A fast link between face perception and memory in the temporal pole

Basis functions for complex social decisions in dorsomedial frontal cortex

Navigating social environments is a fundamental challenge for the brain. It has been established that the brain solves this problem, in part, by representing social information in an agent-centric manner; knowledge about others' abilities or attitudes is tagged to individuals such as 'oneself' or the 'other'1-6. This intuitive approach has informed the understanding of key nodes in the social parts of the brain, the dorsomedial prefrontal cortex (dmPFC) and the anterior cingulate cortex (ACC)7-9. However, the patterns or combinations in which individuals might interact with one another is as important as the identities of the individuals. Here, in four studies using functional magnetic resonance imaging, behavioural experiments and a social group decision-making task, we show that the dmPFC and ACC represent the combinatorial possibilities for social interaction afforded by a given situation, and that they do so in a compressed format resembling the basis functions used in spatial, visual and motor domains10-12. The basis functions align with social interaction types, as opposed to individual identities. Our results indicate that there are deep analogies between abstract neural coding schemes in the visual and motor domain and the construction of our sense of social identity.

Imagery and perception in acquired prosopagnosia: Functional variants and their relation to structure

Current models of face perception and the face-processing network suggest that acquired prosopagnosia may not be a single disorder but rather a family of variants differing in mechanism. It has been proposed that tests of face perception and face imagery can probe component processes to support apperceptive, associative, and amnestic distinctions. However, validating this proposal is hampered by the rarity of this condition. Here we report observations gathered over two-and-a-half decades on the perception of facial shape and the imagery for famous faces of twenty-three patients. Patients with lesions limited to the occipitotemporal lobes had an apperceptive profile, with impaired perception of facial shape but no or mild deficits for face imagery. The apperceptive defect affected not just configuration but also feature size and external contour, especially in the upper face, and was more severe when subjects attended to multiple aspects of the face. An amnestic profile, with severely impaired imagery and minimally affected perception, was seen in two patients, one with right and one with bilateral anterior temporal damage. Four patients had an apperceptive/amnestic combination, all with bilateral occipitotemporal and right anterior temporal damage. Right anterior temporal damage alone often caused only mild imagery deficits: along with their relatively intact face perception, these subjects came closest to meeting proposed exclusionary criteria for an associative variant, i.e., relative preservation of both imagery and perception. These results confirm a link between apperceptive prosopagnosia and occipitotemporal lesions. Damage to the right anterior temporal lobe was common to all with a severe amnestic deficit, but often requiring additional damage.

The neural dynamics of familiar face recognition

Humans are highly efficient at recognising familiar faces. However, previous EEG/ERP research has given a partial and fragmented account of the neural basis of this remarkable ability. We argue that this is related to insufficient consideration of fundamental characteristics of familiar face recognition. These include image-independence (recognition across different pictures), levels of familiarity (familiar faces vary hugely in duration and intensity of our exposure to them), automaticity (we cannot voluntarily withhold from recognising a familiar face), and domain-selectivity (the degree to which face familiarity effects are selective). We review recent EEG/ERP work, combining uni- and multivariate methods, that has systematically targeted these shortcomings. We present a theoretical account of familiar face recognition, dividing it into early visual, domain-sensitive and domain-general phases, and integrating image-independence and levels of familiarity. Our account incorporates classic and more recent concepts, such as multi-dimensional face representation and course-to-fine processing. While several questions remain to be addressed, this new account represents a major step forward in our understanding of the neurophysiological basis of familiar face recognition.

Microstructural and functional substrates underlying dispositional greed and its link with trait but not state impulsivity

The interplay between personality traits and impulsivity has long been a central theme in psychology and psychiatry. However, the potential association between Greed Personality Traits (GPT) and impulsivity, encompassing both trait and state impulsivity and future time perspective, remains largely unexplored. To address these issues, we employed questionnaires and an inter-temporal choice task to estimate corresponding trait/state impulsivity and collected multi-modal neuroimaging data (resting-state functional imaging: n = 430; diffusion-weighted imaging: n = 426; task-related functional imaging: n = 53) to investigate the underlying microstructural and functional substrates. Behavioral analyses revealed that GPT mediated the association between time perspective (e.g., present fatalism) and trait impulsivity (e.g., motor impulsivity). Functional imaging analyses further identified that brain activation strengths and patterns related to delay length, particularly in the dorsomedial prefrontal cortex, superior parietal lobule, and cerebellum, were associated with GPT. Moreover, individuals with similar levels of greed exhibited analogous spontaneous brain activity patterns, predominantly in the Default Mode Network (DMN), Fronto-Parietal Network (FPN), and Visual Network (VIS). Diffusion imaging analysis observed specific microstructural characteristics in the spinocerebellar/pontocerebellar fasciculus, internal/external capsule, and corona radiata that support the formation of GPT. Furthermore, the corresponding neural activation pattern, spontaneous neural activity pattern, and analogous functional couplings among the aforementioned brain regions mediated the relationships between time perspective and GPT and between GPT and motor impulsivity. These findings provide novel insights into the possible pathway such as time perspective → dispositional greed → impulsivity and uncover their underlying microstructural and functional substrates.

The 2024 Richardson Lecture: Prosopagnosia - A Classic Neurologic Deficit Meets the Modern Era

Acquired prosopagnosia is a rare disorder, but it serves as a model for impairments in expert-level visual processing. This review discusses five key observations made over the past 30 years. First, there are variants, an apperceptive type linked to damage to the inferior occipitotemporal cortex and an amnestic type associated with anterior temporal lesions, both either right or bilateral. Second, these variants are clustered in syndromes with other perceptual deficits, the apperceptive type with field defects, dyschromatopsia and topographagnosia, and the amnestic type with topographagnosia and the auditory disorders of phonagnosia and acquired amusia. Third, extensive testing often shows additional problems with recognizing exemplars of other objects, especially when degrees of expertise are taken into account. Fourth, the prosopagnosic impairment does not affect all facial information. For example, the perception of expression and lip-reading likely depends on other neural substrates than those for processing facial identity. Last, face perception in prosopagnosia is not immutable but can improve with extensive training, though as yet this does not represent a cure for the condition. Continuing work with neural networks and animal models will enhance our understanding of this intriguing condition and what it tells us about how our brains process vision.

Object color knowledge representation occurs in the macaque brain despite the absence of a developed language system

Animals guide their behaviors through internal representations of the world in the brain. We aimed to understand how the macaque brain stores such general world knowledge, focusing on object color knowledge. Three functional magnetic resonance imaging (fMRI) experiments were conducted in macaque monkeys: viewing chromatic and achromatic gratings, viewing grayscale images of their familiar fruits and vegetables (e.g., grayscale strawberry), and viewing true- and false-colored objects (e.g., red strawberry and green strawberry). We observed robust object knowledge representations in the color patches, especially the one located around TEO: the activity patterns could classify grayscale pictures of objects based on their memory color and response patterns in these regions could translate between chromatic grating viewing and grayscale object viewing (e.g., red grating-grayscale images of strawberry), such that classifiers trained by viewing chromatic gratings could successfully classify grayscale object images according to their memory colors. Our results showed direct positive evidence of object color memory in macaque monkeys. These results indicate the perceptually grounded knowledge representation as a conservative memory mechanism and open a new avenue to study this particular (semantic) memory representation with macaque models.

The big mixup: Neural representation during natural modes of primate visual behavior

The primate brain has evolved specialized visual capacities to navigate complex physical and social environments. Researchers studying cortical circuits underlying these capacities have traditionally favored the use of simplified tasks and brief stimulus presentations in order to isolate cognitive variables with tight experimental control. As a result, operational theories about visual brain function have come to emphasize feature detection, hierarchical stimulus encoding, top-down task modulation, and functional segregation in distinct cortical areas. Recently, however, experimental paradigms combining natural behavior with electrophysiological recordings have begun to offer a distinctly different portrait of how the brain takes in and analyzes its visual surroundings. The present article reviews recent work in this area, highlighting some of the more surprising findings in domains of social vision and spatial navigation along with shifts in thinking that have begun to emanate from this approach.

Neuronal Mechanisms Underlying Face Recognition in Non-human Primates

Humans and primates rely on visual face recognition for social interactions. Damage to specific brain areas causes prosopagnosia, a condition characterized by the inability to recognize familiar faces, indicating the presence of specialized brain areas for face processing. A breakthrough finding came from a non-human primate (NHP) study conducted in the early 2000s; it was the first to identify multiple face processing areas in the temporal lobe, termed face patches. Subsequent studies have demonstrated the unique role of each face patch in the structural analysis of faces. More recent studies have expanded these findings by exploring the role of face patch networks in social and memory functions and the importance of early face exposure in the development of the system. In this review, we discuss the neuronal mechanisms responsible for analyzing facial features, categorizing faces, and associating faces with memory and social contexts within both the cerebral cortex and subcortical areas. Use of NHPs in neuropsychological and neurophysiological studies can highlight the mechanistic aspects of the neuronal circuit underlying face recognition at both the single-neuron and whole-brain network levels.

A familiar face and person processing area in the human temporal pole

How does the brain process the faces of familiar people? Neuropsychological studies have argued for an area of the temporal pole (TP) linking faces with person identities, but magnetic susceptibility artifacts in this region have hampered its study with fMRI. Using data acquisition and analysis methods optimized to overcome this artifact, we identify a familiar face response in TP, reliably observed in individual brains. This area responds strongly to visual images of familiar faces over unfamiliar faces, objects, and scenes. However, TP did not just respond to images of faces, but also to a variety of high-level social cognitive tasks, including semantic, episodic, and theory of mind tasks. The response profile of TP contrasted with a nearby region of the perirhinal cortex that responded specifically to faces, but not to social cognition tasks. TP was functionally connected with a distributed network in the association cortex associated with social cognition, while PR was functionally connected with face-preferring areas of the ventral visual cortex. This work identifies a missing link in the human face processing system that specifically processes familiar faces, and is well placed to integrate visual information about faces with higher-order conceptual information about other people. The results suggest that separate streams for person and face processing reach anterior temporal areas positioned at the top of the cortical hierarchy.

Simultaneous oscillatory encoding of "hot" and "cold" information during social interactions in the monkey medial prefrontal cortex

Social interactions in primates require social cognition abilities such as anticipating the partner's future choices as well as pure cognitive skills involving processing task-relevant information. The medial prefrontal cortex (mPFC) has been implicated in these cognitive processes. Here, we investigated the neural oscillations underlying the complex social behaviors involving the interplay of social roles (Actor vs. Observer) and interaction types (whether working with a "Good" or "Bad" partner). We found opposite power modulations of the beta and gamma bands by social roles, indicating dedicated processing for task-related information. Concurrently, the interaction type was conveyed by lower frequencies, which are commonly associated with neural circuits linked to performance and reward monitoring. Thus, the mPFC exhibits parallel coding of both "cold" processes (purely cognitive) and "hot" processes (reward and social-related). This allocation of neural resources gives the mPFC a key neural node, flexibly integrating multiple sources of information during social interactions.

Temporal multiplexing of perception and memory codes in IT cortex

A central assumption of neuroscience is that long-term memories are represented by the same brain areas that encode sensory stimuli1. Neurons in inferotemporal (IT) cortex represent the sensory percept of visual objects using a distributed axis code2-4. Whether and how the same IT neural population represents the long-term memory of visual objects remains unclear. Here we examined how familiar faces are encoded in the IT anterior medial face patch (AM), perirhinal face patch (PR) and temporal pole face patch (TP). In AM and PR we observed that the encoding axis for familiar faces is rotated relative to that for unfamiliar faces at long latency; in TP this memory-related rotation was much weaker. Contrary to previous claims, the relative response magnitude to familiar versus unfamiliar faces was not a stable indicator of familiarity in any patch5-11. The mechanism underlying the memory-related axis change is likely intrinsic to IT cortex, because inactivation of PR did not affect axis change dynamics in AM. Overall, our results suggest that memories of familiar faces are represented in AM and perirhinal cortex by a distinct long-latency code, explaining how the same cell population can encode both the percept and memory of faces.

Cortical thickness and sub-cortical volumes in post-H1N1 narcolepsy type 1: A brain-wide MRI case-control study

OBJECTIVE

There was more than a 10-fold increase in the incidence of narcolepsy type 1 (NT1) after the H1N1 mass vaccination in 2009/2010 in several countries. NT1 is associated with loss and increase of cell groups in the hypothalamus which may be associated with secondary affected sub-cortical and cortical gray matter. We performed a case-control comparison of MRI-based global and sub-cortical volume and cortical thickness in post-H1N1 NT1 patients compared with controls.

METHODS

We included 54 post-H1N1 NT1 patients (51 with confirmed hypocretin-deficiency; 48 H1N1-vaccinated with Pandemrix®; 39 females, mean age 21.8 ± 11.0 years) and 114 healthy controls (77 females, mean age 23.2 ± 9.0 years). 3T MRI brain scans were obtained, and the T1-weighted MRI data were processed using FreeSurfer. Group differences among three global, 10 sub-cortical volume measures and 34 cortical thickness measures for bilateral brain regions were tested using general linear models with permutation testing.

RESULTS

Patients had significantly thinner brain cortex bilaterally in the temporal poles (Cohen's d = 0.68, p = 0.00080), entorhinal cortex (d = 0.60, p = 0.0018) and superior temporal gyrus (d = 0.60, p = 0.0020) compared to healthy controls. The analysis revealed no significant group differences for sub-cortical volumes.

CONCLUSIONS

Post-H1N1(largely Pandemrix®-vaccinated) NT1 patients have significantly thinner cortex in temporal brain regions compared to controls. We speculate that this effect can be partly attributed to the hypothalamic neuronal change in NT1, including loss of function of the widely projecting hypocretin-producing neurons and secondary effects of the abnormal sleep-wake pattern in NT1 or could be specific for post-H1N1 (largely Pandemrix®-vaccinated) NT1 patients.

Neural mechanisms of face familiarity and learning in the human amygdala and hippocampus

Recognizing familiar faces and learning new faces play an important role in social cognition. However, the underlying neural computational mechanisms remain unclear. Here, we record from single neurons in the human amygdala and hippocampus and find a greater neuronal representational distance between pairs of familiar faces than unfamiliar faces, suggesting that neural representations for familiar faces are more distinct. Representational distance increases with exposures to the same identity, suggesting that neural face representations are sharpened with learning and familiarization. Furthermore, representational distance is positively correlated with visual dissimilarity between faces, and exposure to visually similar faces increases representational distance, thus sharpening neural representations. Finally, we construct a computational model that demonstrates an increase in the representational distance of artificial units with training. Together, our results suggest that the neuronal population geometry, quantified by the representational distance, encodes face familiarity, similarity, and learning, forming the basis of face recognition and memory.

Cortical field maps across human sensory cortex

Cortical processing pathways for sensory information in the mammalian brain tend to be organized into topographical representations that encode various fundamental sensory dimensions. Numerous laboratories have now shown how these representations are organized into numerous cortical field maps (CMFs) across visual and auditory cortex, with each CFM supporting a specialized computation or set of computations that underlie the associated perceptual behaviors. An individual CFM is defined by two orthogonal topographical gradients that reflect two essential aspects of feature space for that sense. Multiple adjacent CFMs are then organized across visual and auditory cortex into macrostructural patterns termed cloverleaf clusters. CFMs within cloverleaf clusters are thought to share properties such as receptive field distribution, cortical magnification, and processing specialization. Recent measurements point to the likely existence of CFMs in the other senses, as well, with topographical representations of at least one sensory dimension demonstrated in somatosensory, gustatory, and possibly olfactory cortical pathways. Here we discuss the evidence for CFM and cloverleaf cluster organization across human sensory cortex as well as approaches used to identify such organizational patterns. Knowledge of how these topographical representations are organized across cortex provides us with insight into how our conscious perceptions are created from our basic sensory inputs. In addition, studying how these representations change during development, trauma, and disease serves as an important tool for developing improvements in clinical therapies and rehabilitation for sensory deficits.

Your place or mine? The neural dynamics of personally familiar scene recognition suggests category independent familiarity encoding

Recognizing a stimulus as familiar is an important capacity in our everyday life. Recent investigation of visual processes has led to important insights into the nature of the neural representations of familiarity for human faces. Still, little is known about how familiarity affects the neural dynamics of non-face stimulus processing. Here we report the results of an EEG study, examining the representational dynamics of personally familiar scenes. Participants viewed highly variable images of their own apartments and unfamiliar ones, as well as personally familiar and unfamiliar faces. Multivariate pattern analyses were used to examine the time course of differential processing of familiar and unfamiliar stimuli. Time-resolved classification revealed that familiarity is decodable from the EEG data similarly for scenes and faces. The temporal dynamics showed delayed onsets and peaks for scenes as compared to faces. Familiarity information, starting at 200 ms, generalized across stimulus categories and led to a robust familiarity effect. In addition, familiarity enhanced category representations in early (250-300 ms) and later (>400 ms) processing stages. Our results extend previous face familiarity results to another stimulus category and suggest that familiarity as a construct can be understood as a general, stimulus-independent processing step during recognition.

Multilayer network analysis of dynamic network reconfiguration in patients with moderate-to-severe obstructive sleep apnea and its association with neurocognitive function

BACKGROUND

Brain functional network disruption and neurocognitive dysfunction have been reported in obstructive sleep apnea (OSA) patients. Nevertheless, most research studies static networks, while brain evolution continues dynamically.

PURPOSE

To investigate the characteristics of dynamical networks in moderate-to-severe OSA patients using multilayer network analysis of dynamic networks and compare their association with neurocognitive function.

METHODS

Twenty-seven moderate-to-severe OSA patients and twenty-five matched healthy controls (HCs) who completed the examination of the Epworth sleepiness scale (ESS), neurocognitive function, polysomnography, and functional magnetic resonance imaging (fMRI) were prospectively included. The dynamic variations of resting-state functional networks in both groups were described via network switching rate. Switching rates and their correlation with clinical parameters were analyzed.

RESULTS

At the global level, network switching rates were notably lower in the OSA group than in the HCs group (p = 0.002). More specifically, the differences include the default mode network (DMN), auditory network, and ventral attention network at the subnetwork level, and the right rolandic operculum, left middle temporal gyrus, and right precentral gyrus at the nodal level. Furthermore, these altered switching rates have a close correlation with ESS, sleep parameters, and neurocognitive function.

CONCLUSION

Patients with moderate-to-severe OSA showed lower network switching rates, especially in the DMN, auditory network, and ventral attention network. The disruption of dynamic functional networks may be a potentially crucial mechanism of neurocognitive dysfunction in moderate-to-severe OSA patients.

Cross-modal representation of identity in the primate hippocampus

Faces and voices are the dominant social signals used to recognize individuals among primates. Yet, it is not known how these signals are integrated into a cross-modal representation of individual identity in the primate brain. We discovered that, although single neurons in the marmoset hippocampus exhibited selective responses when presented with the face or voice of a specific individual, a parallel mechanism for representing the cross-modal identities for multiple individuals was evident within single neurons and at the population level. Manifold projections likewise showed the separability of individuals as well as clustering for others' families, which suggests that multiple learned social categories are encoded as related dimensions of identity in the hippocampus. Neural representations of identity in the hippocampus are thus both modality independent and reflect the primate social network.

Modeling Biological Face Recognition with Deep Convolutional Neural Networks

Deep convolutional neural networks (DCNNs) have become the state-of-the-art computational models of biological object recognition. Their remarkable success has helped vision science break new ground, and recent efforts have started to transfer this achievement to research on biological face recognition. In this regard, face detection can be investigated by comparing face-selective biological neurons and brain areas to artificial neurons and model layers. Similarly, face identification can be examined by comparing in vivo and in silico multidimensional "face spaces." In this review, we summarize the first studies that use DCNNs to model biological face recognition. On the basis of a broad spectrum of behavioral and computational evidence, we conclude that DCNNs are useful models that closely resemble the general hierarchical organization of face recognition in the ventral visual pathway and the core face network. In two exemplary spotlights, we emphasize the unique scientific contributions of these models. First, studies on face detection in DCNNs indicate that elementary face selectivity emerges automatically through feedforward processing even in the absence of visual experience. Second, studies on face identification in DCNNs suggest that identity-specific experience and generative mechanisms facilitate this particular challenge. Taken together, as this novel modeling approach enables close control of predisposition (i.e., architecture) and experience (i.e., training data), it may be suited to inform long-standing debates on the substrates of biological face recognition.

Single neuron responses underlying face recognition in the human midfusiform face-selective cortex

Faces are critical for social interactions and their recognition constitutes one of the most important and challenging functions of the human brain. While neurons responding selectively to faces have been recorded for decades in the monkey brain, face-selective neural activations have been reported with neuroimaging primarily in the human midfusiform gyrus. Yet, the cellular mechanisms producing selective responses to faces in this hominoid neuroanatomical structure remain unknown. Here we report single neuron recordings performed in 5 human subjects (1 male, 4 females) implanted with intracerebral microelectrodes in the face-selective midfusiform gyrus, while they viewed pictures of familiar and unknown faces and places. We observed similar responses to faces and places at the single cell level, but a significantly higher number of neurons responding to faces, thus offering a mechanistic account for the face-selective activations observed in this region. Although individual neurons did not respond preferentially to familiar faces, a population level analysis could consistently determine whether or not the faces (but not the places) were familiar, only about 50 ms after the initial recognition of the stimuli as faces. These results provide insights into the neural mechanisms of face processing in the human brain.

NMDA-driven dendritic modulation enables multitask representation learning in hierarchical sensory processing pathways

While sensory representations in the brain depend on context, it remains unclear how such modulations are implemented at the biophysical level, and how processing layers further in the hierarchy can extract useful features for each possible contextual state. Here, we demonstrate that dendritic N-Methyl-D-Aspartate spikes can, within physiological constraints, implement contextual modulation of feedforward processing. Such neuron-specific modulations exploit prior knowledge, encoded in stable feedforward weights, to achieve transfer learning across contexts. In a network of biophysically realistic neuron models with context-independent feedforward weights, we show that modulatory inputs to dendritic branches can solve linearly nonseparable learning problems with a Hebbian, error-modulated learning rule. We also demonstrate that local prediction of whether representations originate either from different inputs, or from different contextual modulations of the same input, results in representation learning of hierarchical feedforward weights across processing layers that accommodate a multitude of contexts.

Specialized Networks for Social Cognition in the Primate Brain

Primates have evolved diverse cognitive capabilities to navigate their complex social world. To understand how the brain implements critical social cognitive abilities, we describe functional specialization in the domains of face processing, social interaction understanding, and mental state attribution. Systems for face processing are specialized from the level of single cells to populations of neurons within brain regions to hierarchically organized networks that extract and represent abstract social information. Such functional specialization is not confined to the sensorimotor periphery but appears to be a pervasive theme of primate brain organization all the way to the apex regions of cortical hierarchies. Circuits processing social information are juxtaposed with parallel systems involved in processing nonsocial information, suggesting common computations applied to different domains. The emerging picture of the neural basis of social cognition is a set of distinct but interacting subnetworks involved in component processes such as face perception and social reasoning, traversing large parts of the primate brain.

Representational geometry of incomplete faces in macaque face patches

The neural code of faces has been intensively studied in the macaque face patch system. Although the majority of previous studies used complete faces as stimuli, faces are often seen partially in daily life. Here, we investigated how face-selective cells represent two types of incomplete faces: face fragments and occluded faces, with the location of the fragment/occluder and the facial features systematically varied. Contrary to popular belief, we found that the preferred face regions identified with two stimulus types are dissociated in many face cells. This dissociation can be explained by the nonlinear integration of information from different face parts and is closely related to a curved representation of face completeness in the state space, which allows a clear discrimination between different stimulus types. Furthermore, identity-related facial features are represented in a subspace orthogonal to the nonlinear dimension of face completeness, supporting a condition-general code of facial identity.

Multimodal deep neural decoding reveals highly resolved spatiotemporal profile of visual object representation in humans

Perception and categorization of objects in a visual scene are essential to grasp the surrounding situation. Recently, neural decoding schemes, such as machine learning in functional magnetic resonance imaging (fMRI), has been employed to elucidate the underlying neural mechanisms. However, it remains unclear as to how spatially distributed brain regions temporally represent visual object categories and sub-categories. One promising strategy to address this issue is neural decoding with concurrently obtained neural response data of high spatial and temporal resolution. In this study, we explored the spatial and temporal organization of visual object representations using concurrent fMRI and electroencephalography (EEG), combined with neural decoding using deep neural networks (DNNs). We hypothesized that neural decoding by multimodal neural data with DNN would show high classification performance in visual object categorization (faces or non-face objects) and sub-categorization within faces and objects. Visualization of the fMRI DNN was more sensitive than that in the univariate approach and revealed that visual categorization occurred in brain-wide regions. Interestingly, the EEG DNN valued the earlier phase of neural responses for categorization and the later phase of neural responses for sub-categorization. Combination of the two DNNs improved the classification performance for both categorization and sub-categorization compared with fMRI DNN or EEG DNN alone. These deep learning-based results demonstrate a categorization principle in which visual objects are represented in a spatially organized and coarse-to-fine manner, and provide strong evidence of the ability of multimodal deep learning to uncover spatiotemporal neural machinery in sensory processing.

Uncovering the fast, directional signal flow through the human temporal pole during semantic processing

The temporal pole (TP) plays a central role in semantic memory, yet its neural machinery is unknown. Intracerebral recordings in patients discriminating visually the gender or actions of an actor, yielded gender discrimination responses in the ventrolateral (VL) and tip (T) regions of right TP. Granger causality revealed task-specific signals travelling first forward from VL to T, under control of orbitofrontal cortex (OFC) and neighboring prefrontal cortex, and then, strongly, backwards from T to VL. Many other cortical regions provided inputs to or received outputs from both TP regions, often with longer delays, with ventral temporal afferents to VL signaling the actor's physical appearance. The TP response timing reflected more that of the connections to VL, controlled by OFC, than that of the input leads themselves. Thus, visual evidence for gender categories, collected by VL, activates category labels in T, and consequently, category features in VL, indicating a two-stage representation of semantic categories in TP.

Progressive neuronal plasticity in primate visual cortex during stimulus familiarization

The primate brain is equipped to learn and remember newly encountered visual stimuli such as faces and objects. In the macaque inferior temporal (IT) cortex, neurons mark the familiarity of a visual stimulus through response modification, often involving a decrease in spiking rate. Here, we investigate the emergence of this neural plasticity by longitudinally tracking IT neurons during several weeks of familiarization with face images. We found that most neurons in the anterior medial (AM) face patch exhibited a gradual decline in their late-phase visual responses to multiple stimuli. Individual neurons varied from days to weeks in their rates of plasticity, with time constants determined by the number of days of exposure rather than the cumulative number of presentations. We postulate that the sequential recruitment of neurons with experience-modified responses may provide an internal and graded measure of familiarity strength, which is a key mnemonic component of visual recognition.

Functional connectivity fingerprints of the frontal eye field and inferior frontal junction suggest spatial versus nonspatial processing in the prefrontal cortex

Neuroimaging evidence suggests that the frontal eye field (FEF) and inferior frontal junction (IFJ) govern the encoding of spatial and nonspatial (such as feature- or object-based) representations, respectively, both during visual attention and working memory tasks. However, it is still unclear whether such contrasting functional segregation is also reflected in their underlying functional connectivity patterns. Here, we hypothesized that FEF has predominant functional coupling with spatiotopically organized regions in the dorsal ('where') visual stream whereas IFJ has predominant functional connectivity with the ventral ('what') visual stream. We applied seed-based functional connectivity analyses to temporally high-resolving resting-state magnetoencephalography (MEG) recordings. We parcellated the brain according to the multimodal Glasser atlas and tested, for various frequency bands, whether the spontaneous activity of each parcel in the ventral and dorsal visual pathway has predominant functional connectivity with FEF or IFJ. The results show that FEF has a robust power correlation with the dorsal visual pathway in beta and gamma bands. In contrast, anterior IFJ (IFJa) has a strong power coupling with the ventral visual stream in delta, beta and gamma oscillations. Moreover, while FEF is phase-coupled with the superior parietal lobe in the beta band, IFJa is phase-coupled with the middle and inferior temporal cortex in delta and gamma oscillations. We argue that these intrinsic connectivity fingerprints are congruent with each brain region's function. Therefore, we conclude that FEF and IFJ have dissociable connectivity patterns that fit their respective functional roles in spatial versus nonspatial top-down attention and working memory control.

Evaluating the impact of short educational videos on the cortical networks for mathematics

Many teaching websites, such as the Khan Academy, propose vivid videos illustrating a mathematical concept. Using functional magnetic resonance imaging, we asked whether watching such a video suffices to rapidly change the brain networks for mathematical knowledge. We capitalized on the finding that, when judging the truth of short spoken statements, distinct semantic regions activate depending on whether the statements bear on mathematical knowledge or on other domains of semantic knowledge. Here, participants answered such questions before and after watching a lively 5-min video, which taught them the rudiments of a new domain. During the video, a distinct math-responsive network, comprising anterior intraparietal and inferior temporal nodes, showed intersubject synchrony when viewing mathematics course rather than control courses in biology or law. However, this experience led to minimal subsequent changes in the activity of those domain-specific areas when answering questions on the same topics a few minutes later. All taught facts, whether mathematical or not, led to domain-general repetition enhancement, particularly prominent in the cuneus, posterior cingulate, and posterior parietal cortices. We conclude that short videos do not suffice to induce a meaningful lasting change in the brain's math-responsive network, but merely engage domain-general regions possibly involved in episodic short-term memory.

Bidirectional and parallel relationships in macaque face circuit revealed by fMRI and causal pharmacological inactivation

Although the presence of face patches in primate inferotemporal (IT) cortex is well established, the functional and causal relationships among these patches remain elusive. In two monkeys, muscimol was infused sequentially into each patch or pair of patches to assess their respective influence on the remaining IT face network and the amygdala, as determined using fMRI. The results revealed that anterior face patches required input from middle face patches for their responses to both faces and objects, while the face selectivity in middle face patches arose, in part, from top-down input from anterior face patches. Moreover, we uncovered a parallel fundal-lateral functional organization in the IT face network, supporting dual routes (dorsal-ventral) in face processing within IT cortex as well as between IT cortex and the amygdala. Our findings of the causal relationship among the face patches demonstrate that the IT face circuit is organized into multiple functional compartments.

An opinion on Wastewater-Based Epidemiological Monitoring (WBEM) with Clinical Diagnostic Test (CDT) for detecting high-prevalence areas of community COVID-19 Infections

Wastewater-Based Epidemiological Monitoring (WBEM) is an efficient surveillance tool during the COVID-19 pandemic as it meets all requirements of a complete monitoring system including early warning, tracking the current trend, prevalence of the disease, detection of genetic diversity as well asthe up-surging SARS-CoV-2 new variants with mutations from the wastewater samples. Subsequently, Clinical Diagnostic Test is widely acknowledged as the global gold standard method for disease monitoring, despite several drawbacks such as high diagnosis cost, reporting bias, and the difficulty of tracking asymptomatic patients (silent spreaders of the COVID-19 infection who manifest nosymptoms of the disease). In this current reviewand opinion-based study, we first propose a combined approach) for detecting COVID-19 infection in communities using wastewater and clinical sample testing, which may be feasible and effective as an emerging public health tool for the long-term nationwide surveillance system. The viral concentrations in wastewater samples can be used as indicatorsto monitor ongoing SARS-CoV-2 trends, predict asymptomatic carriers, and detect COVID-19 hotspot areas, while clinical sampleshelp in detecting mostlysymptomaticindividuals for isolating positive cases in communities and validate WBEM protocol for mass vaccination including booster doses for COVID-19.

Top-down modulation and cortical-AMG/HPC interaction in familiar face processing

Humans can accurately recognize familiar faces in only a few hundred milliseconds, but the underlying neural mechanism remains unclear. Here, we recorded intracranial electrophysiological signals from ventral temporal cortex (VTC), superior/middle temporal cortex (STC/MTC), medial parietal cortex (MPC), and amygdala/hippocampus (AMG/HPC) in 20 epilepsy patients while they viewed faces of famous people and strangers as well as common objects. In posterior VTC and MPC, familiarity-sensitive responses emerged significantly later than initial face-selective responses, suggesting that familiarity enhances face representations after they are first being extracted. Moreover, viewing famous faces increased the coupling between cortical areas and AMG/HPC in multiple frequency bands. These findings advance our understanding of the neural basis of familiar face perception by identifying the top-down modulation in local face-selective response and interactions between cortical face areas and AMG/HPC.

Local features drive identity responses in macaque anterior face patches

Humans and other primates recognize one another in part based on unique structural details of the face, including both local features and their spatial configuration within the head and body. Visual analysis of the face is supported by specialized regions of the primate cerebral cortex, which in macaques are commonly known as face patches. Here we ask whether the responses of neurons in anterior face patches, thought to encode face identity, are more strongly driven by local or holistic facial structure. We created stimuli consisting of recombinant photorealistic images of macaques, where we interchanged the eyes, mouth, head, and body between individuals. Unexpectedly, neurons in the anterior medial (AM) and anterior fundus (AF) face patches were predominantly tuned to local facial features, with minimal neural selectivity for feature combinations. These findings indicate that the high-level structural encoding of face identity rests upon populations of neurons specialized for local features.

Anterior face patches in the macaque have been assumed to represent face identity in a holistic manner. Here the authors show that the neural encoding of face identity in the anterior medial and anterior fundus face patches are instead driven principally by local features.

Multiple-stage impairments of unfamiliar face learning in developmental prosopagnosia: Evidence from fMRI repetition suppression and multi-voxel pattern stability

Individuals with developmental prosopagnosia (DP) are characterized by severe face recognition deficits, yet it remains unknown how they are hindered in the process of unfamiliar face learning. Here we tracked the changes of neural activation during unfamiliar face repetition in DP with fMRI to reveal their neural deficits in learning unfamiliar faces. At the perceptual level, we found that the bilateral fusiform face area (FFA) in individuals with DP showed attenuated repetition suppression for faces, suggesting an inefficient perceptual analysis for learned faces. At the mnemonic level, individuals with DP showed decreased multi-voxel pattern stability for repeated faces in bilateral medial temporal lobe (MTL), suggesting an unstable mnemonic representation for learned faces. In addition, resting-state functional connectivity between the FFA and MTL was also disrupted in individuals with DP. Finally, the MTL's unstable mnemonic representation was associated with the impaired face recognition performance in DP. In sum, our study provides evidence that individuals with DP showed multi-stage neural deficits in unfamiliar face learning and sheds new light on how unfamiliar faces are learned in normal population.

Predicting conversion from mild cognitive impairment to Alzheimer's disease with multimodal latent factors

INTRODUCTION

We studied the ability of latent factor scores to predict conversion from mild cognitive impairment (MCI) to Alzheimer's disease (AD) and investigated whether multimodal factor scores improve predictive power, relative to single-modal factor scores.

METHOD

We conducted exploratory factor analyses (EFAs) and confirmatory factor analyses (CFAs) of the baseline data of MCI subjects in the Alzheimer's Disease Neuroimaging Initiative (ADNI) to generate factor scores for three data modalities: neuropsychological (NP), magnetic resonance imaging (MRI), and cerebrospinal fluid (CSF). Factor scores from single or multiple modalities were entered in logistic regression models to predict MCI to AD conversion for 160 ADNI subjects over a 2-year interval.

RESULTS

NP factors attained an area under the curve (AUC) of .80, with a sensitivity of .66 and a specificity of .77. MRI factors reached a comparable level of performance (AUC = .80, sensitivity = .66, specificity = .78), whereas CSF factors produced weaker prediction (AUC = .70, sensitivity = .56, specificity = .79). Combining NP factors with MRI or CSF factors produced better prediction than either MRI or CSF factors alone. Similarly, adding MRI factors to NP or CSF factors produced improvements in prediction relative to NP or CSF factors alone. However, adding CSF factors to either NP or MRI factors produced no improvement in prediction.

CONCLUSIONS

Latent factor scores provided good accuracy for predicting MCI to AD conversion. Adding NP or MRI factors to factors from other modalities enhanced predictive power but adding CSF factors did not.

Widespread implementations of interactive social gaze neurons in the primate prefrontal-amygdala networks

Social gaze interaction powerfully shapes interpersonal communication. However, compared with social perception, very little is known about the neuronal underpinnings of real-life social gaze interaction. Here, we studied a large number of neurons spanning four regions in primate prefrontal-amygdala networks and demonstrate robust single-cell foundations of interactive social gaze in the orbitofrontal, dorsomedial prefrontal, and anterior cingulate cortices, in addition to the amygdala. Many neurons in these areas exhibited high temporal heterogeneity for social discriminability, with a selectivity bias for looking at a conspecific compared with an object. Notably, a large proportion of neurons in each brain region parametrically tracked the gaze of self or other, providing substrates for social gaze monitoring. Furthermore, several neurons displayed selective encoding of mutual eye contact in an agent-specific manner. These findings provide evidence of widespread implementations of interactive social gaze neurons in the primate prefrontal-amygdala networks during social gaze interaction.

The cortical and subcortical correlates of face pareidolia in the macaque brain

Face detection is a foundational social skill for primates. This vital function is thought to be supported by specialized neural mechanisms; however, although several face-selective regions have been identified in both humans and nonhuman primates, there is no consensus about which region(s) are involved in face detection. Here, we used naturally occurring errors of face detection (i.e. objects with illusory facial features referred to as examples of 'face pareidolia') to identify regions of the macaque brain implicated in face detection. Using whole-brain functional magnetic resonance imaging to test awake rhesus macaques, we discovered that a subset of face-selective patches in the inferior temporal cortex, on the lower lateral edge of the superior temporal sulcus, and the amygdala respond more to objects with illusory facial features than matched non-face objects. Multivariate analyses of the data revealed differences in the representation of illusory faces across the functionally defined regions of interest. These differences suggest that the cortical and subcortical face-selective regions contribute uniquely to the detection of facial features. We conclude that face detection is supported by a multiplexed system in the primate brain.

What kind of network is the brain?

The different areas of the cerebral cortex are linked by a network of white matter, comprising the myelinated axons of pyramidal cells. Is this network a neural net, in the sense that representations of the world are embodied in the structure of the net, its pattern of nodes, and connections? Or is it a communications network, where the same physical substrate carries different information from moment to moment? This question is part of the larger question of whether the brain is better modeled by connectionism or by symbolic artificial intelligence (AI), but we review it in the specific context of the psychophysics of stimulus comparison and the format and protocol of information transmission over the long-range tracts of the brain.

Time-Based Binding as a Solution to and a Limitation for Flexible Cognition

Why can't we keep as many items as we want in working memory? It has long been debated whether this resource limitation is a bug (a downside of our fallible biological system) or instead a feature (an optimal response to a computational problem). We propose that the resource limitation is a consequence of a useful feature. Specifically, we propose that flexible cognition requires time-based binding, and time-based binding necessarily limits the number of (bound) memoranda that can be stored simultaneously. Time-based binding is most naturally instantiated via neural oscillations, for which there exists ample experimental evidence. We report simulations that illustrate this theory and that relate it to empirical data. We also compare the theory to several other (feature and bug) resource theories.

Social agent identity cells in the prefrontal cortex of interacting groups of primates

[Figure: see text].

Toward collective animal neuroscience

[Figure: see text].

Neuronal Circuits for Social Decision-Making and Their Clinical Implications

Social living facilitates individual access to rewards, cognitive resources, and objects that would not be otherwise accessible. There are, however, some drawbacks to social living, particularly when competing for scarce resources. Furthermore, variability in our ability to make social decisions can be associated with neuropsychiatric disorders. The neuronal mechanisms underlying social decision-making are beginning to be understood. The momentum to study this phenomenon has been partially carried over by the study of economic decision-making. Yet, because of the similarities between these different types of decision-making, it is unclear what is a social decision. Here, we propose a definition of social decision-making as choices taken in a context where one or more conspecifics are involved in the decision or the consequences of it. Social decisions can be conceptualized as complex economic decisions since they are based on the subjective preferences between different goods. During social decisions, individuals choose based on their internal value estimate of the different alternatives. These are complex decisions given that conspecifics beliefs or actions could modify the subject's internal valuations at every choice. Here, we first review recent developments in our collective understanding of the neuronal mechanisms and circuits of social decision-making in primates. We then review literature characterizing populations with neuropsychiatric disorders showing deficits in social decision-making and the underlying neuronal circuitries associated with these deficits.

A fast link between face perception and memory in the temporal pole

The question of how the brain recognizes the faces of familiar individuals has been important throughout the history of neuroscience. Cells linking visual processing to person memory have been proposed but not found. Here, we report the discovery of such cells through recordings from an area in the macaque temporal pole identified with functional magnetic resonance imaging. These cells responded to faces that were personally familiar. They responded nonlinearly to stepwise changes in face visibility and detail and holistically to face parts, reflecting key signatures of familiar face recognition. They discriminated between familiar identities, as fast as a general face identity area. The discovery of these cells establishes a new pathway for the fast recognition of familiar individuals.