Biology subjects

Panzeri, S.

Publications and source records attributed to Panzeri, S..

47 records · Page 3Linked to original sources

bioRxiv2020

Intrinsic excitation-inhibition imbalance affects medial prefrontal cortex differently in autistic men versus women

Excitation-inhibition (E:I) imbalance is theorized as an important pathophysiological mechanism in autism. Autism affects males more frequently than females and sex-related mechanisms (e.g., X-linked genes, androgen hormones) can influence E:I balance. This suggests that E:I imbalance may affect autism differently in males versus females. With a combination of in-silico modeling and in-vivo chemogenetic manipulations in mice, we first show that a time-series metric estimated from fMRI BOLD signal, the Hurst exponent (H), can be an index for underlying change in the synaptic E:I ratio. In autism we find that H is reduced, indicating increased excitation, in the medial prefrontal cortex (MPFC) of autistic males but not females. Increasingly intact MPFC H is also associated with heightened ability to behaviorally camouflage social-communicative difficulties, but only in autistic females. This work suggests that H in BOLD can index synaptic E:I ratio and that E:I imbalance affects autistic males and females differently.

neuroscience↗

bioRxiv2019

Stimulus dependent relationships between behavioral choice and sensory neural responses

Understanding the relationship between trial-to-trial variability in neural responses of sensory areas and behavioral choices is fundamental to elucidate the mechanisms of perceptual decision-making. In two-choice tasks, activity-choice co-variations have traditionally been quantified with choice probabilities (CP). It has been so far commonly assumed that choice-related neural signals are separable from stimulus-driven responses, which has led to characterizing activity-choice covariations only with a single CP value estimated combining trials from all stimulus levels. In this work we provide theoretical and experimental evidence for the stimulus dependence of the relationship between neural responses and behavioral choices. We derived a general analytical CP expression for this dependency under the general assumption that a decision threshold converts an internal stimulus estimate into a binary choice. This expression predicts a stereotyped threshold-induced CP modulation by the stimulus information content. We reanalyzed data from Britten et al. (1996) and found evidence of this modulation in the responses of macaque MT cells during a random dot discrimination task. Moreover, we developed new methods of analysis that allowed us to further identify a richer structure of cell-specific CP stimulus dependencies. Finally, we capitalised on this progress to develop new generalized linear models (GLMs) with stimulus-choice interaction terms, which show a higher predictive power and lead to a more precise assessment of how much each neuron is stimulus- or choice-driven, hence allowing a more accurate comparison across areas or cell types. Our work suggests that characterizing the patterns of stimulus dependence of choice-related signals is essential to properly determine how neurons in different areas contribute to linking sensory representations to perceptual decisions.

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bioRxiv2019

Transient disruption of the inferior parietal lobule impairs action mindreading

Although it is well established that fronto-parietal regions are active during action observation, whether they play a causal role in the ability to "mindread" others actions remains controversial. In experiments reported here, we combined offline continuous theta-burst stimulation (cTBS) with computational modeling to reveal single-trial computations in the inferior parietal lobule (IPL) and inferior frontal gyrus (IFG). Participants received cTBS over the left IPL and IFG, in separate sessions, before completing an intention discrimination task or a kinematic discrimination task unrelated to intention. We found that transient disruption of activity of the IPL, but not the IFG, specifically impaired the observers ability to judge intention from movement kinematics. Kinematic discrimination unrelated to intention, in contrast, was largely unaffected. Computational analyses revealed that IPL cTBS did not impair the ability to see changes in movement kinematics, nor did it alter the weight given to informative versus non-informative kinematic features. Rather, it selectively impaired the ability to link variations in informative features to the correct intention. These results provide the first causal evidence that IPL maps kinematics to intentions.

neuroscience↗

bioRxiv2019

Manipulating synthetic optogenetic odors reveals the coding logic of olfactory perception

How does neural activity generate perception? The spatial identities and temporal latencies of activated units correlate with external sensory features, but finding the principal activity subspace that is consequential for perception, remains challenging. We trained mice to recognize synthetic odors constructed from parametrically-defined patterns of optogenetic activation, then measured perceptual changes during extensive and controlled perturbations across spatio-temporal dimensions. We modelled recognition as the matching of patterns to learned templates, finding that perceptually-meaningful templates are sequences of spatially-identified units, ordered by latencies relative to each other (with minimal effects of sniff). Within templates, individual units contribute additively, with larger contributions from earlier-activated units. Our synthetic approach reveals the fundamental logic of the olfactory code, and provides a general framework for testing links between sensory activity and perception.

neuroscience↗

bioRxiv2019

A Non-negative Measure Of Feature-specific Information Transfer Between Neural Signals

Quantifying both the amount and content of the information transferred between neuronal populations is crucial to understand brain functions. Traditional data-driven methods based on Wiener-Granger causality quantify information transferred between neuronal signals, but do not reveal whether transmission of information refers to one specific feature of external stimuli or another. Here, we developed a new measure called Feature-specific Information Transfer (FIT), that quantifies the amount of information transferred between neuronal signals about specific stimulus features. The FIT quantifies the feature-related information carried by a receiver that was previously carried by a sender, but that was never carried by the receiver earlier. We tested the FIT on simulated data in various scenarios. We found that, unlike previous measures, FIT successfully disambiguated genuine feature-specific communication from non-feature specific communication, from external confounding inputs and synergistic interactions. Moreover, the FIT had enhanced temporal sensitivity that facilitates the estimation of the directionality of transfer and the communication delay between neuronal signals. We validated the FITs ability to track feature-specific information flow using neurophysiological data. In human electroencephalographic data acquired during a face detection task, the FIT demonstrated that information about the eye in face pictures flowed from the hemisphere contralateral to the eye to the ipsilateral one. In multi-unit activity recorded from thalamic nuclei and primary sensory cortices of rats during multimodal stimulation, FIT, unlike Wiener-Granger methods, credibly detected both the direction of information flow and the sensory features about which information was transmitted. In human cortical high-gamma activity recorded with magnetoencephalography during visuomotor mapping, FIT showed that visuomotor-related information flowed from superior parietal to premotor areas. Our work suggests that the FIT measure has the potential to uncover previously hidden feature-specific information transfer in neuronal recordings and to provide a better understanding of brain communication. Author summaryThe emergence of coherent percepts and behavior relies on the processing and flow of information about sensory features, such as the color or shape of an object, across different areas of the brain. To understand how computations within the brain lead to the emergence of these functions, we need to map the flow of information about each specific feature. Traditional methods, such as those based on Wiener-Granger causality, quantify whether information is transmitted from one brain area to another, but do not reveal if the information being transmitted is about a certain feature or another feature. Here, we develop a new mathematical technique for the analysis of brain activity recordings, called Feature-specific Information Transfer (FIT), that can reveal not only if any information is being transmitted across areas, but whether or not such transmitted information is about a certain sensory feature. We validate the method with both simulated and real neuronal data, showing its power in detecting the presence of feature-specific information transmission, as well as the timing and directionality of this transfer. This work provides a tool of high potential significance to map sensory information processing in the brain.

neuroscience↗

bioRxiv2018

Categorical encoding of decision variables in orbitofrontal cortex

A fundamental and recurrent question in systems neuroscience is that of assessing what variables are encoded by a given population of neurons. Such assessments are often challenging because neurons in one brain area may encode multiple variables, and because neuronal representations might be categorical (different neurons encoding different variables) or mixed (individual neurons encoding a combination of variables). These issues are particularly pertinent to the representation of decision variables in the orbitofrontal cortex (OFC) - an area implicated in economic choices. Here we present a new algorithm to assess whether a neuronal representation is categorical or mixed, and to identify the encoded variables if the representation is indeed categorical. The algorithm is based on two clustering procedures, one variable-independent and the other variable-based. The two partitions are then compared through adjusted mutual information. The present algorithm overcomes limitations of previous approaches and is widely applicable. We tested the algorithm on synthetic data and then used it to examine neuronal data recorded in the primate OFC during economic decisions. Confirming previous assessments, we found the neuronal representation in OFC to be categorical in nature. We also found that neurons in this area encode the value of individual offers, the binary choice outcome and the chosen value. In other words, during economic choice, neurons in the primate OFC encode decision variables in a categorical way.\n\nAuthor SummaryMental functions such as sensory perception or decision making ultimately rely on the activity of neuronal populations in different brain regions. Much research in neuroscience is devoted to understanding how different groups of neurons support specific brain functions by representing behaviorally relevant variables. In this respect, one important question is whether individual neurons represent single variables, or linear combination of variables. Here we developed a new algorithm to assess this general issue. We then used the algorithm to examine neurons in the orbitofrontal cortex (OFC) recorded while non-human primates performed economic decisions. We found that different neurons represent different variables (categorical encoding). Specifically, neurons in the OFC encoded the value of individual offers, the binary choice outcome, and the chosen value. The present results support the hypothesis that economic decisions are formed within the OFC.

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bioRxiv2018

The Spectrum of Asynchronous Dynamics in Spiking Networks: A Theory for the Diversity of Non-Rhythmic Waking states in Neocortex

The cerebral cortex of awake animals exhibits frequent transitions between diverse non-rhythmic network states. However, it is still unclear how these different activity states emerge within the same network and how each state impacts network function. Here, we demonstrate that model networks of spiking neurons with moderate recurrent interactions dynamically change their asynchronous dynamics depending upon the level of afferent excitation. We found that the model network displayed a spectrum of asynchronous states, ranging from afferent input-dominated (AD) regimes, characterized by unbalanced synaptic currents and sparse firing, to recurrent input-dominated (RD) regimes, characterized by balanced synaptic currents and dense firing. The model predicted regime-specific relationships between several different neural biophysical properties which were all experimentally confirmed by intracellular recordings in the somatosensory cortex of awake mice. Moreover, theoretical analysis showed that AD regimes more precisely encode spatiotemporal patterns of presynaptic activity, while RD regimes better encoded the strength of afferent inputs. These results provide a theoretical foundation for how recurrent neocortical circuits generate non-rhythmic waking states and how these different states modulate the processing of incoming information.

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bioRxiv2018

Oscillatory brain states govern spontaneous fMRI network dynamics

Spontaneous brain activity as assessed with resting-state fMRI exhibits rich spatiotemporal structure. However, the principles by which brain-wide patterns of spontaneous fMRI activity reconfigure and interact with each other, remain unclear. We devised a frame-wise clustering approach to map spatiotemporal dynamics of spontaneous fMRI activity with voxel resolution in the resting mouse brain. We show that brain-wide patterns of fMRI co-activation can be reliably mapped at the group and subject level, defining a restricted set of recurring brain states characterized by rich network structure. We document that these functional states are characterized by contrasting patterns of spontaneous fMRI activity and exhibit coupled oscillatory dynamics, with each state occurring at specific phases of global fMRI signal fluctuations. Finally, we show that autism-associated genetic alterations result in the engagement of non-canonical brain states and altered coupled oscillatory dynamics. Our approach reveals a new set of fundamental principles guiding the spatiotemporal organization of resting state fMRI activity, and its disruption in brain disorders.

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bioRxiv2017

Decision-Related Signals In The Presence Of Nonzero Signal Stimuli, Internal Bias, And Feedback

Understanding the nature of decision-related signals in sensory neurons promises to give insights into their role in perceptual decision-making. Those signals, traditionally quantified as choice probabilities (CP), are well-understood in a feedforward framework assuming zero-signal trials with no choice bias. Here, we extend this understanding by analytically solving models of choice-related signals that account for informative stimuli, choice bias, and importantly, feedback signals reflecting either internal states, such as attention or belief, or the outcome of the decision process. First, we relate CPs to Choice Triggered Averages (CTAs), which quantify choice-related average changes in neural responses, and show that both have general expressions valid for activity-choice covariations of both feedforward or feedback origin. These expressions allow a meaningful calculation of CPs across all trials, including non-zero signal trials. Second, we derive how CPs and CTAs depend on feedforward and feedback weights and on noise correlations under several plausible model architectures. Third, we examine different types of feedback signals, related to predictive coding, probabilistic inference, and attention, and we predict how CPs and CTAs depend in each case on the stimulus signal level and on the neural tuning properties. Finally, we show that measuring both CPs and CTAs offers complementary information about the origin of choice-related signals, especially when studying temporal changes of activity-choice covariations across the trial time. Overall, our work provides new analytical tools to better understand the link between sensory representations and perceptual decisions.

neuroscience↗

bioRxiv2017

Monitoring large populations of locus coeruleus neurons reveals the non-global nature of the norepinephrine neuromodulatory system

Understanding the forebrain neuromodulation by the noradrenergic locus coeruleus (LC) is fundamental for cognitive and systems neuroscience. The diffuse projections of individual LC neurons and presumably their synchronous spiking have long been perceived as features of the global nature of noradrenergic neuromodulation. Yet, the commonly referenced \"synchrony\" underlying global neuromodulation, has never been assessed in a large population, nor has it been related to projection target specificity. Here, we recorded up to 52 single units simultaneously (3164 unit pairs in total) in rat LC and characterized projections by stimulating 15 forebrain sites. Spike count correlations were low and, surprisingly, only 13% of pairwise spike trains had synchronized spontaneous discharge. Notably, even noxious sensory stimulation did not activate the entire population, only evoking synchronized responses in ~16% of units on each trial. We also identified novel infra-slow (0.01-1 Hz) fluctuations of LC unit spiking that were asynchronous across the population. A minority, synchronized possibly by gap junctions, was biased toward restricted (non-global) forebrain projection patterns. Finally, we characterized two types of LC single units differing by waveform shape, propensity for synchronization, and interactions with cortex. These cell types formed finely-structured ensembles. Our findings suggest that the LC conveys a highly complex, differentiated, and potentially target-specific neuromodulatory signal.

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bioRxiv2016

Contributions of local speech encoding and functional connectivity to audio-visual speech integration

Seeing a speakers face enhances speech intelligibility in adverse environments. We investigated the underlying network mechanisms by quantifying local speech representations and directed connectivity in MEG data obtained while human participants listened to speech of varying acoustic SNR and visual context. During high acoustic SNR speech encoding by entrained brain activity was strong in temporal and inferior frontal cortex, while during low SNR strong entrainment emerged in premotor and superior frontal cortex. These changes in local encoding were accompanied by changes in directed connectivity along the ventral stream and the auditory-premotor axis. Importantly, the behavioural benefit arising from seeing the speaker's face was not predicted by changes in local encoding but rather by enhanced functional connectivity between temporal and inferior frontal cortex. Our results demonstrate a role of auditory-motor interactions in visual speech representations and suggest that functional connectivity along the ventral pathway facilitates speech comprehension in multisensory environments.

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