Subject‐specific segregation of functional territories based on deep phenotyping

Abstract

Functional magnetic resonance imaging (fMRI) has opened the possibility to investigate how brain activity is modulated by behavior. Most studies so far are bound to one single task, in which functional responses to a handful of contrasts are analyzed and reported as a group average brain map. Contrariwise, recent data‐collection efforts have started to target a systematic spatial representation of multiple mental functions. In this paper, we leverage the Individual Brain Charting (IBC) dataset—a high‐resolution task‐fMRI dataset acquired in a fixed environment—in order to study the feasibility of individual mapping. First, we verify that the IBC brain maps reproduce those obtained from previous, large‐scale datasets using the same tasks. Second, we confirm that the elementary spatial components, inferred across all tasks, are consistently mapped within and, to a lesser extent, across participants. Third, we demonstrate the relevance of the topographic information of the individual contrast maps, showing that contrasts from one task can be predicted by contrasts from other tasks. At last, we showcase the benefit of contrast accumulation for the fine functional characterization of brain regions within a prespecified network. To this end, we analyze the cognitive profile of functional territories pertaining to the language network and prove that these profiles generalize across participants.

Predicting brain activation maps for arbitrary tasks with cognitive encoding models

“…The present findings extend previous work that had established the ability of encoding models to predict task activation maps for broad sets of cognitive tasks ( Pinho et al, 2021 ; Nakai and Nishimoto, 2020 ), by demonstrating the ability to use expert annotations of cognitive functions as the basis for the encoding model. This provides the potential to use cognitive encoding models to test cognitive theories; whereas cognitive theories rarely make specific predictions regarding locations of brain activation, they nearly always make predictions regarding the specific processes engaged by a particular set of tasks, and hence the similarity or overlap of task-related activation maps.…”

“…This study also demonstrated the ability to predict activation patterns for unseen tasks, using a latent feature space derived from the Neurosynth database ( Yarkoni et al, 2011 ). The ability to predict activation patterns for novel tasks based on data from other tasks was also demonstrated by Pinho et al, ( Pinho et al, 2021 ), using the extensive Individualized Brain Charting database ( Pinho et al, 2018 ).…”

Mapping Cytoarchitectonics and Receptor Architectonics to Understand Brain Function and Connectivity

“…fMRI datasets can also be correlated with nuclear imaging (PET and SPECT maps) of various neurotransmitter systems by using the user interface of the JuSpace toolbox (61). Moreover, the atlas provides access to a high resolution task-fMRI dataset (62).…”

Linking Brain Structure, Activity, and Cognitive Function through Computation

“… The multilevel Human Brain Atlas provides different maps, e.g., ( A ) Julich-Brain cytoarchitectonic atlas ( Amunts et al, 2020 ), ( B ) DTI-based maps of fiber bundles Guevara ( Guevara et al, 2012 , 2017 ), and ( D ) functional parcellation based on task-based fMRI ( Pinho et al, 2021a ). C , Microscopical data are available through the BigBrain model ( Amunts et al, 2013 ).…”

“…Julich-Brain is a part of the Human Brain Atlas and serves as a cytoarchitectonic reference, while taking intersubject variability into account ( Amunts et al, 2020 ). It is linked to a comprehensive map of DTI-based fiber tracts ( Guevara et al, 2012 , 2017 ), functional parcellation schemes based on multiple fMRI in a well-defined group of subjects ( Pinho et al, 2021a ), which provide insights into the cognitive dimension of brain parcellation. MR-based approaches are central to open up applications into in vivo imaging, which is relevant for medical research.…”