Seminars

Margaret Livingstone of Harvard Medical School presents in Princeton Neuroscience Institute's public seminar series. Livingstone's laboratory studies how visual recognition occurs in the primate brain using behavior, brain imaging, and electrophysiology.

Visual restoration after decades of blindness is now becoming possible by means of retinal and cortical prostheses, as well as emerging stem cell and gene therapeutic approaches. After restoring visual perception, however, a key question remains. Are there optimal means and methods for retraining the visual cortex to process visual inputs, and for learning or relearning to “see”? Up to this point, it has been largely assumed that if the sensory loss is visual, then the rehabilitation focus should also be primarily visual. However, the other senses play a key role in visual rehabilitation due to the plastic repurposing of visual cortex during blindness by audition and somatosensation, and also to the reintegration of restored vision with the other senses. I will present multisensory neuroimaging results, cortical thickness changes, as well as behavioral outcomes for patients with Retinitis Pigmentosa (RP), which causes blindness by destroying photoreceptors in the retina. These patients have had their vision partially restored by the implantation of a retinal prosthesis, which electrically stimulates still viable retinal ganglion cells in the eye. Our multisensory and structural neuroimaging and behavioral results suggest a new, holistic concept of visual rehabilitation that leverages rather than neglects audition, somatosensation, and other sensory modalities.

Our ability to recognize an object amongst many others is one of the most important features of the human mind. However, object recognition requires tremendous computational effort, as we need to solve a complex and recursive environment with ease and proficiency. This challenging feat is dependent on the implementation of an effective organization of knowledge in the brain. Here I put forth a novel understanding of how object knowledge is organized in the brain, by proposing that the organization of object knowledge follows key object-related dimensions, analogously to how sensory information is organized in the brain. Moreover, I will also put forth that this knowledge is topographically laid out in the cortical surface according to these object-related dimensions that code for different types of representational content – I call this contentopic mapping. I will show a combination of fMRI and behavioral data to support these hypotheses and present a principled way to explore the multidimensionality of object processing.

The visual system faces a dual challenge. On the one hand, features of the natural visual environment should be stably processed - irrespective of ongoing wiring changes, representational drift, and behavior. On the other hand, eye, head, and body motion require a robust integration of pose and gaze shifts in visual computations for a stable perception of the world. We address these dimensions of stable visual processing by studying the circuit mechanism of long-term representational stability, focusing on the role of plasticity, network structure, experience, and behavioral state while recording large-scale neuronal activity with miniature two-photon microscopy.

Trends in NeuroAI is a reading group hosted by the MedARC Neuroimaging & AI lab (https://medarc.ai/fmri). This will be an informal journal club presentation, we do not have an author of the paper joining us. Title: Brain decoding: toward real-time reconstruction of visual perception Abstract: In the past five years, the use of generative and foundational AI systems has greatly improved the decoding of brain activity. Visual perception, in particular, can now be decoded from functional Magnetic Resonance Imaging (fMRI) with remarkable fidelity. This neuroimaging technique, however, suffers from a limited temporal resolution (≈0.5 Hz) and thus fundamentally constrains its real-time usage. Here, we propose an alternative approach based on magnetoencephalography (MEG), a neuroimaging device capable of measuring brain activity with high temporal resolution (≈5,000 Hz). For this, we develop an MEG decoding model trained with both contrastive and regression objectives and consisting of three modules: i) pretrained embeddings obtained from the image, ii) an MEG module trained end-to-end and iii) a pretrained image generator. Our results are threefold: Firstly, our MEG decoder shows a 7X improvement of image-retrieval over classic linear decoders. Second, late brain responses to images are best decoded with DINOv2, a recent foundational image model. Third, image retrievals and generations both suggest that MEG signals primarily contain high-level visual features, whereas the same approach applied to 7T fMRI also recovers low-level features. Overall, these results provide an important step towards the decoding - in real time - of the visual processes continuously unfolding within the human brain. Speaker: Dr. Paul Scotti (Stability AI, MedARC) Paper link: https://arxiv.org/abs/2310.19812

Subsecond timing underlies nearly all sensory and motor activities across species and is critical to survival. While subsecond temporal information has been found across cortical and subcortical regions, it is unclear if it is generated locally and intrinsically or if it is a read out of a centralized clock-like mechanism. Indeed, mechanisms of subsecond timing at the circuit level are largely obscure. Primary sensory areas are well-suited to address these question as they have early access to sensory information and provide minimal processing to it: if temporal information is found in these regions, it is likely to be generated intrinsically and locally. We test this hypothesis by training mice to perform an audio-visual temporal pattern sensory discrimination task as we use 2-photon calcium imaging, a technique capable of recording population level activity at single cell resolution, to record activity in primary visual cortex (V1). We have found significant changes in network dynamics through mice’s learning of the task from naive to middle to expert levels. Changes in network dynamics and behavioral performance are well accounted for by an intrinsic model of timing in which the trajectory of q network through high dimensional state space represents temporal sensory information. Conversely, while we found evidence of other temporal encoding models, such as oscillatory activity, we did not find that they accounted for increased performance but were in fact correlated with the intrinsic model itself. These results provide insight into how subsecond temporal information is encoded mechanistically at the circuit level.

Algonauts 2023 was a challenge to create the best model that predicts fMRI brain activity given a seen image. Huze team dominated the competition and released a preprint detailing their process. This journal club meeting will involve open discussion of the paper with Q/A with Huze. Paper: https://arxiv.org/pdf/2308.01175.pdf Related paper also from Huze that we can discuss: https://arxiv.org/pdf/2307.14021.pdf

Any sensory experience of the world, from the touch of a caress to the smile on our friend’s face, is embedded in time and it is often associated with the perception of the flow of it. The perception of time is therefore a peculiar sensory experience built without dedicated sensors. How the perception of time and the content of a sensory experience interact to give rise to this unique percept is unclear. A few empirical evidences show the existence of this interaction, for example the speed of a moving object or the number of items displayed on a computer screen can bias the perceived duration of those objects. However, to what extent the coding of time is embedded within the coding of the stimulus itself, is sustained by the activity of the same or distinct neural populations and subserved by similar or distinct neural mechanisms is far from clear. Addressing these puzzles represents a way to gain insight on the mechanism(s) through which the brain represents the passage of time. In my talk I will present behavioral and neuroimaging studies to show how concurrent changes of visual stimulus duration, speed, visual contrast and numerosity, shape and modulate brain’s and pupil’s responses and, in case of numerosity and time, influence the topographic organization of these features along the cortical visual hierarchy.

Faces and voices convey much of the non-verbal information that we use when communicating with other people. We look at faces and listen to voices to recognize others, understand how they are feeling, and decide how to act. Recent research in my lab aims to investigate whether there are similar coding mechanisms to represent faces and voices, and whether there are brain regions that integrate information across the visual and auditory modalities. In the first part of my talk, I will focus on an fMRI study in which we found that a region of the posterior STS exhibits modality-general representations of familiar people that can be similarly driven by someone’s face and their voice (Tsantani et al. 2019). In the second part of the talk, I will describe our recent attempts to shed light on the type of information that is represented in different face-responsive brain regions (Tsantani et al., 2021).

The human middle temporal complex (hMT+) has a crucial biological relevance for the processing and detection of direction and speed of motion in visual stimuli. In both humans and monkeys, it has been extensively investigated in terms of its retinotopic properties and selectivity for direction of moving stimuli; however, only in recent years there has been an increasing interest in how neurons in MT encode the speed of motion. In this talk, I will explore the proposed mechanism of speed encoding questioning whether hMT+ neuronal populations encode the stimulus speed directly, or whether they separate motion into its spatial and temporal components. I will characterize how neuronal populations in hMT+ encode the speed of moving visual stimuli using electrocorticography ECoG and 7T fMRI. I will illustrate that the neuronal populations measured in hMT+ are not directly tuned to stimulus speed, but instead encode speed through separate and independent spatial and temporal frequency tuning. Finally, I will suggest that this mechanism may play a role in evaluating multisensory responses for visual, tactile and auditory stimuli in hMT+.

How does our brain generate consciousness, that is, the subjective experience of what it is like to see face or hear a sound? Do we become aware of a stimulus during early sensory processing or only later when information is shared in a wide-spread fronto-parietal network? Neural correlates of consciousness are typically identified by comparing brain activity when a constant stimulus (e.g., a face) is perceived versus not perceived. However, in most previous experiments, conscious perception was systematically confounded with post-perceptual processes such as decision-making and report. In this talk, I will present recent EEG and fMRI studies dissociating neural correlates of consciousness and task-related processing in visual and auditory perception. Our results suggest that consciousness emerges during early sensory processing, while late, fronto-parietal activity is associated with post-perceptual processes rather than awareness. These findings challenge predominant theories of consciousness and highlight the importance of considering task relevance as a confound across different neuroscientific methods, experimental paradigms and sensory modalities.

Quantifying the timing (duration and frequency) of brief visual events is vital to human perception, multisensory integration and action planning. For example, this allows us to follow and interact with the precise timing of speech and sports. Here we investigate how visual event timing is represented and transformed across the brain’s hierarchy: from sensory processing areas, through multisensory integration areas, to frontal action planning areas. We hypothesized that the dynamics of neural responses to sensory events in sensory processing areas allows derivation of event timing representations. This would allow higher-level processes such as multisensory integration and action planning to use sensory timing information, without the need for specialized central pacemakers or processes. Using 7T fMRI and neural model-based analyses, we found responses that monotonically increase in amplitude with visual event duration and frequency, becoming increasingly clear from primary visual cortex to lateral occipital visual field maps. Beginning in area MT/V5, we found a gradual transition from monotonic to tuned responses, with response amplitudes peaking at different event timings in different recording sites. While monotonic response components were limited to the retinotopic location of the visual stimulus, timing-tuned response components were independent of the recording sites' preferred visual field positions. These tuned responses formed a network of topographically organized timing maps in superior parietal, postcentral and frontal areas. From anterior to posterior timing maps, multiple events were increasingly integrated, response selectivity narrowed, and responses focused increasingly on the middle of the presented timing range. These results suggest that responses to event timing are transformed from the human brain’s sensory areas to the association cortices, with the event’s temporal properties being increasingly abstracted from the response dynamics and locations of early sensory processing. The resulting abstracted representation of event timing is then propagated through areas implicated in multisensory integration and action planning.

Cells preferentially responding to visual motion in a particular direction are said to be direction-selective, and these were first identified in the primary visual cortex. Since then, direction-selective responses have been observed in the retina of several species, including mice, indicating motion analysis begins at the earliest stage of the visual hierarchy. Yet little is known about how retinal direction selectivity contributes to motion processing in the visual cortex. In this talk, I will present our experimental efforts to narrow this gap in our knowledge. To this end, we used genetic approaches to disrupt direction selectivity in the retina and mapped neuronal responses to visual motion in the visual cortex of mice using intrinsic signal optical imaging and two-photon calcium imaging. In essence, our work demonstrates that direction selectivity computed at the level of the retina causally serves to establish specialized motion responses in distinct areas of the mouse visual cortex. This finding thus compels us to revisit our notions of how the brain builds complex visual representations and underscores the importance of the processing performed in the periphery of sensory systems.

The human middle temporal complex (hMT+) has a crucial biological relevance for the processing and detection of direction and speed of motion in visual stimuli. In both humans and monkeys, it has been extensively investigated in terms of its retinotopic properties and selectivity for direction of moving stimuli; however, only in recent years there has been an increasing interest in how neurons in MT encode the speed of motion. In this talk, I will explore the proposed mechanism of speed encoding questioning whether hMT+ neuronal populations encode the stimulus speed directly, or whether they separate motion into its spatial and temporal components. I will characterize how neuronal populations in hMT+ encode the speed of moving visual stimuli using electrocorticography ECoG and 7T fMRI. I will illustrate that the neuronal populations measured in hMT+ are not directly tuned to stimulus speed, but instead encode speed through separate and independent spatial and temporal frequency tuning. Finally, I will show that this mechanism plays a role in evaluating multisensory responses for visual, tactile and auditory motion stimuli in hMT+.

Sleep is crucial for the continuity and development of life. Sleep-related problems can alter brain function, and cause potentially severe psychological and behavioral consequences. However, the role of sleep in our mind and behavior is far from clear. In this talk, I will present our research on how sleep may play a role in visual perceptual learning (VPL) by using simultaneous magnetic resonance spectroscopy and polysomnography in human subjects. We measured the concentrations of neurotransmitters in the early visual areas during sleep and obtained the excitation/inhibition (E/I) ratio which represents the amount of plasticity in the visual system. We found that the E/I ratio significantly increased during NREM sleep while it decreased during REM sleep. The E/I ratio during NREM sleep was correlated with offline performance gains by sleep, while the E/I ratio during REM sleep was correlated with the amount of learning stabilization. These suggest that NREM sleep increases plasticity, while REM sleep decreases it to solidify once enhanced learning. NREM and REM sleep may play complementary roles, reflected by significantly different neurochemical processing, in VPL.

Statistics of natural scenes are not uniform - their structure varies dramatically from ground to sky. It remains unknown whether these non-uniformities are reflected in the large-scale organization of the early visual system and what benefits such adaptations would confer. By deploying an efficient coding argument, we predict that changes in the structure of receptive fields across visual space increase the efficiency of sensory coding. To test this experimentally, developed a simple, novel imaging system that is indispensable for studies at this scale. In agreement with our predictions, we could show that receptive fields of retinal ganglion cells change their shape along the dorsoventral axis, with a marked surround asymmetry at the visual horizon. Our work demonstrates that, according to principles of efficient coding, the panoramic structure of natural scenes is exploited by the retina across space and cell-types.

Information processing is energetically expensive. In the mammalian brain, it is unclear how information coding and energy usage are regulated during food scarcity. I addressed this in the visual cortex of awake mice using whole-cell recordings and two-photon imaging to monitor layer 2/3 neuronal activity and ATP usage. I found that food restriction reduced synaptic ATP usage by 29% through a decrease in AMPA receptor conductance. Neuronal excitability was nonetheless preserved by a compensatory increase in input resistance and a depolarized resting membrane potential. Consequently, neurons spiked at similar rates as controls, but spent less ATP on underlying excitatory currents. This energy-saving strategy had a cost since it amplified the variability of visually-evoked subthreshold responses, leading to a 32% broadening in orientation tuning and impaired fine visual discrimination. This reduction in coding precision was associated with reduced levels of the fat mass-regulated hormone leptin and was restored by exogenous leptin supplementation. These findings reveal novel mechanisms that dynamically regulate energy usage and coding precision in neocortex.