What’s in a functional brain parcellation?
Anonymous Author(s)
AfﬁliationAddress email
Abstract
To communicate, to ground hypotheses and models, to analyse data, neuroscientistsoften refer to divisions of the brain.
Here we consider atlases used to parcellate thebrain when studying brain function.
We discuss the meaning and the validity ofthese parcellations, from a conceptual point of view as well as by running variousanalytical tasks on popular functional brain parcellations.
Breaking up the human brain in territories is a longstanding tradition, dating back to Brodmann areas[5].
While these subdivisions of the brain originated from a desire to reveal homogeneous neuralpopulations, they have become a major communication tool in human neurosciences.
As such, abrain parcellation shapes how we think about units of the brain.
When studying brain function, egthe neural implementations of mental processes, the macroscopic brain structures that we investigateimplicitly shape our decompositions of mental function and the models we ﬁt to data.
Even theseemingly-simple study of brain responses to stimuli is tied to a choice of functional units.
Forinstance, in the ventral visual stream, the Fusiform Face Area (FFA) is generally associated to facerecognition [15], yet it is also strongly associated with visual-expertise in object recognition [10], andneurons supporting face recognition overlap in a distributed way with place recognition [14].
Beyondstimuli response, brain-wide models call for macroscopic units, for instance to study small-wordproperties of brain functional connectivity [1], to build coupled-oscillator dynamical models of neuralactivity, [8], or ﬁtting spiking-neuron network models to brain-activity data [17].
Brain imaging has brought many different divisions of the brain into areas, available as brain atlasesor parcellations.
However, their extraction from brain-imaging data all entail different modelingchoices.
Even for atlases based on anatomical structures, there is weak concordance across differentatlases used [4].
As functional subdivisions are not always simply visible in the microstructure orthe brain anatomy, the problem is even more pronounced for the choice of functional units.
Yet
, thechoice of functional parcellation impacts models of brain functions that are learned from these [25].
Here, we discuss principles to guide these choices.
We ﬁrst consider the meaning and constructvalidity of functional brain units.
We then conduct an empirical study using analytic questions thatprobe different aspects of functional parcels, on 6 popular brain-imaging functional parcellations.
How to think about functional brain parcellations?
What are functional units?
A neurocognitive model entails brain units associated with speciﬁcfunctions.
For instance, a model of spatial navigation could position long-term spatial-memoryrepresentations in the hippocampus and short-term visuospatial representation in the intra-parietalsulcus.
Both structures are fairly big, and encompass many millions of neurons.
Should they besubdivided?
What should be their speciﬁc boundaries?
The problem is particularly pronouncedin the cortex which is a continuous sheet of neurons.
Models of vision provide a paradigmaticexample of successful cognitive decomposition of a functional system.
We understand vision wellenough to break it down in elementary constituents that can be mapped precisely to neural supports[13].
Studying local descriptors of the visual ﬁeld, edge detection, color, or orientation, leads to retinotopic maps that reveal neural populations with local gradients and yet large-scale organizationsin functional modules: from low-level functional units V1, V2...
all the way to mid and high-levelrepresentations as in IT or the FFA.
From a cognitive modeling perspective, these visual areas can beseen as implementing an elementary operation, analogous to layers of an artiﬁcial neural network [9].
In an ideal world, a functional brain parcellation would capture such units.
Can a large-scale division of the brain true, or merely useful?
There is very active research onfull-brain functional parcellations, used for instance to model functional connectivity.
Multimodalimaging data has been used to seek a division of the brain in units with homogeneous neurobiologicalproperties [11].
One goal is to reﬂect intrinsic brain structure with parcels that characterize betterbrain locations than stereotactic coordinates.
Yet, representing brain function on a few hundredparcels is a vast simpliﬁcation compared to the 100 billions neurons in the human brain, or the100 thousands voxels in an fMRI volume.
Even prototypical functional areas well known for sharpintrinsic functional properties, such as V1, have ﬁner topological functional organization, as withretonotopic maps or ocular-dominance columns.
Sharp boundaries are not present in other, higherlevel, paradigmatic functional areas, such as the FFA, characterized by functional properties –facerecognition– that partly overlap with neighboring areas [14].
The picture of functionally-uniformunits is a convenient simpliﬁcation with no intrinsic truth.
And yet, it is very useful.
A functionalparcellation is a crucial component to build a rich picture of the neural basis of mental function: Atthe level of a study, it provides a necessary data reduction to ﬁt full-brain models to the data.
At thelevel of the ﬁeld, the brain structures that it delineates deﬁne common objects of study.
Better MR-based functional parcellations Some brain parcellations are however more usefulthan others.
Nodes adapted to the functional signal give better models of functional connectivity[22, 7].
Given the weak concordance between anatomical atlases [4], grounding a functional analysison anatomical labels brings little beneﬁt.
Rather, the functional subdivisions can be learned fromlarge-scale fMRI data [20].
An empirical investigation of some functional atlases for MR imaging
Measuring the analytic utility of an atlas To serve as a common object in the ﬁeld, a goodfunctional brain parcellation should be well suited for a variety of analytic tasks that model brainactivity and its relationship to behavior.
a. Mapping brain responses Standard analysis in fMRI strives to detect difference in brain responses.
Performing it on parcels mitigates multiple comparisons and inter-subject spatial variability [23].
Good parcels lead to detecting brain structure that match at the voxel level with
well-powered analysis on the original volumetric data.
b. Decoding brain function Assign a functional label to a brain structure calls for decoding: predicting mental processes from observed brain activity [19].
Good functional units would haveclear-cut functional labels and thus help decoding performance.
c. Fidelity to the original signal Summarizing brain activity on large parcels necessarily leads to
signal loss.
A good parcellation should minimize this distortion for a given number of regions.d.
Functional-connectivity biomarkers Brain parcellations are often used to deﬁne the nodes of
functional-connectivity models.
An independent validation of such a model is whether it can be
well associated with variations of behavioral or clinical traits across subjects [7].
Popular functional atlases We investigate 6 popular atlases derived from fMRI, detailed in Table 1.
The atlases differ in their number of regions, whether these regions are deﬁned with continuous orbinary maps, and which method was used to extract them.
a. Consistency in detection of neural responses We run an fMRI standard analysis at the parcellevel and compare the overlap of detected brain territories to detections at the voxel level.
We
calibrate the noise level with the consistency of single subjects with regards to group-level results.b. Decoding brain function We compare the performance of an SVM decoders trained on the data extracted on various parcellations and at the voxel level.
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AAL (114) x=
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Craddock (200) x=
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BASC (122) c. Fidelity to the original signal We compute the fraction of variance of the original signal ex- plained by
the
summarizing brain
images
on
parcels
d. Functional biomarkers
We measure the prediction performance using a standard functionalconnectivity prediction pipeline [7] on the different parcellations.
Results: which atlases lead to clear analysis For standard analysis (Figure 1a) and data approximation (Figure 1c), where analysis on the voxel-level data deﬁnes the gold standard, the larger thenumber of regions, the better the performance.
On the other hand, for predictive tasks –decoding brainresponses (Figure 1b) or biomarkers from functional connectivity (Figure 1d)– a reduced number ofregions acts as a regularizer and using a few hundred regions outperforms voxel-level analysis.
At alow dimensionality, ICA-derived functional atlases performed comparatively well, conﬁrming theusefulness of continuously deﬁned node reported in [7].
At higher dimensions, BASC [3] gave thebest overall compromise, aside from very high-dimensional settings ( > 500 ) only covered by the
Schaefer parcellation [20].
Overall, for most analytic tasks, very high-dimensional atlases –with anumber of regions neighboring the thousand– are beneﬁcial.
Only to build connectomes is it useful tolimit the number of nodes to a few hundreds.
This can be explained because the number of edgesgrows quadratically, and quickly encounters the curse of dimensionality.
Conclusion
Atlases deﬁning functional regions can lead to better constructs and better data analysis.
Validatingthese functional regions is challenging, yet a guiding principle is that they should improve statisticalmodeling.
More research is needed in high-dimensional atlases, which prove very useful.
Consensusand adoption of easily-accessible functional atlases is important.
Indeed, in the mean time, manycomputational modeling studies [1, 8] default to the AAL, an atlas that is neither functionnal, norcaptures well anatomical boundaries (Figure 2).
But the evidence to ground the choice of a functional atlas is subtle and there is not simple story.
From a pure signal-processing point of view (Figure 1 a and c), the best option at low dimensionalityare ICA-derived modes, and at higher dimensionality the BASC atlas, built by clustering fMRI withweak spatial constraints.
Decoding performance (Figure 1b) gives a useful measure of the functionalspeciﬁcity of the units deﬁned [24].
In this respect, for higher dimensionalities the Schaefer atlas[20] –built from clustering with more spatial constraints– appears to give the most useful functionalunits.
Finally, the best option to build brain-connectivity models (Figure 1d) is the BASC atlas.
Continuously-deﬁned modes, as opposed to hard parcellations, give excellent expressive power forlow dimensionality, however no such atlas is currently available at high dimensionality