Electroencephalography (EEG) has been thoroughly studied for decades in psychiatry research. Yet its integration into clinical practice as a diagnostic/prognostic tool remains unachieved. We hypothesize that a key reason is the underlying patient's heterogeneity, overlooked in psychiatric EEG research relying on a case-control approach. We combine HD-EEG with normative modeling to quantify this heterogeneity using two well-established and extensively investigated EEG characteristics -spectral power and functional connectivity- across a cohort of 1674 patients with attention-deficit/hyperactivity disorder, autism spectrum disorder, learning disorder, or anxiety, and 560 matched controls. Normative models showed that deviations from population norms among patients were highly heterogeneous and frequency-dependent. Deviation spatial overlap across patients did not exceed 40% and 24% for spectral and connectivity, respectively. Considering individual deviations in patients has significantly enhanced comparative analysis, and the identification of patient-specific markers has demonstrated a correlation with clinical assessments, representing a crucial step towards attaining precision psychiatry through EEG.

When experts are immersed in a task, a natural assumption is that their brains prioritize task-related activity. Accordingly, most efforts to understand neural activity during well-learned tasks focus on cognitive computations and task-related movements. Surprisingly, we observed that during decision-making, the cortex-wide activity of multiple cell types is dominated by movements, especially “uninstructed movements”, that are spontaneously expressed. These observations argue that animals execute expert decisions while performing richly varied, uninstructed movements that profoundly shape neural activity. To understand the relationship between these movements and decision-making, we examined the movements more closely. We tested whether the magnitude or the timing of the movements was correlated with decision-making performance. To do this, we partitioned movements into two groups: task-aligned movements that were well predicted by task events (such as the onset of the sensory stimulus or choice) and task independent movement (TIM) that occurred independently of task events. TIM had a reliable, inverse correlation with performance in head-restrained mice and freely moving rats. This hinted that the timing of spontaneous movements could indicate periods of disengagement. To confirm this, we compared TIM to the latent behavioral states recovered by a hidden Markov model with Bernoulli generalized linear model observations (GLM-HMM) and found these, again, to be inversely correlated. Finally, we examined the impact of these behavioral states on neural activity. Surprisingly, we found that the same movement impacts neural activity more strongly when animals are disengaged. An intriguing possibility is that these larger movement signals disrupt cognitive computations, leading to poor decision-making performance. Taken together, these observations argue that movements and cognitionare closely intertwined, even during expert decision-making.

The use of molecular imaging, particularly PET and SPECT, has significantly transformed the treatment of schizophrenia with antipsychotic drugs since the late 1980s. It has offered insights into the links between drug target engagement, clinical effects, and side effects. A therapeutic window for receptor occupancy is established for antipsychotics, yet there is a divergence of opinions regarding the importance of blood levels, with many downplaying their significance. As a result, the role of therapeutic drug monitoring (TDM) as a personalized therapy tool is often underrated. Since molecular imaging of antipsychotics has focused almost entirely on D2-like dopamine receptors and their potential to control positive symptoms, negative symptoms and cognitive deficits are hardly or not at all investigated. Alternative methods have been introduced, i.e. to investigate the correlation between approximated receptor occupancies from blood levels and cognitive measures. Within the domain of antidepressants, and specifically regarding ketamine's efficacy in depression treatment, there is limited comprehension of the association between plasma concentrations and target engagement. The measurement of AMPA receptors in the human brain has added a new level of comprehension regarding ketamine's antidepressant effects. To ensure precise prescription of psychotropic drugs, it is vital to have a nuanced understanding of how molecular and clinical effects interact. Clinician scientists are assigned with the task of integrating these indispensable pharmacological insights into practice, thereby ensuring a rational and effective approach to the treatment of mental health disorders, signaling a new era of personalized drug therapy mechanisms that promote neuronal plasticity not only under pathological conditions, but also in the healthy aging brain.

The hippocampus is a key player in learning and memory. Research into this brain structure has long emphasized its plasticity and flexibility, though recent reports have come to appreciate its remarkably stable firing patterns. How novel information incorporates itself into networks that maintain their ongoing dynamics remains an open question, largely due to a lack of experimental access points into network stability. Development may provide one such access point. To explore this hypothesis, we birthdated CA1 pyramidal neurons using in-utero electroporation and examined their functional features in freely moving, adult mice. We show that CA1 pyramidal neurons of the same embryonic birthdate exhibit prominent cofiring across different brain states, including behavior in the form of overlapping place fields. Spatial representations remapped across different environments in a manner that preserves the biased correlation patterns between same birthdate neurons. These features of CA1 activity could partially be explained by structured connectivity between pyramidal cells and local interneurons. These observations suggest the existence of developmentally installed circuit motifs that impose powerful constraints on the statistics of hippocampal output.

Modern electrophysiological recordings simultaneously capture single-unit spiking activities of hundreds of neurons. These neurons exhibit highly complex coordination patterns. Where does this complexity stem from? One candidate is the ubiquitous heterogeneity in connectivity of local neural circuits. Studying neural network dynamics in the linearized regime and using tools from statistical field theory of disordered systems, we derive relations between structure and dynamics that are readily applicable to subsampled recordings of neural circuits: Measuring the statistics of pairwise covariances allows us to infer statistical properties of the underlying connectivity. Applying our results to spontaneous activity of macaque motor cortex, we find that the underlying network operates in a strongly recurrent regime. In this regime, network connectivity is highly heterogeneous, as quantified by a large radius of bulk connectivity eigenvalues. Being close to the point of linear instability, this dynamical regime predicts a rich correlation structure, a large dynamical repertoire, long-range interaction patterns, relatively low dimensionality and a sensitive control of neuronal coordination. These predictions are verified in analyses of spontaneous activity of macaque motor cortex and mouse visual cortex. Finally, we show that even microscopic features of connectivity, such as connection motifs, systematically scale up to determine the global organization of activity in neural circuits.

Neurons in the primary visual cortex (V1) of rodents are selective to the orientation of the stimulus, as in other mammals such as cats and monkeys. However, in contrast with those species, their neurons display a very different type of spatial organization. Instead of orientation maps they are organized in a “salt and pepper” pattern, where adjacent neurons have completely different preferred orientations. This structure has motivated both experimental and theoretical research with the objective of determining which aspects of the connectivity patterns and intrinsic neuronal responses can explain the observed behavior. These analysis have to take into account also that the neurons of the thalamus that send their outputs to the cortex have more complex responses in rodents than in higher mammals, displaying, for instance, a significant degree of orientation selectivity. In this talk we present work showing that a random feed-forward connectivity pattern, in which the probability of having a connection between a cortical neuron and a thalamic neuron depends only on the relative distance between them is enough explain several aspects of the complex phenomenology found in these systems. Moreover, this approach allows us to evaluate analytically the statistical structure of the thalamic input on the cortex. We find that V1 neurons are orientation selective but the preferred orientation of the stimulus depends on the spatial frequency of the stimulus. We disentangle the effect of the non circular thalamic receptive fields, finding that they control the selectivity of the time-averaged thalamic input, but not the selectivity of the time locked component. We also compare with experiments that use reverse correlation techniques, showing that ON and OFF components of the aggregate thalamic input are spatially segregated in the cortex.

Memory impairment is a common cognitive deficit in temporal lobe epilepsy (TLE). The hippocampus is severely altered in TLE exhibiting multiple anatomical changes that lead to a hyperexcitable network capable of generating frequent epileptic discharges and seizures. In this study we investigated whether hippocampal involvement in epileptic activity drives working memory deficits using bilateral LFP recordings from CA1 during task performance. We discovered that epileptic mice experienced focal rhythmic discharges (FRDs) while they performed the spatial working memory task. Spatial correlation analysis revealed that FRDs were often spatially stable on the maze and were most common around reward zones (25 ‰) and delay zones (50 ‰). Memory performance was correlated with stability of FRDs, suggesting that spatially unstable FRDs interfere with working memory codes in real time.

In this talk, we propose to decipher the activity of neural networks via a “multiply and conquer” approach. This approach considers limit networks made of infinitely many replicas with the same basic neural structure. The key point is that these so-called replica-mean-field networks are in fact simplified, tractable versions of neural networks that retain important features of the finite network structure of interest. The finite size of neuronal populations and synaptic interactions is a core determinant of neural dynamics, being responsible for non-zero correlation in the spiking activity and for finite transition rates between metastable neural states. Theoretically, we develop our replica framework by expanding on ideas from the theory of communication networks rather than from statistical physics to establish Poissonian mean-field limits for spiking networks. Computationally, we leverage our original replica approach to characterize the stationary spiking activity of various network models via reduction to tractable functional equations. We conclude by discussing perspectives about how to use our replica framework to probe nontrivial regimes of spiking correlations and transition rates between metastable neural states.

A major challenge for causal inference from time-series data is the trade-off between computational feasibility and accuracy. Motivated by process motifs for lagged covariance in an autoregressive model with slow mean-reversion, we propose to infer networks of causal relations via pairwise edge measure (PEMs) that one can easily compute from lagged correlation matrices. Motivated by contributions of process motifs to covariance and lagged variance, we formulate two PEMs that correct for confounding factors and for reverse causation. To demonstrate the performance of our PEMs, we consider network interference from simulations of linear stochastic processes, and we show that our proposed PEMs can infer networks accurately and efficiently. Specifically, for slightly autocorrelated time-series data, our approach achieves accuracies higher than or similar to Granger causality, transfer entropy, and convergent crossmapping -- but with much shorter computation time than possible with any of these methods. Our fast and accurate PEMs are easy-to-implement methods for network inference with a clear theoretical underpinning. They provide promising alternatives to current paradigms for the inference of linear models from time-series data, including Granger causality, vector-autoregression, and sparse inverse covariance estimation.

Resting-state functional magnetic resonance imaging (MRI) has yielded seemingly disparate insights into large-scale organization of the human brain. The brain’s large-scale organization can be divided into two broad categories: zero-lag representations of functional connectivity structure and time-lag representations of traveling wave or propagation structure. In this study, we sought to unify observed phenomena across these two categories in the form of three low-frequency spatiotemporal patterns composed of a mixture of standing and traveling wave dynamics. We showed that a range of empirical phenomena, including functional connectivity gradients, the task-positive/task-negative anti-correlation pattern, the global signal, time-lag propagation patterns, the quasiperiodic pattern and the functional connectome network structure, are manifestations of these three spatiotemporal patterns. These patterns account for much of the global spatial structure that underlies functional connectivity analyses and unifies phenomena in resting-state functional MRI previously thought distinct.

Three intimately related dimensions of the mammalian genome—linear DNA sequence, gene transcription, and 3D genome architecture—are crucial for the development of nervous systems. Changes in the linear genome (e.g., de novo mutations), transcriptome, and 3D genome structure lead to debilitating neurodevelopmental disorders, such as autism and schizophrenia. However, current technologies and data are severely limited: (1) 3D genome structures of single brain cells have not been solved; (2) little is known about the dynamics of single-cell transcriptome and 3D genome after birth; (3) true de novo mutations are extremely difficult to distinguish from false positives (DNA damage and/or amplification errors). Here, I filled in this longstanding technological and knowledge gap. I recently developed a high-resolution method—diploid chromatin conformation capture (Dip-C)—which resolved the first 3D structure of the human genome, tackling a longstanding problem dating back to the 1880s. Using Dip-C, I obtained the first 3D genome structure of a single brain cell, and created the first transcriptome and 3D genome atlas of the mouse brain during postnatal development. I found that in adults, 3D genome “structure types” delineate all major cell types, with high correlation between chromatin A/B compartments and gene expression. During development, both transcriptome and 3D genome are extensively transformed in the first month of life. In neurons, 3D genome is rewired across scales, correlated with gene expression modules, and independent of sensory experience. Finally, I examined allele-specific structure of imprinted genes, revealing local and chromosome-wide differences. More recently, I expanded my 3D genome atlas to the human and mouse cerebellum—the most consistently affected brain region in autism. I uncovered unique 3D genome rewiring throughout life, providing a structural basis for the cerebellum’s unique mode of development and aging. In addition, to accurately measure de novo mutations in a single cell, I developed a new method—multiplex end-tagging amplification of complementary strands (META-CS), which eliminates nearly all false positives by virtue of DNA complementarity. Using META-CS, I determined the true mutation spectrum of single human brain cells, free from chemical artifacts. Together, my findings uncovered an unknown dimension of neurodevelopment, and open up opportunities for new treatments for autism and other developmental disorders.

Recent chronic imaging experiments in mice have revealed that the hippocampal code exhibits non-trivial turnover dynamics over long time scales. Specifically, the subset of cells which are active on any given session in a familiar environment changes over the course of days and weeks. While some cells transition into or out of the code after a few sessions, others are stable over the entire experiment. The mechanisms underlying this turnover are unknown. Here we show that the statistics of turnover are consistent with a model in which non-spatial inputs to CA1 pyramidal cells readily undergo plasticity, while spatially tuned inputs are largely stable over time. The heterogeneity in stability across the cell assembly, as well as the decrease in correlation of the population vector of activity over time, are both quantitatively fit by a simple model with Gaussian input statistics. In fact, such input statistics emerge naturally in a network of spiking neurons operating in the fluctuation-driven regime. This correspondence allows one to map the parameters of a large-scale spiking network model of CA1 onto the simple statistical model, and thereby fit the experimental data quantitatively. Importantly, we show that the observed drift is entirely consistent with random, ongoing synaptic turnover. This synaptic turnover is, in turn, consistent with Hebbian plasticity related to continuous learning in a fast memory system.

Hippocampal pyramidal cells have been widely studied during locomotion, when theta oscillations are present, and during short wave ripples at rest, when replay takes place. However, we find a subset of pyramidal cells that are preferably active during rest, in the absence of theta oscillations and short wave ripples. We recorded these cells using two-photon imaging in dorsal CA1 of the hippocampus of mice, during a virtual reality object location recognition task. During locomotion, the cells show a similar level of activity as control cells, but their activity increases during rest, when this population of cells shows highly synchronous, oscillatory activity at a low frequency (0.1-0.4 Hz). In addition, during both locomotion and rest these cells show place coding, suggesting they may play a role in maintaining a representation of the current location, even when the animal is not moving. We performed simultaneous electrophysiological and calcium recordings, which showed a higher correlation of activity between the LFO and the hippocampal cells in the 0.1-0.4 Hz low frequency band during rest than during locomotion. However, the relationship between the LFO and calcium signals varied between electrodes, suggesting a localized effect. We used the Allen Brain Observatory Neuropixels Visual Coding dataset to further explore this. These data revealed localised low frequency oscillations in CA1 and DG during rest. Overall, we show a novel population of hippocampal cells, and a novel oscillatory band of activity in hippocampus during rest.

To better understand human scene understanding, we extracted features from images using CLIP, a neural network model of visual concept trained with supervision from natural language. We then constructed voxelwise encoding models to explain whole brain responses arising from viewing natural images from the Natural Scenes Dataset (NSD) - a large-scale fMRI dataset collected at 7T. Our results reveal that CLIP, as compared to convolution based image classification models such as ResNet or AlexNet, as well as language models such as BERT, gives rise to representations that enable better prediction performance - up to a 0.86 correlation with test data and an r-square of 0.75 - in higher-level visual cortex in humans. Moreover, CLIP representations explain distinctly unique variance in these higher-level visual areas as compared to models trained with only images or text. Control experiments show that the improvement in prediction observed with CLIP is not due to architectural differences (transformer vs. convolution) or to the encoding of image captions per se (vs. single object labels). Together our results indicate that CLIP and, more generally, multimodal models trained jointly on images and text, may serve as better candidate models of representation in human higher-level visual cortex. The bridge between language and vision provided by jointly trained models such as CLIP also opens up new and more semantically-rich ways of interpreting the visual brain.

Establishment of precise neuronal connectivity in the neocortex relies on activity-dependent circuit reorganization during postnatal development. In the mouse somatosensory cortex layer 4, barrels are arranged in one-to-one correspondence to whiskers on the face. Thalamocortical axon termini are clustered in the center of each barrel. The layer 4 spiny stellate neurons are located around the barrel edge, extend their dendrites primarily toward the barrel center, and make synapses with thalamocortical axons corresponding to a single whisker. These organized circuits are established during the first postnatal week through activity-dependent refinement processes. However, activity pattern regulating the circuit formation is still elusive. Using two-photon calcium imaging in living neonatal mice, we found that layer 4 neurons within the same barrel fire synchronously in the absence of peripheral stimulation, creating a ''patchwork'' pattern of spontaneous activity corresponding to the barrel map. We also found that disruption of GluN1, an obligatory subunit of the N-methyl-D-aspartate (NMDA) receptor, in a sparse population of layer 4 neurons reduced activity correlation between GluN1 knockout neuron pairs within a barrel. Our results provide evidence for the involvement of layer 4 neuron NMDA receptors in spatial organization of the spontaneous firing activity of layer 4 neurons in the neonatal barrel cortex. In the talk I will introduce our strategy to analyze the role of NMDA receptor-dependent correlated activity in the layer 4 circuit formation.

Natural soundscapes such as from a forest, a busy restaurant, or a busy intersection are generally composed of a cacophony of sounds that the brain needs to interpret either independently or collectively. In certain instances sounds - such as from moving cars, sirens, and people talking - are perceived in unison and are recognized collectively as single sound (e.g., city noise). In other instances, such as for the cocktail party problem, multiple sounds compete for attention so that the surrounding background noise (e.g., speech babble) interferes with the perception of a single sound source (e.g., a single talker). I will describe results from my lab on the perception and neural representation of auditory textures. Textures, such as a from a babbling brook, restaurant noise, or speech babble are stationary sounds consisting of multiple independent sound sources that can be quantitatively defined by summary statistics of an auditory model (McDermott & Simoncelli 2011). How and where in the auditory system are summary statistics represented and the neural codes that potentially contribute towards their perception, however, are largely unknown. Using high-density multi-channel recordings from the auditory midbrain of unanesthetized rabbits and complementary perceptual studies on human listeners, I will first describe neural and perceptual strategies for encoding and perceiving auditory textures. I will demonstrate how distinct statistics of sounds, including the sound spectrum and high-order statistics related to the temporal and spectral correlation structure of sounds, contribute to texture perception and are reflected in neural activity. Using decoding methods I will then demonstrate how various low and high-order neural response statistics can differentially contribute towards a variety of auditory tasks including texture recognition, discrimination, and categorization. Finally, I will show examples from our recent studies on how high-order sound statistics and accompanying neural activity underlie difficulties for recognizing speech in background noise.

The hippocampal formation has been implicated in both cognitive functions as well as the sensing and control of endocrine states. To identify a candidate activity pattern which may link such disparate functions, we simultaneously measured electrophysiological activity from the hippocampus and interstitial glucose concentrations in the body of freely behaving rats. We found that clusters of sharp wave-ripples (SPW-Rs) recorded from both dorsal and ventral hippocampus reliably predicted a decrease in peripheral glucose concentrations within ~10 minutes. This correlation was less dependent on circadian, ultradian, and meal-triggered fluctuations, it could be mimicked with optogenetically induced ripples, and was attenuated by pharmacogenetically suppressing activity of the lateral septum, the major conduit between the hippocampus and subcortical structures. Our findings demonstrate that a novel function of the SPW-R is to modulate peripheral glucose homeostasis and offer a mechanism for the link between sleep disruption and blood glucose dysregulation seen in type 2 diabetes and obesity.

L-DOPA-induced dyskinesia is a debilitating adverse effect of treating Parkinson’s disease with this drug. New therapeutic approaches that prevent or attenuate this side effect is clearly needed. Wistar adult male rats submitted to 6-hydroxydopamine-induced unilateral medial forebrain bundle lesions were treated with L-DOPA (oral or subcutaneous, 20 mg kg-1) once a day for 14 days. After this period, we tested if doxycycline (40 mg kg-1, intraperitoneal, a subantimicrobial dose) and COL-3 (50 and 100 nmol, intracerebroventricular) could reverse LID. In an additional experiment, doxycycline was also administered repeatedly with L-DOPA to verify if it would prevent LID development. A single injection of doxycycline or COL-3 together with L-DOPA attenuated the dyskinesia. Co-treatment with doxycycline from the first day of L-DOPA suppressed the onset of dyskinesia. The improved motor responses to L-DOPA remained intact in the presence of doxycycline or COL-3, indicating the preservation of L-DOPA-produced benefits. Doxycycline treatment was associated with decreased immunoreactivity of FosB, cyclooxygenase-2, the astroglial protein GFAP and the microglial protein OX-42 which are elevated in the basal ganglia of rats exhibiting dyskinesia. Doxycycline also decreased metalloproteinase-2/-9 activity, metalloproteinase-3 expression and reactive oxygen species production. Metalloproteinase-2/-9 activity and production of reactive oxygen species in the basal ganglia of dyskinetic rats showed a significant correlation with the intensity of dyskinesia. The present study demonstrates the anti-dyskinetic potential of doxycycline and its analog compound COL-3 in hemiparkinsonian rats. Given the long-established and safe clinical use of doxycycline, this study suggests that these drugs might be tested to reduce or to prevent L-DOPA-induced dyskinesia in Parkinson’s patients.