2023 Topic-Specific Grants and Descriptions
Topic-specific grants for 6 positions in 2023's Selection Announcement
1. Synaptic logic of visuo-motor circuits (IIT - 1 position)
To improve their perception of the environment, animals constantly face the problem of discriminating external stimuli from those triggered by their voluntary movements. For example, during eye and head movements images sweep rapidly on our retinas, yet we do not perceive the world blurring away. This perceptual phenomenon indicates that the visual pathway may predict, discern or and cancel out self-generated visual signals resulting from body movements. However, the organization of the synaptic connections, at the circuit and dendritic level, mediating this visuo-motor processing remain poorly understood.
Using the visual pathway as a model system, we will investigate the circuit motifs, neural elements and dendritic mechanisms that compute shape and direction of motion of external stimuli, and disambiguate them from self-generated stimuli, allowing to pursue visual targets. To address these questions, we will develop and deploy methods high-throughput mapping of synaptic connectivity at single neuron level, based on viral tracing, optogenetics and dendritic imaging. We will then combine these methods with two-photon functional imaging to record how visual and motor signals are combined by circuits of connected neurons, both during active visual exploration and visual pursuit tasks.
Applications are invited from candidates with an MSc in neuroscience, physics, engineering, or any STEM discipline. Hands-on training in experimental neuroscience and/or familiarity with programming (e.g. Python, Matlab) will be highly valued. References in the download section.
Supervisor: Federico Rossi. Corresponding research area in the online application: Neuroimaging and brain connectivity.
2. Neural circuit mechanisms for cognitive processes (IIT - 1 position)
All animals protect their body by constantly monitoring their peripersonal space. The parietal and premotor cortices and the basal ganglia play an essential role in this function because their degeneration or damage can result in abnormal defensive behaviors in response to proximal stimuli. Work from several species indicates that neurons in those areas integrate somatosensory information about the body and visual information about the proximal space. However, how this multimodal representation of the peripersonal space influences defensive actions needs to be better understood. We are seeking for a graduate student willing to tackle this challenge. The first goal is to determine how the synaptic architecture of tactile, visual and motor inputs to neurons in the posterior parietal cortex builds a map of the peri-facial space.
The second goal is to determine how neurons in the premotor cortex integrate the multisensory inputs from the parietal cortex to guide the selection of an appropriate action to protect the face. Finally, the third goal is to determine how the caudoputamen learns to control specific defensive actions directed against threats within the peri-facial space. The successful applicant will receive training and utilise the laboratory’s fully equipped state-of-the-art technology to combine behavioural, physiological recordings and brain stimulation, in both controlled and ecological environments.
The techniques available in the lab include: in vivo two-photon imaging, wide-field cortical imaging; miniscope imaging of freely moving mice; fiber photometry; neuropixels acute and chronic recordings; in vivo patch-clamp recordings; optogenetics; viral strategies for circuit tracing; machine-learning assisted behavioral analysis The candidate should have completed their Bachelor’s or Master’s degree in either biological or numerate sciences, such as biology, pharmacology, psychology, neuroscience, computer science, physics, or artificial intelligence.
They should have a propensity for interdisciplinary research. Strong analytical skills, and coding abilities (e.g. MATLAB, Python) are desirable. The ideal candidate should be independent, proactive, suited for collaborative work in a team with senior and junior collaborators, as well as interaction with other groups in international networks.
Supervisor: Giuliano Iurilli. Corresponding research area in the online application: Perception and attention
3. Synaptic modulation of brain connectivity (IIT - 1 "Andrea Baracchino scholarship" position)
Modern neuroimaging methods like fMRI have been widely used to map inter-areal communication in health and disease. However, many fundamental questions regarding the organization and neural mechanisms governing inter-areal coupling in the mammalian brain remain unanswered. For one, how does activity in one region causally affect whole-brain patterns of brain activity? Addressing this question has important implications for both theoretical and translational neuroscience. The goal of this project is to investigate how synaptic coupling affects brain-wide network activity as measured with fMRI.
To address this question, we have developed novel inducible viral vectors enabling remote strengthening or weakening of synaptic coupling in target regions of the mouse brain. By regionally controlling synaptic coupling we will be able to measure, and model, how activity at one brain node can influence network-level mechanism of brain communication (i.e. “functional connectivity”), and predict effect of targeted brain stimulation. The successful candidate will have a MSc in neuroscience, biotechnology, pharmacology, psychology, physics, or any STEM discipline.
Hands-on training in in vivo experimental neuroscience and/or familiarity with science-related coding platforms will be highly valued. For reference to recent work of relevance to this project, please see Rocchi et al. Nature Communications (2022), Pagani et al., Nature Communications (2021).
Supervisor: Alessandro Gozzi. Corresponding research area in the online application: Neuroimaging and brain connectivity.
4. Neuroimaging and electrophysiological biomarkers behind autisms distinguished by disability versus difference over development (IIT - 2 positions)
This project is centered around identifying biomarkers with neuroimaging and electrophysiology (fMRI, EEG) that distinguish different types of autisms defined by ‘disability’ versus ‘difference’ in core and non-core behavioral features. The research uses methods and approaches from a variety of fields such as neuroimaging (e.g., fMRI, MEG, EEG), eye tracking, and data science. Ideal candidates could have any or all of the following backgrounds and/or substantive prior experience:
- collecting data and working with children and/or patient populations relevant to the project (e.g., autism, other neurodevelopmental disorders);
- methodologies relevant to the project (e.g., fMRI, MEG, EEG, eye tracking);
- existing strong computational skills (e.g., machine learning, advanced statistics, R, Python, MATLAB) or strong potential and motivation to develop such skills. For further information, candidates are encouraged to get in contact with the PI (Michael Lombardo).
At least one, but possibly both, of the PhD positions will also be heavily oriented towards neuroimaging (MRI, fMRI, DTI, MRS) and be co-supervised with CIMeC Professor Jorge Jovicich.
Supervisor: Michael Lombardo. Corresponding research area in the online application: Neurodevelopmental disorders.
5. Machine Learning for Brain Connectivity in Clinical Neuroscience (FBK - 1 position)
Clinical neuroscience is playing a key role in the understanding of the brain with data of pathological alterations. The detection of anomalies in the brain structure and function is a crucial step not only for diagnosis and prognosis but also to decode the connectome of the human brain. Data driven approaches are providing promising results to characterize the patterns of the healthy brain. The challenge is to disentangle the intrinsic interindividual differences in the brain structure and function with respect to alterations related to cognitive impairment.
The research objective is to investigate the most innovative techniques of Artificial Intelligence, such as geometric deep learning, to translate the knowledge of connectivity structures from a healthy population to the individual patients of a clinical study. The ultimate goal is the development of computational methods to support the detection of altered structures in the connectome affected by brain disorders. The successful candidate will have a MSc in computer science, biomedical engineering, physics, or any STEM discipline. Hands-on training in neuroimaging data analysis and/or software tools for brain connectivity will be highly valued.
For reference to recent works of relevance to this project, please see Astolfi et al., MICCAI (2020), Legarreta et al., Medical Image Analysis (2021).
Supervisor: avesani [at] fbk.eu (Paolo Avesani). Corresponding research area in the online application: Machine Intelligence.