Atlases of cognition with large-scale human brain mapping

Abstract

To map the neural substrate of mental function, cognitive neuroimaging relies on controlled psychological manipulations that engage brain systems associated with specific cognitive processes. In order to build comprehensive atlases of cognitive function in the brain, it must assemble maps for many different cognitive processes, which often evoke overlapping patterns of activation. Such data aggregation faces contrasting goals: on the one hand finding correspondences across vastly different cognitive experiments, while on the other hand precisely describing the function of any given brain region. Here we introduce a new analysis framework that tackles these difficulties and thereby enables the generation of brain atlases for cognitive function. The approach leverages ontologies of cognitive concepts and multi-label brain decoding to map the neural substrate of these concepts. We demonstrate the approach by building an atlas of functional brain organization based on 30 diverse functional neuroimaging studies, totaling 196 different experimental conditions. Unlike conventional brain mapping, this functional atlas supports robust reverse inference: predicting the mental processes from brain activity in the regions delineated by the atlas. To establish that this reverse inference is indeed governed by the corresponding concepts, and not idiosyncrasies of experimental designs, we show that it can accurately decode the cognitive concepts recruited in new tasks. These results demonstrate that aggregating independent task-fMRI studies can provide a more precise global atlas of selective associations between brain and cognition.

Fig 1. Brain mapping with a cognitive ontology.

Our approach characterizes the task conditions that correspond to each brain image with terms from a cognitive ontology. Forward inference maps differences between brain responses for a given term and its neighbors in the ontology, i.e. closely related psychological notions. Reverse inference is achieved by predicting the terms associated with the task from brain activity. The figure depicts the analysis of visual object perception tasks with motor response. A forward inference captures brain responses in motor, primary visual and high-level visual areas. Reverse inference captures which regions or neural substrate are predictive of different terms, discarding common response to different tasks, here in the primary visual cortex.

Fig 2. Ontology informed decoding.

The hierarchical decoding procedure reduces the dimensionality by stacking the decision functions of several simple binary classifiers, which mimic study-level contrasts by opposing each term to matching ones. A second level of one-versus-all (OvA) classifiers predicts the presence of terms using the output of the first level. The first layer may be seen as capturing whether a given brain activity map looks more like face or place recognition, objects or scrambled images, visual or motor stimuli. The second layer combines this information to conclude on what cognitive terms best describe the given activity. Final linear classifiers may be recovered by combining the coefficients of the first and second level classifiers.

Fig 3. Maps for the different inference types.

Left (a–d): maps of the different inferences on our database for the “place” concept. The consensus between reverse inference and forward inference based on contrasts defined from the ontology singles out the “parahippocampal place area” (PPA) for the “place” concept. Right (d): the NeuroSynth reverse-inference map for this concept. Reverse inference with Neurosynth also narrows well on the PPA, but is more noisy.

Fig 4. Different functional atlases.

Regions outlined using different functional mapping approaches, from left to right: a. forward term mapping; b. forward inference with ontology contrasts (standard analysis); c. reverse inference with logistic regression; d. NeuroSynth reverse inference; and e. our approach, mapping with decoding and an ontology. The top part shows visual regions, and the lower one auditory regions in the left hemisphere. Forward term mapping outlines overlapping regions, as brain responses capture side effects such as the stimulus modality: for visual and auditory regions every cognitive term is represented in the corresponding primary cortex. Forward mapping using contrasts removes the overlap in primary regions, but a large overlap persists in mid-level regions, as control conditions are not well matched across studies. Standard reverse inference, specific to a term, creates overly sparse regions though with little overlap. Reverse inference with Neurosynth also displays large overlap in mid-level regions. Finally, ontology-based decoding maps recover known functional areas the visual and auditory cortices.

Fig 5. Prediction scores for different methods.

Area under the ROC curve (1 is perfect prediction, while chance is at 0.5); a score for each term; b score relative to the average per term for each decoding approach. As the terms in NeuroSynth do not fully overlap with the terms used in our database, not every term has a prediction score with NeuroSynth. The ontology-informed decoder is almost always able to assign the right cognitive concepts to an unknown task and clearly out-performs standards decoders: logistic regression and naive Bayes classifier trained on our database. It also outperforms the NeuroSynth decoding based on meta-analysis.

Fig 6. Functional atlases with decoding in an ontology.

Regions linked to the various cognitive terms by our mapping approach. They are displayed in 5 different panels depending on their location in the brain: a. visual regions; b. auditory regions; c. motor regions; d. parietal regions; e.cerebellum regions.