neuroimaging - Max Planck Neuroscience

Deep learning models to study sentence comprehension in the human brain

MPFI — Wed, 28 Jun 2023 14:18:28 +0000

Recent artificial neural networks that process natural language achieve unprecedented performance in tasks requiring sentence-level understanding. As such, they could be interesting models of the integration of linguistic information in the human brain. We review works that compare these artificial language models with human brain activity and we assess the extent to which this approach has improved our understanding of the neural processes involved in natural language comprehension. Two main results emerge. First, the neural representation of word meaning aligns with the context-dependent, dense word vectors used by the artificial neural networks. Second, the processing hierarchy that emerges within artificial neural networks broadly matches the brain, but is surprisingly inconsistent across studies. We discuss current challenges in establishing artificial neural networks as process models of natural language comprehension. We suggest exploiting the highly structured representational geometry of artificial neural networks when mapping representations to brain data.

Sophie Arana, Jacques Pesnot Lerousseau & Peter Hagoort (2023): Deep learning models to study sentence comprehension in the human brain, Language, Cognition and Neuroscience. Link

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Decoding cognition from spontaneous neural activity

MPFI — Tue, 26 Apr 2022 01:08:11 +0000

In human neuroscience, studies of cognition are rarely grounded in non-task-evoked, ‘spontaneous’ neural activity. Indeed, studies of spontaneous activity tend to focus predominantly on intrinsic neural patterns (for example, resting-state networks). Taking a ‘representation-rich’ approach bridges the gap between cognition and resting-state communities: this approach relies on decoding task-related representations from spontaneous neural activity, allowing quantification of the representational content and rich dynamics of such activity. For example, if we know the neural representation of an episodic memory, we can decode its subsequent replay during rest. We argue that such an approach advances cognitive research beyond a focus on immediate task demand and provides insight into the functional relevance of the intrinsic neural pattern (for example, the default mode network). This in turn enables a greater integration between human and animal neuroscience, facilitating experimental testing of theoretical accounts of intrinsic activity, and opening new avenues of research in psychiatry.

Liu, Y., Nour, M.M., Schuck, N.W. et al. Decoding cognition from spontaneous neural activity. Nat Rev Neurosci 23, 204–214 (2022) Article Link

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Assessing reliability in neuroimaging research through intra-class effect decomposition (ICED)

MPFI — Tue, 07 Aug 2018 15:30:11 +0000

Magnetic resonance imaging has become an indispensable tool for studying associations of structural and functional properties of the brain with behavior in humans. However, generally recognized standards for assessing and reporting the reliability of these techniques are still lacking. Here, we introduce a new approach for assessing and reporting reliability, termed intra-class effect decomposition (ICED). ICED uses structural equation modeling of data from a repeated-measures design to decompose reliability into orthogonal sources of measurement error that are associated with different characteristics of the measurements, for example, session, day, or scanning site. This allows researchers to describe the magnitude of different error components, make inferences about error sources, and inform them in planning future studies. We apply ICED to published measurements of myelin content and resting state functional connectivity. These examples illustrate how longitudinal data can be leveraged separately or conjointly with cross-sectional data to obtain more precise estimates of reliability.

Brandmaier, A. M., Wenger, E., Bodammer, N. C., Kühn, S., Raz, N., & Lindenberger, U. (2018). Assessing reliability in neuroimaging research through intra-class effect decomposition (ICED). eLife, 7:e35718.
https://elifesciences.org/articles/35718

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Neuroimaging Techniques

MPFI — Thu, 17 Aug 2017 14:41:01 +0000

This is an intensive and comprehensive laboratory-oriented course focusing on applying imaging techniques to neuroscience research. The objective of this imaging course is to gain exposure to modern imaging tools from principle optics to applications in modern neuroscience.