NeuroLeman Network

Perception can be strongly serially-dependent (i.e. biased toward previously seen stimuli). Recently, serial dependencies in perception were proposed as a mechanism for perceptual stability, increasing the apparent continuity of the complex environments we experience in everyday life. For example, stable scene perception can be actively achieved by the visual system through global serial dependencies, a special kind of serial dependence between summary statistical representations. Serial dependence occurs also between emotional expressions, but it is highly selective for the same identity. Overall, these results further support the notion of serial dependence as a global, highly specialized, and purposeful mechanism. However, serial dependence could also be a deleterious phenomenon in unnatural or unpredictable situations, such as visual search in radiological scans, biasing current judgments toward previous ones even when accurate and unbiased perception is needed. For example, observers make consistent perceptual errors when classifying a tumor- like shape on the current trial, seeing it as more similar to the shape presented on the previous trial. In a separate localization test, observers make consistent errors when reporting the perceived position of an objects on the current trial, mislocalizing it toward the position in the preceding trial. Taken together, these results show two opposite sides of serial dependence; it can be a beneficial mechanism which promotes perceptual stability, but at the same time a deleterious mechanism which impairs our percept when fine recognition is needed.

Arguably one of the biggest mysteries in neuroscience is how the brain stores long-term memories. The major challenge for investigating the neural circuit underlying memory formation in the neocortex is the distributed nature of the resulting memory trace throughout the cortex. Here, we used a new behavioral paradigm that enabled us to generate memory traces in a specific cortical location and to specifically examine the mechanisms of memory formation in that region. We found that medial-temporal inputs arrive in neocortical layer 1 where the apical dendrites of cortical pyramidal neurons predominate. These dendrites have active properties that make them sensitive to contextual inputs from other areas that also send axons to layer 1 around the cortex. Blocking the influence of these medial-temporal inputs prevented learning and suppressed resulting dendritic activity. We conclude that layer 1 is the locus for hippocampal-dependent memory formation in the neocortex and propose that this process enhances the sensitivity of the tuft dendrites to contextual inputs.

For social animals, successfully communicating with others is essential for interactions and survival. My research aims to answer a central question on the neuronal basis of this ability, from the perspective of the listener: how do our brains enable us to communicate with each other? My work develops nonhuman animal models to study the behavioural and neuronal mechanisms underlying the perception of vocal patterns. I will start by providing an overview of my past research characterizing the neuronal-level substrates of voice processing along the primate temporal lobe. I will then focus on my current work on vocal perception in mice, in which I utilize natural male-female courtship behaviour to evaluate the acoustic dimensions extracted by listeners from ultrasonic sequences. I will then talk about ongoing work investigating the neuronal substrates supporting the perception of behaviourally relevant acoustic cues from mouse vocal sequences.

In this talk I will present recent developments in data sharing, organization, and analyses that allow to build fully reproducible workflows. First, I will present the Brain Imaging Data structure and discuss how this allows to build workflows, showing some new tools to read/import/create studies from EEG data structured that way. Second, I will present several newly developed tools for reproducible pre-processing and statistical analyses. Although it does take some extra effort, I will argue that it largely feasible to make most EEG data analysis fully reproducible.

Human neuroimaging research has consistently shown that drug addiction is associated with structural and functional changes within the orbitofrontal cortex (OFC). In view of the important role of the OFC in value-based decision-making, these changes have been hypothesised to bias choice towards drug use despite and at the expense of other competing pursuits, thereby explaining drug addiction. Here I will present in vivo recording data in the OFC supporting this hypothesis in a choice-based model of addiction where rats could choose between two actions, one rewarded by a drug (cocaine or heroin), the other by a nondrug alternative (saccharin).

Current computational models of visual salience accurately predict the distribution of fixations on isolated visual stimuli. It is not known, however, whether the global salience of a stimulus, that is its effectiveness in the competition for attention with other stimuli, is a function of the local salience or an independent measure. Further, do task and familiarity with the competing images influence eye movements? In this talk, I will present the analysis of a computational model of the global salience of natural images. We trained a machine learning algorithm to learn the direction of the first saccade of participants who freely observed pairs of images. The pairs balanced the combinations of new and already seen images, as well as task and task-free trials. The coefficients of the model provided a reliable measure of the likelihood of each image to attract the first fixation when seen next to another image, that is their global salience. For example, images of close-up faces and images containing humans were consistently looked first and were assigned higher global salience. Interestingly, we found that global salience cannot be explained by the feature-driven local salience of images, the influence of task and familiarity was rather small and we reproduced the previously reported left-sided bias. This computational model of global salience allows to analyse multiple other aspects of human visual perception of competing stimuli. In the talk, I will also present our latest results from analysing the saccadic reaction time as a function of the global salience of the pair of images.

Tara Spires-Jones’ research focuses on the mechanisms and reversibility of neurodegeneration in Alzheimer’s disease, other degenerative brain diseases, and ageing. The objective of her research group is to understand why synapses and neurons become dysfunctional and die in these diseases in order to develop effective therapeutic strategies. Her work has shown that soluble forms of the pathological proteins amyloid beta and tau contribute to synapse degeneration, and that lowering levels of these proteins can prevent and reverse phenotypes in model systems. Further, she has pioneered high-resolution imaging techniques in human post-mortem brain and found evidence that these proteins accumulate in synapses in human disease.

Much of our daily behavior is driven by rewards. The ability to learn to pursue rewarding experiences is, in fact, an essential metric of mental health. Conversely, reduced capacity to engage in adaptive goal-oriented behavior is the hallmark of apathy, and present in the psychotic disorder. The search for its underlying mechanisms has resulted in findings of profound impairments in learning from rewards and the associated blunted activation in key reward areas of the brain of patients with psychosis. An emerging research field has been relying on digital phenotyping tools and ecological momentary assessments (EMA) that map patients’ current mood, behavior and context in the flow of their daily lives. Using these tools, we have started to see a different picture of apathy, one that is exquisitely driven by the environment. For one, reward sensitivity appears to be blunted by stressors, and exposure to undue chronic stress in the daily life may result in apathy in those predisposed to psychosis. Secondly, even patients with psychosis who exhibit clinically elevated levels of apathy are perfectly capable of seeking out and enjoying social interactions in their daily life, if their environment allows them to do so. The use of digital phenotyping tools in combination with neuroimaging of apathy not only allows us to add meanings to the neurobiological findings, but could also help design rational interventions.

Sleep has been classically described as an all-or-nothing global phenomenon. However, a growing body of evidence indicates that typical sleep hallmarks, such as slow waves and spindles, occur and are regulated locally. I will present here evidence indicating that slow waves, in particular, may be related with, and offer a read-out of, local and long-range brain connectivity. In fact, slow waves do not only track changes related to both experience-dependent plasticity and brain maturation during development, but also appear to be actively involved in the fine regulation of brain plasticity and in the removal of metabolic wastes. I will also show that, consistent with a local regulation of sleep, slow waves can often occur locally during wakefulness, with an incidence that varies as a function of time spent awake and of previous rest. These waking slow waves are associated with impaired performance during cognitive tasks and may contribute to explain attention lapses and errors commonly associated with insufficient sleep.

Classically, vision is seen as a cascade of local, feedforward computations. This framework has been tremendously successful, inspiring a wide range of ground-breaking findings in neuroscience and computer vision. Recently, feedforward Convolutional Neural Networks (ffCNNs), inspired by this classic framework, have revolutionized computer vision and been adopted as tools in neuroscience. However, despite these successes, there is much more to vision. I will present our work using visual crowding and related psychophysical effects as probes into visual processes that go beyond the classic framework. In crowding, perception of a target deteriorates in clutter. We focus on global aspects of crowding, in which perception of a small target is strongly modulated by the global configuration of elements across the visual field. We show that models based on the classic framework, including ffCNNs, cannot explain these effects for principled reasons and identify recurrent grouping and segmentation as a key missing ingredient. Then, we show that capsule networks, a recent kind of deep learning architecture combining the power of ffCNNs with recurrent grouping and segmentation, naturally explain these effects. We provide psychophysical evidence that humans indeed use a similar recurrent grouping and segmentation strategy in global crowding effects. In crowding, visual elements interfere across space. To study how elements interfere over time, we use the Sequential Metacontrast psychophysical paradigm, in which perception of visual elements depends on elements presented hundreds of milliseconds later. We psychophysically characterize the temporal structure of this interference and propose a simple computational model. Our results support the idea that perception is a discrete process. Together, the results presented here provide stepping-stones towards a fuller understanding of the visual system by suggesting architectural changes needed for more human-like neural computations.

Emotion processing is thought to be impaired in autism and linked to atypical visual exploration and arousal modulation to others faces and gaze, yet evidence is equivocal. We propose that, where observed, atypical socioemotional processing is due to alexithymia, a distinct but frequently co-occurring condition which affects emotional self-awareness and Interoception. In study 1 (N = 80), we tested this hypothesis by studying the spatio-temporal dynamics and entropy of eye-gaze during emotion processing tasks. Evidence from traditional and novel methods revealed that atypical eye-gaze and emotion recognition is best predicted by alexithymia in both autistic and non-autistic individuals. In Study 2 (N = 70), we assessed interoceptive and autonomic signals implicated in socioemotional processing, and found evidence for alexithymia (not autism) driven effects on gaze and arousal modulation to emotions. We also conducted two large-scale studies (N = 1300), using confirmatory factor-analytic and network modelling and found evidence that Alexithymia and Autism are distinct at both a latent level and their intercorrelations. We argue that: 1) models of socioemotional processing in autism should conceptualise difficulties as intrinsic to alexithymia, and 2) assessment of alexithymia is crucial for diagnosis and personalised interventions in autism.

Sex differences in brain structure and function are of substantial scientific interest because of sex-related susceptibility to psychiatric and neurological disorders. Neuroinflammation is a common denominator of many of these diseases and thus microglia as the brain´s immunocompetent and instrumental cells has come into focus in sex specific studies. We and others show that male microglia are more frequent in specific brain areas and appear to have a higher potential to respond to stimuli, whereas female microglia seem to acquire a more “protective” phenotype.

Parkinson’s disease is affects millions of people around the world. The disease is characterized by typical movement defects that are caused by the loss of dopaminergic neurons, but several very debilitating non-motor symptoms occur more than 10 years before the motor symptoms. I will discuss how we study these non-motor symptoms including sleep disturbances and olfactory defects using large collections of knock in fruit flies that model the numerous familial forms of Parkinson’s disease as well as using human iPS cells from patients. A common emerging theme are defects in protein homeostasis that in specific neuronal cell types, cause cellular defects that explain the Parkinson-relevant phenotypes. Our work reveals the mechanisms that cause early defects in Parkinson’s disease and it opens therapeutic avenues to start tackling this disease.