High-level Cortical Areas Research Articles

Attentional selection and communication through coherence: Scope and limitations.

Synchronous neural oscillations are strongly associated with a variety of perceptual, cognitive, and behavioural processes. It has been proposed that the role of the synchronous oscillations in these processes is to facilitate information transmission between brain areas, the 'communication through coherence,' or CTC hypothesis. The details of how this mechanism would work, however, and its causal status, are still unclear. Here we investigate computationally a proposed mechanism for selective attention that directly implicates the CTC as causal. The mechanism involves alpha band (about 10 Hz) oscillations, originating in the pulvinar nucleus of the thalamus, being sent to communicating cortical areas, organizing gamma (about 40 Hz) oscillations there, and thus facilitating phase coherence and communication between them. This is proposed to happen contingent on control signals sent from higher-level cortical areas to the thalamic reticular nucleus, which controls the alpha oscillations sent to cortex by the pulvinar. We studied the scope of this mechanism in parameter space, and limitations implied by this scope, using a computational implementation of our conceptual model. Our results indicate that, although the CTC-based mechanism can account for some effects of top-down and bottom-up attentional selection, its limitations indicate that an alternative mechanism, in which oscillatory coherence is caused by communication between brain areas rather than being a causal factor for it, might operate in addition to, or even instead of, the CTC mechanism.

Perceptual Learning of Fine Contrast Discrimination Under Non-roving, Roving-Without-Flanker, and Roving-with-Flanker Conditions and its Relation to Neuronal Activity in Macaque V1

AbstractPerceptual learning refers to an improvement in perceptual abilities with training. Neural signatures of visual perceptual learning have been demonstrated mostly in mid- and high-level cortical areas, while changes in early sensory cortex were often more limited. We recorded continuously from multiple neuronal clusters in area V1 while macaque monkeys learned a fine contrast categorization task. Monkeys performed the contrast discrimination task initially when a constant-contrast sample stimulus was followed by a test stimulus of variable contrast, whereby they had to indicate whether the test was of lower or higher contrast than the sample. This was followed by sessions where we employed stimulus roving; i.e. the contrast of the sample stimulus varied from trial to trial. Finally, we trained animals, under ‘stimulus roving-with-flanker’ conditions, where the test stimuli to be discriminated were flanked by ‘flanking stimuli’. Perceptual discrimination abilities improved under non-roving conditions and under roving-with-flanker conditions as training progressed. Neuronal discrimination abilities improved with training mostly under non-roving conditions, but the effect was modest and limited to the most difficult contrast. Choice probabilities, quantifying how well neural activity is correlated with choice, equally increased with training during non-roving, but not during either of the roving conditions (with and without flankers). Noise correlations changed with training in both monkeys, but the changes were not consistent between monkeys. In one monkey, noise correlations decreased with training for non-roving and both roving conditions. In the other monkey, noise correlations changed for some conditions, but lacked a systematic pattern. Thus, while perceptual learning occurred under non-roving and roving-with-flanker conditions, the changes in neural activity in V1 were overall modest and were essentially absent under the different roving conditions.

Driving and suppressing the human language network using large language models.

Transformer models such as GPT generate human-like language and are predictive of human brain responses to language. Here, using functional-MRI-measured brain responses to 1,000 diverse sentences, we first show that a GPT-based encoding model can predict the magnitude of the brain response associated with each sentence. We then use the model to identify new sentences that are predicted to drive or suppress responses in the human language network. We show that these model-selected novel sentences indeed strongly drive and suppress the activity of human language areas in new individuals. A systematic analysis of the model-selected sentences reveals that surprisal and well-formedness of linguistic input are key determinants of response strength in the language network. These results establish the ability of neural network models to not only mimic human language but also non-invasively control neural activity in higher-level cortical areas, such as the language network.

Top-down influence of areas 21a and 7 differently affects the surround suppression of V1 neurons in cats.

Surround suppression (SS) is a phenomenon whereby a neuron's response to stimuli in its central receptive field (cRF) is suppressed by stimuli extending to its surround receptive field (sRF). Recent evidence show that top-down influence contributed to SS in the primary visual cortex (V1). However, how the top-down influence from different high-level cortical areas affects SS in V1 has not been comparatively observed. The present study applied transcranial direct current stimulation (tDCS) to modulate the neural activity in area 21a (A21a) and area 7 (A7) of cats and examined the changes in the cRF and sRF of V1 neurons. We found that anode-tDCS at A21a reduced V1 neurons' cRF size and increased their response to visual stimuli in cRF, causing an improved SS strength. By contrast, anode-tDCS at A7 increased V1 neurons' sRF size and response to stimuli in cRF, also enhancing the SS. Modeling analysis based on DoG function indicated that the increased SS of V1 neurons after anode-tDCS at A21a could be explained by a center-only mechanism, whereas the improved SS after anode-tDCS at A7 might be mediated through a combined center and surround mechanism. In conclusion, A21a and A7 may affect the SS of V1 neurons through different mechanisms.

Functional connectivity between medial pulvinar and cortical networks as a predictor of arousal to noxious stimuli during sleep.

The interruption of sleep by a nociceptive stimulus is favoured by an increase in the pre-stimulus functional connectivity between sensory and higher level cortical areas. In addition, stimuli inducing arousal also trigger a widespread electroencephalographic (EEG) response reflecting the coordinated activation of a large cortical network. Because functional connectivity between distant cortical areas is thought to be underpinned by trans-thalamic connections involving associative thalamic nuclei, we investigated the possible involvement of one principal associative thalamic nucleus, the medial pulvinar (PuM), in the sleeper's responsiveness to nociceptive stimuli. Intra-cortical and intra-thalamic signals were analysed in 440 intracranial electroencephalographic (iEEG) segments during nocturnal sleep in eight epileptic patients receiving laser nociceptive stimuli. The spectral coherence between the PuM and 10 cortical regions grouped in networks was computed during 5s before and 1 s after the nociceptive stimulus and contrasted according to the presence or absence of an arousal EEG response. Pre- and post-stimulus phase coherence between the PuM and all cortical networks was significantly increased in instances of arousal, both during N2 and paradoxical (rapid eye movement [REM]) sleep. Thalamo-cortical enhancement in coherence involved both sensory and higher level cortical networks and predominated in the pre-stimulus period. The association between pre-stimulus widespread increase in thalamo-cortical coherence and subsequent arousal suggests that the probability of sleep interruption by a noxious stimulus increases when it occurs during phases of enhanced trans-thalamic transfer of information between cortical areas.

Time-locked auditory cortical responses in the high-gamma band: A window into primary auditory cortex.

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 with 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.

The neural response at the fundamental frequency of speech is modulated by word-level acoustic and linguistic information.

Spoken language comprehension requires rapid and continuous integration of information, from lower-level acoustic to higher-level linguistic features. Much of this processing occurs in the cerebral cortex. Its neural activity exhibits, for instance, correlates of predictive processing, emerging at delays of a few 100 ms. However, the auditory pathways are also characterized by extensive feedback loops from higher-level cortical areas to lower-level ones as well as to subcortical structures. Early neural activity can therefore be influenced by higher-level cognitive processes, but it remains unclear whether such feedback contributes to linguistic processing. Here, we investigated early speech-evoked neural activity that emerges at the fundamental frequency. We analyzed EEG recordings obtained when subjects listened to a story read by a single speaker. We identified a response tracking the speaker's fundamental frequency that occurred at a delay of 11 ms, while another response elicited by the high-frequency modulation of the envelope of higher harmonics exhibited a larger magnitude and longer latency of about 18 ms with an additional significant component at around 40 ms. Notably, while the earlier components of the response likely originate from the subcortical structures, the latter presumably involves contributions from cortical regions. Subsequently, we determined the magnitude of these early neural responses for each individual word in the story. We then quantified the context-independent frequency of each word and used a language model to compute context-dependent word surprisal and precision. The word surprisal represented how predictable a word is, given the previous context, and the word precision reflected the confidence about predicting the next word from the past context. We found that the word-level neural responses at the fundamental frequency were predominantly influenced by the acoustic features: the average fundamental frequency and its variability. Amongst the linguistic features, only context-independent word frequency showed a weak but significant modulation of the neural response to the high-frequency envelope modulation. Our results show that the early neural response at the fundamental frequency is already influenced by acoustic as well as linguistic information, suggesting top-down modulation of this neural response.

Decoding grasp and speech signals from the cortical grasp circuit in a tetraplegic human

The cortical grasp network encodes planning and execution of grasps and processes spoken and written aspects of language. High-level cortical areas within this network are attractive implant sites for brain-machine interfaces (BMIs). While a tetraplegic patient performed grasp motor imagery and vocalized speech, neural activity was recorded from the supramarginal gyrus (SMG), ventral premotor cortex (PMv), and somatosensory cortex (S1). In SMG and PMv, five imagined grasps were well represented by firing rates of neuronal populations during visual cue presentation. During motor imagery, these grasps were significantly decodable from all brain areas. During speech production, SMG encoded both spoken grasp types and the names of five colors. Whereas PMv neurons significantly modulated their activity during grasping, SMG's neural population broadly encoded features of both motor imagery and speech. Together, these results indicate that brain signals from high-level areas of the human cortex could be used for grasping and speech BMI applications.

Neuropsychological correlates of cerebellar volumes in multiple sclerosis: an MRI volumetric analysis study.

The hallmark of Multiple Sclerosis (MS) pathophysiology is the damage to the myelin sheath around axons. The cerebellum is a predilection site for demyelination with a well-recognized role in motor and a rather understudied contribution to cognitive functions. The aim of this study is to investigate patterns of cerebellar grey and white matter pathology, expressed as reduced volume, as well as cortical thickness and their potential contribution to cognitive performance and disability status of patients with MS. 24 patients with MS underwent extensive neuropsychological assessment using paper and pencil tests and the Brain Health Assessment (BHA) tablet-based battery. Cerebellar lobular volumes and thickness were calculated using a volumetric analysis with automated segmentation of the cerebellum and its lobules. The main findings are a reduction of cerebellar grey matter (CGMV) and white matter volumes (CWMV) in lobule X and a widespread cerebellar cortical thinning in patients. Overall disease severity and neurological disability, assessed with the Expanded Disability Status Severity Scale, was correlated with fatigue and information processing speed tasks, but not with CGMV and CWMV. CWMV and CGMV of lobule I-II was negatively correlated with information processing speed, as well as visuospatial memory tests and, finally, inverse cortical thinning associations were noted between the whole cerebellum, lobule I-II, lobule III, lobule VI, Crus I, lobule VIIIA and information processing speed and verbal fluency tasks. The inverse associations observed may represent a compensatory mechanism activated in MS engaging additional high-level cortical areas functionally interconnected with the damaged cerebellum, in order to cope with the cognitive demands of a task.

The Neural Basis of Semantic Prediction in Sentence Comprehension.

Although prediction plays an important role in language comprehension, its precise neural basis remains unclear. This fMRI study investigated whether and how semantic-category-specific and common cerebral areas are recruited in predictive semantic processing during sentence comprehension. We manipulated the semantic constraint of sentence contexts, upon which a tool-related, a building-related, or no specific category of noun is highly predictable. This noun-predictability effect was measured not only over the target nouns but also over their preceding transitive verbs. Both before and after the appearance of target nouns, left anterior supramarginal gyrus was specifically activated for tool-related nouns and left parahippocampal place area was activated specifically for building-related nouns. The semantic-category common areas included a subset of left inferior frontal gyrus during the anticipation of incoming target nouns (activity enhancement for high predictability) and included a wide spread of areas (bilateral inferior frontal gyrus, left superior/middle temporal gyrus, left medial pFC, and left TPJ) during the integration of actually perceived nouns (activity reduction for high predictability). These results indicated that the human brain recruits fine divisions of cortical areas to distinguish different semantic categories of predicted words, and anticipatory semantic processing relies, at least partially, on top-down prediction conducted in higher-level cortical areas.

Violation of rhythmic expectancies can elicit late frontal gamma activity nested in theta oscillations.

Rhythm processing involves building expectations according to the hierarchical temporal structure of auditory events. Although rhythm processing has been addressed in the context of predictive coding, the properties of the oscillatory response in different cortical areas are still not clear. We explored the oscillatory properties of the neural response to rhythmic incongruence and the cross‐frequency coupling between multiple frequencies to further investigate the mechanisms underlying rhythm perception. We designed an experiment to investigate the neural response to rhythmic deviations in which the tone either arrived earlier than expected or the tone in the same metrical position was omitted. These two manipulations modulate the rhythmic structure differently, with the former creating a larger violation of the general structure of the musical stimulus than the latter. Both deviations resulted in an MMN response, whereas only the rhythmic deviant resulted in a subsequent P3a. Rhythmic deviants due to the early occurrence of a tone, but not omission deviants, seemed to elicit a late high gamma response (60–80 Hz) at the end of the P3a over the left frontal region, which, interestingly, correlated with the P3a amplitude over the same region and was also nested in theta oscillations. The timing of the elicited high‐frequency gamma oscillations related to rhythmic deviation suggests that it might be related to the update of the predictive neural model, corresponding to the temporal structure of the events in higher‐level cortical areas.

Intracortical Functional Connectivity Predicts Arousal to Noxious Stimuli during Sleep in Humans.

Nociceptive stimuli disrupt sleep, but may, or may not, entail an arousal. While arousal reactions go along with the activation of a widespread cortical network, the factors enabling such activation remain unknown. Here we used intracranial EEG in humans to test the relation between the cortical activity immediately preceding a noxious stimulus and the capacity of such a stimulus to trigger arousal. Intracranial EEG signals were analyzed during all-night sleep in 14 epileptic patients (4 women), who received laser stimuli slightly above their individual pain threshold. During 5 s preceding each stimulus, the functional correlation (spectral phase-coherence) between the main spinothalamic sensory area (posterior insula) and 12 other brain regions, grouped in four networks, as well as their spectral contents, were contrasted according to the presence of a stimulus-induced arousal, and then fed into a logistic regression model to assess their predictive value. Enhanced prestimulus phase-coherence between the sensory posterior insula and neocortical and limbic areas increased significantly the probability of arousal to nociceptive stimuli, in both slow-wave (N2) and rapid eye movements/paradoxical sleep. Furthermore, during N2 sleep, arousal was facilitated by stimulus delivery in periods of attenuated slow-wave activity. Together, these data indicate that sleep micro-states with enhanced interareal communication facilitate information transfer from sensory to higher-order cortical areas, and hence physiological arousal.SIGNIFICANCE STATEMENT Sleep is commonly subdivided into stages based on specific electrophysiological characteristics; however, within each single sleep stage, the functional state of the brain is continuously changing. Here we show that the probability for a phasic noxious stimulus to entail an arousal is modulated by the prestimulus interareal phase-coherence between sensory and higher-level cortical areas. Fluctuations in interareal communication immediately before the noxious stimulus may determine the responsiveness to incoming input by facilitating or preventing the transfer of noxious information from sensory to multiple higher-level cortical networks.

The Effect of Perceptual Learning on Face Recognition in Individuals with Central Vision Loss.

PurposeTo examine whether perceptual learning can improve face discrimination and recognition in older adults with central vision loss.MethodsTen participants with age-related macular degeneration (ARMD) received 5 days of training on a face discrimination task (mean age, 78 ± 10 years). We measured the magnitude of improvements (i.e., a reduction in threshold size at which faces were able to be discriminated) and whether they generalized to an untrained face recognition task. Measurements of visual acuity, fixation stability, and preferred retinal locus were taken before and after training to contextualize learning-related effects. The performance of the ARMD training group was compared to nine untrained age-matched controls (8 = ARMD, 1 = juvenile macular degeneration; mean age, 77 ± 10 years).ResultsPerceptual learning on the face discrimination task reduced the threshold size for face discrimination performance in the trained group, with a mean change (SD) of –32.7% (+15.9%). The threshold for performance on the face recognition task was also reduced, with a mean change (SD) of –22.4% (+2.31%). These changes were independent of changes in visual acuity, fixation stability, or preferred retinal locus. Untrained participants showed no statistically significant reduction in threshold size for face discrimination, with a mean change (SD) of –8.3% (+10.1%), or face recognition, with a mean change (SD) of +2.36% (–5.12%).ConclusionsThis study shows that face discrimination and recognition can be reliably improved in ARMD using perceptual learning. The benefits point to considerable perceptual plasticity in higher-level cortical areas involved in face-processing. This novel finding highlights that a key visual difficulty in those suffering from ARMD is readily amenable to rehabilitation.

Neural mechanisms of vibrotactile categorization.

The grouping of sensory stimuli into categories is fundamental to cognition. Previous research in the visual and auditory systems supports a two-stage processing hierarchy that underlies perceptual categorization: (a) a "bottom-up" perceptual stage in sensory cortices where neurons show selectivity for stimulus features and (b) a "top-down" second stage in higher level cortical areas that categorizes the stimulus-selective input from the first stage. In order to test the hypothesis that the two-stage model applies to the somatosensory system, 14 human participants were trained to categorize vibrotactile stimuli presented to their right forearm. Then, during an fMRI scan, participants actively categorized the stimuli. Representational similarity analysis revealed stimulus selectivity in areas including the left precentral and postcentral gyri, the supramarginal gyrus, and the posterior middle temporal gyrus. Crucially, we identified a single category-selective region in the left ventral precentral gyrus. Furthermore, an estimation of directed functional connectivity delivered evidence for robust top-down connectivity from the second to first stage. These results support the validity of the two-stage model of perceptual categorization for the somatosensory system, suggesting common computational principles and a unified theory of perceptual categorization across the visual, auditory, and somatosensory systems.

Neuroimaging Findings on Amodal Completion: A Review.

Amodal completion is the phenomenon of perceiving completed objects even though physically they are partially occluded. In this review, we provide an extensive overview of the results obtained from a variety of neuroimaging studies on the neural correlates of amodal completion. We discuss whether low-level and high-level cortical areas are implicated in amodal completion; provide an overview of how amodal completion unfolds over time while dissociating feedforward, recurrent, and feedback processes; and discuss how amodal completion is represented at the neuronal level. The involvement of low-level visual areas such as V1 and V2 is not yet clear, while several high-level structures such as the lateral occipital complex and fusiform face area seem invariant to occlusion of objects and faces, respectively, and several motor areas seem to code for object permanence. The variety of results on the timing of amodal completion hints to a mixture of feedforward, recurrent, and feedback processes. We discuss whether the invisible parts of the occluded object are represented as if they were visible, contrary to a high-level representation. While plenty of questions on amodal completion remain, this review presents an overview of the neuroimaging findings reported to date, summarizes several insights from computational models, and connects research of other perceptual completion processes such as modal completion. In all, it is suggested that amodal completion is the solution to deal with various types of incomplete retinal information, and highly depends on stimulus complexity and saliency, and therefore also give rise to a variety of observed neural patterns.

Neural mechanisms of motion perceptual learning in noise.

Practice improves our perceptual ability. However, the neural mechanisms underlying this experience-dependent plasticity in adult brain remain unclear. Here, we studied the long-term neural correlates of motion perceptual learning. Subjects' behavioral performance and BOLD signals were tracked before, immediately after, and 2 weeks after practicing a motion direction discrimination task in noise over six daily sessions. Parallel to the specificity and persistency of the behavioral learning effect, we found that training sharpened the cortical tuning in MT, and enhanced the connectivity strength from MT to the intraparietal sulcus (IPS, a motion decision-making area). In addition, the decoding accuracy for the trained motion direction was improved in IPS 2 weeks after training. The dual changes in the sensory and the high-level cortical areas suggest that learning refines the neural representation of the trained stimulus and facilitates the information transmission in the decision process. Our findings are consistent with the functional specialization in the visual cortex, and provide empirical evidence to the reweighting theory of perceptual learning at a large spatial scale. Hum Brain Mapp 38:6029-6042, 2017. © 2017 Wiley Periodicals, Inc.

Shared memories reveal shared structure in neural activity across individuals.

Our lives revolve around sharing experiences and memories with others. When different people recount the same events, how similar are their underlying neural representations? Participants viewed a fifty-minute movie, then verbally described the events during functional MRI, producing unguided detailed descriptions lasting up to forty minutes. As each person spoke, event-specific spatial patterns were reinstated in default-network, medial-temporal, and high-level visual areas. Individual event patterns were both highly discriminable from one another and similar between people, suggesting consistent spatial organization. In many high-order areas, patterns were more similar between people recalling the same event than between recall and perception, indicating systematic reshaping of percept into memory. These results reveal the existence of a common spatial organization for memories in high-level cortical areas, where encoded information is largely abstracted beyond sensory constraints; and that neural patterns during perception are altered systematically across people into shared memory representations for real-life events.

Mismatch negativity and neural adaptation: Two sides of the same coin. Response: Commentary: Visual mismatch negativity: a predictive coding view.

Mismatch negativity and neural adaptation: Two sides of the same coin. Response: Commentary: Visual mismatch negativity: a predictive coding view.

Effect of prism adaptation on thermoregulatory control in humans

The physiological regulation of skin temperature can be modulated not only by autonomic brain regions, but also by a network of higher-level cortical areas involved in the maintenance of a coherent representation of the body. In this study we assessed in healthy participants if the sensorimotor changes taking place during motor adaptation to the lateral displacement of the visual scene induced by wearing prismatic lenses (prism adaptation, PA), and the aftereffects, after prisms’ removal, on the ability to process spatial coordinates, were associated with skin temperature regulation changes. We found a difference in thermoregulatory control as a function of the direction of the prism-induced displacement of the visual scene, and the subsequent sensorimotor adaptation. After PA to rightward displacing lenses, with leftward aftereffects (the same directional procedure efficaciously used for ameliorating left spatial neglect in right-brain-damaged patients) the hands’ temperature decreased. Conversely, after adaptation to neutral lenses, and PA to leftward displacing lenses, with rightward aftereffects, the temperature of both hands increased. These results suggest a lateral asymmetry in the effects of PA on skin temperature regulation, and a relationship between body spatial representations and homeostatic control in humans.

What is special about expertise? Visual expertise reveals the interactive nature of real-world object recognition

Ever since Diamond and Carey (1986. J. Exp. Psychol.: Gen., vol. 115, pp. 107–117) seminal work, the main model for studying expertise in visual object recognition (“visual expertise”) has been face perception. The underlying assumption was that since faces may be considered the ultimate domain of visual expertise, any face-processing signature might actually be a general characteristic of visual expertise. However, while humans are clearly experts in face recognition, visual expertise is not restricted to faces and can be observed in a variety of domains. This raises the question of whether face recognition is in fact the right model to study visual expertise, and if not, what are the common cognitive and neural characteristics of visual expertise. The current perspective article addresses this question by revisiting past and recent neuroimaging and behavioural works on visual expertise. The view of visual expertise that emerges from these works is that expertise is a unique phenomenon, with distinctive neural and cognitive characteristics. Specifically, visual expertise is a controlled, interactive process that develops from the reciprocal interactions between the visual system and multiple top-down factors, including semantic knowledge, top-down attentional control, and task relevance. These interactions enable the ability to flexibly access domain-specific information at multiple scales and levels guided by multiple recognition goals. Extensive visual experience with a given object category culminates in the recruitment of these multiple systems, and is reflected in widespread neural activity, extending well beyond visual cortex, to include higher-level cortical areas.