Among other uses in neuroscience research, brain-machine interface systems are extensively used in sleep studies to deliver stimuli in response to the detection of salient brain signals (Bellesi et al. 2014, Fehér et al. 2021). In particular, we're interested in building a closed-loop protocol able to simultaneously register and monitor the EEG activity, recognize sleep slow waves (0.5 - 4 Hz), and deliver a stimulus with the finest possible time precision within a predetermined phase of the oscillation. The aim for the brainhack is to group together with different experts to tackle the deriving conceptual and computational issues, to finally create a publicly available small Python package containing a subset of functions to implement the closed-loop protocol.

Goals for the BrainHack:
This project presents several non-trivial issues that should be addressed, during the brainhack we will mainly focus on three points:

• Chose and implementation of the algorithm for online slow wave detection
• Testing the slow wave detection algorithm on data (offline)
• Testing and improving the EEG algorithm for recording and monitoring brain signals, its integration with the detection algorithm and with stimulation delivery (online)

Contributors' skills:
Contributors can participate in different ways, helping us brainstorming on conceptual problems to figure out a valid strategy to achieve the scope, or actively programming algorithms and testing functions.Some of these skills can be useful to participate: EEG signal processing, machine/deep learning, python programming, control system.

The landscape of scientific research is shaped by the dual forces of exploitation—deepening knowledge within established domains—and exploration—venturing into novel areas. Traditional metrics such as citation counts fail to capture the nuanced interplay of these behaviors. This proposal introduces a novel Niche Index, designed to quantify researchers' tendencies towards exploitation or exploration, thereby offering a multidimensional view of scientific contributions.
Objectives:

• To develop a Niche Index that quantifies the exploitation and exploration behaviors in research scientists.
• Compare the different proposed metrics, including a comparison with traditional research indicators (e.g., h-index)
• To create a framework that allows academic institutions to understand the research dynamics of their faculty better, informing resource allocation and strategic planning.

Goals for the BrainHack:

• Day 1: Foundation and Framework Development
• Day 2: Analysis and Development
• Day 3: Refinement and Presentation

Contributors' skills:

• Natural Language Processing and Network analysis
• Data Management and Web Scraping
• Literature Review and Synthesis
• Data Visualization and Presentation Tools

Neuronal oscillation, a process where neurons communicate with rhythmic patterns, can be observed as waves in electroencephalographic (EEG) recordings of brain signals. This communication mode is a cost-efficient way to achieve various cognitive processes ranging from temporal binding of sensory inputs, attention selection to even memory consolidation. Recent developments in our understanding of neuronal oscillation suggest that the phase of slower oscillations can modulates the amplitude of faster neuronal oscillations, a phenomenon known as Phase-Amplitude Coupling (PAC). PAC can be observed in large part of the scalp with EEG and is a putative mechanism for distributing information among large-scale networks with implications for learning, memory consolidation and retrieval.
This project aims to develop an algorithm for detecting PAC events in continuous EEG recordings, focusing initially on the detection of slow-waves coupled with spindles since their co-occurrence is a well characterized phenomenon in sleep EEG and will serve as ‘ground-truth’ for the validation of the method.
The successful method will be adapted to be extend to the cross-coupling of any kind of frequency range, increasing the possibility of usage to a broader extent and answering new biological questions.

Goals for the BrainHack:

• Review the literature on methods of Phase-Amplitude Coupling (PAC) to determine the methods best suited for our algorithm.
• Detect visually specific PAC events (namely Slow-oscillation-spindle coupling) in whole-night EEG recordings.
• Implement the method to extract PAC in a time-frequency manner.
• Implement a clustering algorithm to detect and label automatically changes in PAC.
• Test and validate the full algorithm on the previously labeled whole-night EEG recordings.

Contributors' skills:

• Python coding
• Experience in EEG
• Experience in signal processing
• Experience in clustering algorithm

Structural connectivity in the brain is typically studied by reducing its observation to a single spatial resolution. However, the brain possesses a rich architecture organised over multiple scales linked to one another (Betzel and Bassett, 2017). Simple organising principles underlie the multiscale architecture of human structural brain networks, where the same connectivity law dictates short- and long-range connections between different brain regions over many resolutions. Such a multiscale property can be appreciated by progressively coarse-graining the connected anatomical regions by changing the observation resolution.
This project aims to apply a recently proposed graph coarse-graining method (the Laplacian Renormalization Group: Villegas et al., 2023) to the human connectome and identify the network density's role in identifying multiple connectivity scales.

Goals for the BrainHack:

• To create a pipeline for an effective structural network sparsification, starting from probabilistic and deterministic tractography of multiple subjects.
• To adapt the Laplacian Renormalization Group algorithm for the human connectome.
• To establish the working limits of a non-geometric coarse-graining in terms of network density.

Contributors' skills:

• Familiarity with DTI and Tractography
• General proficiency with Python/MATLAB/Bash
• Network analysis
• Data Visualisation and Communication