MIST: A multi-resolution parcellation of functional brain networks

Sample

All data were taken from the publicly available Cambridge dataset¹⁵, which is available from the 1000 functional connectomes project¹⁶. This dataset consists of 198 subjects (123 female) between 18 and 30 years of age. Six minutes of rs-fMRI data (TR=3s, 119 time points) were recorded on a 3 Tesla Siemens Tim Trio Scanner (Siemens Medical Systems, Erlangen, Germany) at 3s TR using a 12-channel head coil. Information on gender, age and handedness were provided in the dataset along with the imaging data.

Preprocessing

The data were preprocessed using the Neuroimaging Analysis Kit (NIAK) version 0.12.4¹⁷, an Octave¹⁸ based open source processing and analysis framework for imaging data. Each individual data set was corrected for differences in slice acquisition time. Head motion parameters were estimated by spatially re-aligning individual time points with the median volume in the series. This reference median volume was then aligned with the anatomical T1 image of the individual which in turn was coregistered onto the MNI152 template space¹⁹ using an initial affine transformation followed by a nonlinear transformation. Finally, each individual time point was mapped to MNI152 space using the combined spatial transformations. For time points with excessive in scanner motion (> 0.5mm framewise displacement), the time point as well as the preceding and the two following time points were removed from the time series²⁰. Nuisance covariates were regressed from the remaining time series: slow time drifts (basis of discrete cosines with a 0.01 Hz high-pass cut-off), average signals in conservative masks of the white matter and the lateral ventricles as well as the first principal components (95% energy) of the six rigid-body motion parameters and their squares^21,22. Data were then spatially smoothed with a 3D gaussian kernel (FWHM = 6mm).

Quality control

The quality of the registration operations was visually inspected following an in-house developed quality control protocol²³. Specifically the registration between the individual T1 scans and the stereotaxic template space, as well as registration between the individual T1 and the median functional image were assessed. Subjects were excluded from the analysis in cases of failed coregistration with the template space or where less than 40 time points had acceptable amounts of head motion after scrubbing. A total of 190 subjects (119 female, mean age 21 years, SD 2.3) passed quality control criteria for inclusion in the analysis.

Generating the MIST parcellation

Functional connectivity was computed as the fisher-z transformed Pearson correlation between time series. Stable networks of functional connectivity were extracted from rsfMRI data using an implementation of the Bootstrap Analysis of Stable Clusters (BASC) algorithm¹⁴ within NIAK. The goal of the BASC process is to identify groups of brain regions that exhibit consistent and strong functional connectivity at the individual and group level. In brief, the process consists of three stages:

First, we apply a dimensionality reduction procedure using a region growing algorithm²⁴. The algorithm iteratively merges spatially contiguous voxels with highly correlated time series to form 1095 functional atoms (part of the release as MIST_ATOM). Second, we want to identify stable functional connections between these atoms at the level of individual subjects. To do so, we take 1000 bootstrap samples of the individual time series and then repeatedly cluster atoms by functional connectivity using hierarchical agglomerative clustering. We estimate how consistently two atoms are clustered together by averaging the binary adjacency matrices across bootstrap samples. This average adjacency matrix is called the individual stability matrix. Third, at the group level we want to identify connections that are consistently stable across individuals. To do so, we repeatedly sample subsets of individuals from the dataset and average their individual stability matrices. We then apply hierarchical agglomerative clustering on the average stability matrix within each subsample and binarize the resulting clustering assignment. Similar to the individual level clustering we average the binarized adjacency matrices across subsamples to obtain the group level stability matrix. Lastly, we generate the group level consensus partition on the group level stability matrix to identify clusters of stable group level connections. A more detailed description of the method can be found in the BASC paper¹⁴.

Selecting the resolution. The BASC process involves decisions about the spatial resolution, i.e. the number of groups that atoms are assigned to based on the stability of their connections. These spatial resolutions must be selected for the BASC process at the individual level, at the group level, and for the final decomposition of the group stability matrix into networks. Since the stability matrices at the individual and group level are generated by clustering atoms into discrete groups they are not resolution independent, i.e. changing the number of groups will change the stability matrix. The BASC process must therefore be repeated for every combination of individual, group and network level spatial resolutions.

Our goal for the MIST parcellation was to describe stable functional networks and how they break up into smaller functional assemblies across spatial resolutions. We used the the multiscale stepwise selection algorithm (MSTEPS) to identify those spatial resolutions that best reflect major transitions in network organization across resolutions²⁵. This process identified nine representative group level resolutions [7, 12, 20, 36, 64, 122, 197, 325, and 444] that we included in the MIST parcellation. An additional parcellation was generated by splitting parcels of resolution 122 along the midline, which resulted in 210 fully lateralized parcels. To characterize the spatially contiguous nature of the parcels at this resolution and to distinguish it from the other parcellations we call this resolution the ‘ROI’ resolution and refer to the corresponding parcels as ROIs.

Parcels

Figure 1 provides an overview of the functional parcels identified at the different resolutions. The first step in resolution space from 7 to 12 sees the default mode network split into 3 parts, the downstream visual areas separate from the fronto parietal network, and the auditory network and insulae separate from the ventral attention network (to explore the MIST parcellations and their decomposition across resolutions, please refer to our interactive web viewer). Beyond resolution 12, parcels begin breaking up within these large scale networks. This is illustrated by the decomposition of the somatomotor network into 3 parcels at resolution 36 and 7 parcels at resolution 122 (see Figure 2).

Figure 1. Parcellation overview.

Parcellations MIST_7 through MIST_444 with slice coordinates denoted in MNI coordinates. Annotated parcellations are highlighted by orange parcellation names. Note that MIST_ROI is generated by splitting MIST_122 along the midline into fully lateralized ROIs. Parcel colors are random.

Figure 2. Decomposition of the Somatomotor network.

(Top row) MIST_7 rendered on the Civet²⁶ standard surface for easier visualization. (Lower left) MIST_7 parcels highlighted over the sample-average atom by atom connectivity matrix. (Lower right) Zoomed in view on the surface and connectivity matrix representations of the Somatomotor network. Resolutions from top to bottom are: MIST_7, MIST_36, MIST_122. Surface parcel colors correspond to parcels overlayed on the atom by atom connectivity matrix.

Parcels at lower resolutions are exclusively bilateral and highly symmetric, and are generally made up of spatially discontinuous subregions. These subregions tend to separate first while remaining largely bilateral. The decomposition of parcels into left- and right-hemispheric subcomponents occurs more gradually (see Figure 3). Between resolutions 36 and 64, parcels tend to become less symmetric and more lateralized. At resolution 122, from which MIST_ROI is generated by forcing a split along the midline, more than 70% of parcels span both hemispheres. MIST_444 has 120 parcels (or ~27% of parcels) that encompass areas in both hemispheres.

Figure 3. Parcel properties across resolutions.

(Top row) Parcel symmetry across MIST resolutions. MIST_ROI is the lateralized version of MIST_122 and doesn’t have symmetric parcels. (Middle row) Parcel bilaterality scores across MIST resolutions. Higher values indicate a more bilateral parcel. Bilaterality scores are computed as 1 minus the absolute value of the parcel laterality. All parcels of MIST_ROI are by definition fully lateralized and hence have a bilaterality score of 0. (Bottom row) Internal homogeneity of parcels across MIST resolutions. Annotated parcels are highlighted in orange. Hover over individual data points to see additional information. The online version of this figure is interactive.

Hierarchy. While parcellations at each resolution have been generated independently from each other, we found that parcels at higher resolutions largely remain within the boundaries of their lower resolution parents. This allowed us to generate a pseudo-hierarchy by assigning each parcel at resolution R to the parcel at resolution R-1 with the highest overlap. This pseudo-hierarchy provides a decomposition tree of large scale functional networks into fine grained functional nodes at higher resolution. The majority of parcels have an overlap of close to 100% with their parent parcels. Our web viewer gives an interactive representation of the decomposition trees for the 7 functional networks at resolution MIST_7. However, a subset of parcels at higher resolutions does not overlap fully with the root of the pseudo-hierarchical decomposition tree it belongs to. These parcels are predominantly located at the interface of two low resolution functional networks and may represent more ambiguous boundary regions. Figure 4 provides the overlap of parcels at all resolutions with their root parcel (the MIST_7 parcel that the pseudo-hierarchy assigns them to). Note that we haven’t included MIST_ROI here as it is a transformation of MIST_122 and not an independent parcellation.

Figure 4. Overlap with root parcels in the pseudo-hierarchy.

The overlap with the corresponding pseudo-hierarchical root-parcel at MIST_7 is shown for parcels across resolutions. Overlap is represented in percent overlap of total parcel size. Higher values indicate an assignment more in line with the data derived pseudo-hierarchy. Annotated parcels are highlighted in orange. The online version of this figure is interactive.

Parcel nomenclature. Our goal in naming the functional parcels presented here was to provide an intuitive description of the general brain areas included in a parcel. Wherever appropriate, we tried to name parcels based on current terminology in the rsfMRI literature. For parcels that could not be described in their entirety by an established name, we listed the significant subcomponents of the parcel. Particularly at higher spatial resolutions, when parcels would encompass relatively small and well described areas, a functional name could sometimes not be justified. In these cases we named the parcel based on the underlying brain anatomy.

The parcel names included in this release are based on a systematic naming system (outlined below) to ensure consistency.

Naming template:

Naming example:

All fields except the {NAME} field are optional and only used where they add information to the name.

For each parcel name we also provide an abbreviated parcel label designed to take up less space while still being informative. The convention for labels follows that of names but uses different upper- and lowercase rules and values:

Label template:

Label example:

Suggested use, limitations and planned improvement of parcel names. It is important to note that the parcel names are intended to help navigate the parcellations by giving the reader a quick idea where in the brain a parcel is situated. They are not intended for precise delineation of a particular anatomical region or functional pathway. While the parcels names provided with this release are unique, they are also not intended as unique descriptors of individual parcels. We suggest that individual parcels are referred to by their integer ID and parcellation resolution. For example, the parcel 6 of the parcellation resolution 64 should be referred to as 6@MIST_64.

Parcel properties. Apart from individual labels we provide a set of quantitative properties for each parcel.

Data quality assessment

This parcellation encompasses three main elements:

Here we have assessed the quality of the brain-parcellations and the derived hierarchy. The functional and anatomical parcel labels provided with this release are at this point considered suggestions and we call upon the community to provide feedback and suggestions to improve them using our interactive web viewer.

Quality of parcels

To assess the quality of our parcellations we estimated the within parcel functional homogeneity, a common quality metric in the literature^27,28. A good functional brain parcel is expected to encompass voxels with reasonably similar functional activity. Here we estimate the functional homogeneity of a parcel as the average functional connectivity between its voxels. All other things being equal, smaller parcels will have higher internal homogeneity than larger ones, particularly when they are spatially contiguous. We therefore compute the global homogeneity of a parcellation as the size-weighted average of its individual parcels.

We compare our parcellations to a number of published brain parcellations in the literature (see Table 1). Parcellations that were generated on surface data were back-projected into MNI volumetric space. As a consequence, brain coverage differs between parcellations with some covering only the cortical ribbon while others also providing parcellations of subcortical regions and the cerebellum. A parcellation with only cortical coverage will on average have smaller parcels than a full brain parcellation of the same resolution. To account for parcel-size dependent differences in homogeneity, we therefore opted to compare parcellations based on the average size of their parcels (in voxels). Lastly, to allow for direct comparison, all parcellations were resampled to 3mm isotropic voxels.

To conduct the quality assessments we used the openly available HNU1 dataset from the Consortium for Reliability and Reproducibility²⁹ that provides 10 repeat session of fMRI data acquired over the course of a month for 30 individuals. This dataset allows us to average our quality metrics across sessions per individual, giving a more stable estimate. In addition none of the compared brain parcellations has been generated on this data, providing an unbiased estimate of performance on other datasets. Data were preprocessed and quality controlled according to the protocol described above. 27 individuals (15 female, mean age 24.5 years, SD 2.4) were included in the analysis.

Figure 5 shows the global homogeneity of parcels over the logarithm of the average parcel size. The relationship between homogeneity and parcel size is clearly visible in the exponential decline of homogeneity with parcel size. Our multi-resolution parcellations compare well with other parcellations of similar parcel size, particularly at lower resolutions. A second question is how consistent the estimated homogeneity scores are across individuals. High consistency of homogeneity indicates that a parcellation generalizes well across individuals whereas a low consistency indicates a very case-dependent fit of the parcellation to the individual functional architecture. Figure 6 shows the standard deviation of homogeneity estimates across subjects as a function of the logarithm of average parcel size. Larger parcels show lower standard deviation of homogeneity across individuals. Again, our parcels fall well within the range of cross-subject consistency of other atlases with comparable resolutions.

Figure 5. Comparison with other parcellations.

Global parcel homogeneity of MIST parcellations and commonly used other brain parcellations of comparable size and focus. Homogeneity estimates are plotted over the common logarithm of the average parcel size. Higher scores reflect more homogeneous parcels. Annotated parcellations are highlighted in orange. The online version of this figure is interactive.

Figure 6. Consistency of homogeneity estimates as a function of parcel size.

The standard deviation (SD) of homogeneity estimates across individuals in the HNU1 data set is shown as a function of the common logarithm of the average parcel size. Lower scores reflect lower variability in homogeneity estimates between individuals. Annotated parcellations are highlighted in orange. The online version of this figure is interactive.

Quality of hierarchical decomposition

Secondly, we assessed the accuracy of the hierarchical decomposition of parcels across multiple resolutions. We generated the hierarchy by assigning each parcel to the parcel of the next lower resolution it has the highest overlap with (see Methods for details). A good hierarchical decomposition would result in parcels that decompose into fully enclosed subparcels. We therefore tested the percent overlap of parcels with their parent parcels across the resolutions. Our interactive web viewer shows an overview of parcel overlap across resolutions. At every resolution, most parcels have a very high degree of overlap with their parent parcel. Parcels with lower parent overlap are predominantly located along the interface of parietal and occipital lobe and in the lateral prefrontal cortex. The parieto-occipital interface is also a transition area between two large scale functional networks at lower resolution and parcels along this interface could shift across resolutions along the stability gradient of this transition zone. The weighted average of parent overlap across resolutions is very high at 95%, indicating a generally excellent alignment of sub parcels with their parent parcel.

We finally looked at the entire hierarchical decomposition tree to find the overlap of parcels with the root parcel of their decomposition tree. The root parcels are the parcels in the MIST_7 parcellation for which decomposition across resolutions is tracked independently. While each parcel is assigned to the parcel of the next lower resolution it has the maximal overlap with, incremental non-complete overlaps can accumulate to a point where a parcel no longer has any overlap with the root parcel it is assigned to by our pseudo-hierarchy. Out of 1227 parcels across all nine resolutions (not including MIST_ROI) we identified 116 parcels that didn’t overlap with the root parcel of their hierarchical decomposition tree. It is important to note that some of these parcels are themselves decompositions of non-overlapping parcels at lower resolutions. Across the MIST resolutions we found parcels with no root overlap in MIST_36: 1 parcel ~(3%), MIST_64: 4 parcels ~(6%), MIST_122: 7 parcels (~6%), MIST_197: 16 parcels (~8%), MIST_325: 33 (~10%), MIST_444: 55 parcels (12%). None of these parcels had root-parcels located in the somatomotor, visual or cerebellar networks of MIST_7. Figure 4 provides an overview of the root-parcel overlap across scales. While it is unclear what factors may cause the gradual shifts of these identified parcels it is important to note that the majority of parcels have high degrees of overlap with both their immediate parent parcels as well as their root-parcels. We are therefore confident that our pseudo-hierarchy captures meaningful cross-resolution relationships between parcels.

Generalizability of parcellation

Lastly, MIST_64 has been recently used to compare the impact of the choice of brain parcellations on different prediction problems in functional connectivity³⁸. The authors generated functional connectomes from three pre-generated parcellations (Harvard-Oxford Atlas, AAL, and MIST_64) and from custom parcellations derived directly from the data using a range of methods (ICA, dictionary learning, K-means clustering, and Hierarchical clustering using Ward’s criterion). They then used the estimated functional connectomes of each parcellation to predict schizophrenia diagnosis on individuals of the COBRE dataset³⁹, differentiate individuals with Alzheimer’s Disease from those with Mild Cognitive Impairment in the ADNI dataset⁴⁰, and Marijuana users from abstinent individuals in the ACPI dataset⁴¹. The authors found that across prediction problems, the 64 resolution MIST parcellation performed consistently well and on par with data specific parcellations³⁸.