slope r = 0.66, p = 0.015), whereas visual working memory accuracy was tracked by oscillatory theta/alpha (7 - 9 Hz), low beta (14 - 18 Hz) and high beta/gamma (34 - 38 Hz) activity (matches: p < 0.001, mismatches: p = 0.022). CONCLUSIONS: Oscillatory and aperiodic brain activity in scalp electroencephalography track distinct features of perioperative memory function. SIGNIFICANCE: Aperiodic activity provides a potential electroencephalographic biomarker to identify patients at risk for postoperative cognitive impairments.
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Anestesia , Memoria a Corto Plazo , Humanos , Masculino , Memoria a Corto Plazo/fisiología , Encéfalo , Electroencefalografía , AprendizajeRESUMEN
The parkinsonian gait disorder and freezing of gait are therapeutically demanding symptoms with considerable impact on quality of life. The aim of this study was to assess the role of subthalamic and nigral neurons in the parkinsonian gait control using intraoperative microelectrode recordings of basal ganglia neurons during a supine stepping task. Twelve male patients (56 ± 7 years) suffering from moderate idiopathic Parkinson's disease (disease duration 10 ± 3 years, Hoehn and Yahr stage 2), undergoing awake neurosurgery for deep brain stimulation, participated in the study. After 10 s resting, stepping at self-paced speed for 35 s was followed by short intervals of stepping in response to random 'start' and 'stop' cues. Single- and multi-unit activity was analysed offline in relation to different aspects of the stepping task (attentional 'start' and 'stop' cues, heel strikes, stepping irregularities) in terms of firing frequency, firing pattern and oscillatory activity. Subthalamic nucleus and substantia nigra neurons responded to different aspects of the stepping task. Of the subthalamic nucleus neurons, 24% exhibited movement-related activity modulation as an increase of the firing rate, suggesting a predominant role of the subthalamic nucleus in motor aspects of the task, while 8% of subthalamic nucleus neurons showed a modulation in response to the attentional cues. In contrast, responsive substantia nigra neurons showed activity changes exclusively associated with attentional aspects of the stepping task (15%). The firing pattern of subthalamic nucleus neurons revealed gait-related firing regularization and a drop of beta oscillations during the stepping performance. During freezing episodes instead, there was a rise of beta oscillatory activity. This study shows for the first time specific, task-related subthalamic nucleus and substantia nigra single-unit activity changes during gait-like movements in humans with differential roles in motor and attentional control of gait. The emergence of perturbed firing patterns in the subthalamic nucleus indicates a disrupted information transfer within the gait network, resulting in freezing of gait.
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Estimulación Encefálica Profunda , Trastornos Neurológicos de la Marcha , Enfermedad de Parkinson , Trastornos Parkinsonianos , Humanos , Masculino , Estimulación Encefálica Profunda/métodos , Marcha/fisiología , Trastornos Neurológicos de la Marcha/etiología , Neuronas/fisiología , Enfermedad de Parkinson/terapia , Calidad de Vida , Sustancia NegraRESUMEN
Multiple sclerosis (MS) is an inflammatory and demyelinating disease which leads to impairment in several functional systems including cognition. Alteration of brain networks is linked to disability and its progression. However, results are mostly cross-sectional and yet contradictory as putative adaptive and maladaptive mechanisms were found. Here, we aimed to explore longitudinal reorganization of brain networks over 2-years by combining diffusion tensor imaging (DTI), resting-state functional MRI (fMRI), magnetoencephalography (MEG), and a comprehensive neuropsychological-battery. In 37 relapsing-remitting MS (RRMS) and 39 healthy-controls, cognition remained stable over-time. We reconstructed network models based on the three modalities and analyzed connectivity in relation to the hierarchical topology and functional subnetworks. Network models were compared across modalities and in their association with cognition using linear-mixed-effect-regression models. Loss of hub connectivity and global reduction was observed on a structural level over-years (p < .010), which was similar for functional MEG-networks but not for fMRI-networks. Structural hub connectivity increased in controls (p = .044), suggesting a physiological mechanism of healthy aging. Despite a general loss in structural connectivity in RRMS, hub connectivity was preserved (p = .002) over-time in default-mode-network (DMN). MEG-networks were similar to DTI and weakly correlated with fMRI in MS (p < .050). Lower structural (ß between .23-.33) and both lower (ß between .40-.59) and higher functional connectivity (ß = -.54) in DMN was associated with poorer performance in attention and memory in RRMS (p < .001). MEG-networks involved no association with cognition. Here, cognitive stability despite ongoing neurodegeneration might indicate a resilience mechanism of DMN hubs mimicking a physiological reorganization observed in healthy aging.
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Esclerosis Múltiple Recurrente-Remitente , Esclerosis Múltiple , Humanos , Esclerosis Múltiple Recurrente-Remitente/complicaciones , Esclerosis Múltiple Recurrente-Remitente/diagnóstico por imagen , Imagen de Difusión Tensora , Mapeo Encefálico , Estudios Transversales , Pruebas Neuropsicológicas , Encéfalo/diagnóstico por imagen , Cognición , Imagen por Resonancia Magnética , Vías Nerviosas/diagnóstico por imagenRESUMEN
While the increasingly globalized world has brought more and more demands for non-native language communication, the prevalence of background noise in everyday life poses a great challenge to non-native speech comprehension. The present study employed an interbrain approach based on functional near-infrared spectroscopy (fNIRS) to explore how people adapt to comprehend non-native speech information in noise. A group of Korean participants who acquired Chinese as their non-native language was invited to listen to Chinese narratives at 4 noise levels (no noise, 2 dB, -6 dB, and - 9 dB). These narratives were real-life stories spoken by native Chinese speakers. Processing of the non-native speech was associated with significant fNIRS-based listener-speaker neural couplings mainly over the right hemisphere at both the listener's and the speaker's sides. More importantly, the neural couplings from the listener's right superior temporal gyrus, the right middle temporal gyrus, as well as the right postcentral gyrus were found to be positively correlated with their individual comprehension performance at the strongest noise level (-9 dB). These results provide interbrain evidence in support of the right-lateralized mechanism for non-native speech processing and suggest that both an auditory-based and a sensorimotor-based mechanism contributed to the non-native speech-in-noise comprehension.
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Percepción del Habla , Habla , Humanos , Comprensión , Ruido , Percepción AuditivaRESUMEN
In human-robot collaboration scenarios with shared workspaces, a highly desired performance boost is offset by high requirements for human safety, limiting speed and torque of the robot drives to levels which cannot harm the human body. Especially for complex tasks with flexible human behavior, it becomes vital to maintain safe working distances and coordinate tasks efficiently. An established approach in this regard is reactive servo in response to the current human pose. However, such an approach does not exploit expectations of the human's behavior and can therefore fail to react to fast human motions in time. To adapt the robot's behavior as soon as possible, predicting human intention early becomes a factor which is vital but hard to achieve. Here, we employ a recently developed type of brain-computer interface (BCI) which can detect the focus of the human's overt attention as a predictor for impending action. In contrast to other types of BCI, direct projection of stimuli onto the workspace facilitates a seamless integration in workflows. Moreover, we demonstrate how the signal-to-noise ratio of the brain response can be used to adjust the velocity of the robot movements to the vigilance or alertness level of the human. Analyzing this adaptive system with respect to performance and safety margins in a physical robot experiment, we found the proposed method could improve both collaboration efficiency and safety distance.
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Dynamic coupling of neural signals is a hallmark of brain networks, but its potential relevance is still debated. Does coupling play a causal role for network functions, or is it just a by-product of structural connectivity or other physiological processes? With intervention techniques that have become available, experiments seem within reach that may provide answers to this long-standing question.
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Mapeo Encefálico , Encéfalo , Humanos , Mapeo Encefálico/métodos , Encéfalo/fisiología , Imagen por Resonancia Magnética/métodos , Red Nerviosa/fisiología , Vías Nerviosas/fisiologíaRESUMEN
Mice display signs of fear when neurons that express cFos during fear conditioning are artificially reactivated. This finding gave rise to the notion that cFos marks neurons that encode specific memories. Here we show that cFos expression patterns in the mouse dentate gyrus (DG) change dramatically from day to day in a water maze spatial learning paradigm, regardless of training level. Optogenetic inhibition of neurons that expressed cFos on the first training day affected performance days later, suggesting that these neurons continue to be important for spatial memory recall. The mechanism preventing repeated cFos expression in DG granule cells involves accumulation of ΔFosB, a long-lived splice variant of FosB. CA1 neurons, in contrast, repeatedly expressed cFos. Thus, cFos-expressing granule cells may encode new features being added to the internal representation during the last training session. This form of timestamping is thought to be required for the formation of episodic memories.
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Giro Dentado , Aprendizaje Espacial , Animales , Ratones , Giro Dentado/fisiología , Hipocampo , Neuronas/metabolismo , Memoria EspacialRESUMEN
BACKGROUND: The spatially-coded SSVEP BCI employs the retinotopic map in the human visual pathway to infer the gaze direction of the operator relative to a flicker stimulus inducing steady-state visual evoked potentials (SSVEPs) in the brain. It has been shown that with this method, up to 16 channels can be encoded using only a single flicker stimulus. Another advantage over conventional frequency-coded SSVEP BCIs, in which channels are encoded by different combinations of frequencies and phases, is that the operator does not have to gaze directly at flickering lights. This can reduce visual fatigue and improve user comfort. Whereas the frequency of the SSVEP response is well predictable, which has enabled the development of frequency-coded SSVEP BCIs which do not require training data, the spatial distribution of the SSVEP response over the scalp differs much more between different people. This requires collecting a substantial amount of training data before the spatially-coded BCI could be put into operation. NEW METHOD: In this study we address this issue by combining the spatially-coded BCI with a feedback channel which the operator uses to flag classification errors, and which allows the system to accumulate valid training data while the BCI is used to solve a spatial navigation task. RESULTS: Starting from the minimal number of samples required by the classification method, the approach achieved an average accuracy of 69 ± 15 %, corresponding to an ITR of 31 ± 17 bits/min, in solving the task for the first time. This accuracy improved to 87 ± 9 % (ITR: 54 ± 14 bits/min) after completing the task 2 more times. Further we show that participants with a stable SSVEP topography over repeated stimulation enable the BCI to achieve higher accuracies. COMPARISON WITH EXISTING METHODS: Compared to a similar system with separate training and application phases, the time to achieve the same output is reduced by more than 50 %. CONCLUSIONS: Evaluating the approach in 17 participants suggests that the performance of the spatially-coded BCI with a minimal set of training samples is sufficient to be operational, and that performance keeps improving in the course of its application.