Separating parallel threads of cognition to better explain behaviour

Project Information

COGNITIVE THREADS

Grant agreement ID: 850404

DOI

10.3030/850404

EC signature date 23 January 2020

Start date 1 November 2020

End date 31 October 2026

Funded under

• EXCELLENT SCIENCE - European Research Council (ERC)

Total cost

€ 1 490 515,00

EU contribution

€ 1 490 515,00

Coordinated by

AARHUS UNIVERSITET
Denmark

Periodic Reporting for period 3 - COGNITIVE THREADS (Separating parallel threads of cognition to better explain behaviour)

Reporting period: 2023-11-01 to 2025-04-30

Summary of the context and overall objectives of the project

The study of the human brain pursues the understanding of how neural structure and function translate into cognition and behaviour. This general goal has two complementary directions. The first is to uncover fundamental principles that generalise across an entire population. The second is to describe the neural underpinnings of specific individual differences; that is, instead of generalising, the aim is to investigate the particularities of specific individuals. Focusing on the study of stimulus processing, this project bridges between these two approaches by developing novel statistical techniques aimed at dissecting the different parallel aspects of this processing —which is based on general principles— and characterising how such aspects are unique to each individual. This endeavour is important because, bringing closer together goals of basic and applied science, it will contribute methods that can, for example, help to provide better and more individualised treatments and more accurate diagnoses/prognoses by being better at dissecting the heterogeneity of both the causes and the symptoms.

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Conclusions for the final period: we have produced tools to address methodological challenges in the fields of neuroscience and clinical research, including software and tools that we expect to be adopted by other researchers. While the goal of developing tools for finding differences between subjects is been successful, we are still making progress on the statistical disambiguation of concurrent processes from functional data.

Work performed from the beginning of the project to the end of the period covered by the report and main results achieved so far

In this first period of the grant, the group has produced a number of scientific papers that build the foundations for our general objective. These cover questions about how to use effectively these models to predict subject traits (given the difficulties of using the raw data for prediction), about the reliability of the model estimations (critical for their use in practical scenarios) and about how to optimally perform model selection (i.e. how do we choose our models).

Furthermore, we are about the release a software package in Python to make these methods publicly available.

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We have released a Python package with our tools, resulting in two publications (one in Imaging Neuroscience and the other in Nature Protocols). We see how the adoption of these tools by the community is steadily progressing.

Progress beyond the state of the art and expected potential impact (including the socio-economic impact and the wider societal implications of the project so far)

By the end of the project, we expect to move beyond theoretical work and apply these methods to real world applications, as was originally proposed in the project

--- edit 10 September ----

We are currently pushing towards the application of these methods in clinical research.

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