An exemplar-based approach to individualized parcellation reveals the need for sex specific functional networks

Salehi, Mehraveh; Karbasi, Amin; Shen, Xilin; Mayes, Linda C.; Constable, R. Todd

doi:10.1016/j.neuroimage.2017.08.068

Cited by 45 publications

(59 citation statements)

References 81 publications

(121 reference statements)

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“…In the first data set, which we refer to as Yale data, we applied an extended version of our recently developed individualized parcellation algorithm 11,34 to each functional run, generating one parcellation atlas for each condition and each scanning session (8 conditions × 30 sessions = 240 atlases total). A key factor in this algorithm is that each parcellation begins with an atlas obtained from a separate group of subjects and finds an exemplar time-course for each parcel and then grows the parcels (see Methods and Figure 7 for a detailed explanation of the algorithm).…”

Section: Resultsmentioning

confidence: 99%

There is no single functional atlas even for a single individual: Parcellation of the human brain is state dependent

Salehi

Karbasi

et al. 2018

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The goal of human brain mapping has long been to delineate the functional subunits in the brain and elucidate the functional role of each of these brain regions. Recent work has focused on whole-brain parcellation of functional Magnetic Resonance Imaging (fMRI) data to identify these subunits and create a functional atlas. Functional connectivity approaches to understand the brain at the network level require such an atlas to assess connections between parcels and extract network properties. While no single functional atlas has emerged as the dominant atlas to date, there remains an underlying assumption that such an atlas exists. Using fMRI data from a highly sampled subject as well as two independent replication data sets, we demonstrate that functional parcellations based on fMRI connectivity data reconfigure substantially and in a meaningful manner, according to brain state.

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Section: Resultsmentioning

confidence: 99%

There is no single functional atlas even for a single individual: Parcellation of the human brain is state dependent

Salehi

Karbasi

et al. 2018

Preprint

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“…A potential way to circumvent this confound is using individualized parcellations (e.g. Braga and Buckner, 2017;Gordon et al, 2017b;Kong et al, 2018;Laumann et al, 2015;Salehi et al, 2017;Wang et al, 2015). Given that our goal was confirming and extending work using group parcellations, we did not individualize parcellations here.…”

Section: Individual Differences and Individual Parcellationsmentioning

confidence: 99%

The individual functional connectome is unique and stable over months to years

Horien¹,

Shen

Scheinost

et al. 2017

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Using fMRI and functional connectivity analyses, it is possible to establish a functional connectome for an individual. The extent to which functional connectomes from adolescents and young adults remain identifiable across many years has not been investigated. Here we show in three publically available longitudinal resting-state fMRI datasets that connectome-based identification of adolescents and young adults scanned 1-3 years apart is possible at levels well above chance using whole-brain functional connectivity data. When we restrict the identification process to specific edges, we find that edges in the frontal, parietal, and temporal cortices tend to lead to the highest identification rates. We also demonstrate that highly unique edges contributing the most to a successful ID tend to connect nodes in these same cortical regions, while edges contributing the least tend to connect cross-hemispheric homologs. These results suggest that despite developmental changes, adolescent and young adult subjects have unique and stable functional connectomes and that the frontal, parietal, and temporal cortices are important in defining individual uniqueness in younger subjects.

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“…We calculated and normalized the squared Euclidean distances between the mean time courses of each node pair, generating nine functional distance matrices per subject. We used exemplar-based parcellation (36) to assign these nodes into functional networks in an individualized manner for each state and subject, such that every subject acquired an individualized node-to-network assignment (NNA) for each state. A significant advantage of this approach is that it allows for individualized networks while maintaining correspondence of networks across subjects and states.…”

Section: Functional Network Configuration Varies Across Statesmentioning

confidence: 99%

“…Functional networks were delineated for every subject in each functional state using the exemplar-based parcellation algorithm developed earlier in our group (36). This method provides an approach to summarize data by introducing a set of exemplars that best represents the full data.…”

Section: Individualized and State-specific Functional Network Parcellmentioning

confidence: 99%

“…(36), by attempting to find exemplar labels = D , E , … , F s.t. G ∈ {1, … , = 268}, whose corresponding exemplarvectors for each individual in each state , i.e., N,O = P Q N,O , P R N,O , … , P S N,O ⊆ N,O , maximizes a desired utility function, as follows: exemplar label set and N,O is the set including the corresponding exemplar vectors in subject ∈ {1, … , = 718} at state ∈ 1, … , = 9 .…”

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State-specific individualized functional networks form a predictive signature of brain state

Salehi

Karbasi

et al. 2018

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There is extensive evidence that human brain functional organization is dynamic, varying within a subject as the brain switches between tasks demands. This functional organization also varies across subjects, even when they are all engaged in similar tasks. Currently, we lack a comprehensive model that unifies the two dimensions of variation (brain state and subject). Using fMRI data obtained across multiple task-evoked and rest conditions (which we operationally define as brain states) and across multiple subjects, we develop a state-and subject-specific functional network parcellation (the assignment of nodes to networks). Our parcellation approach provides a measure of how node-to-network assignment (NNA) changes across states and across subjects. We demonstrate that the brain's functional networks are not spatially fixed, but reconfigure with brain state. This reconfiguration is robust and reliable to such an extent that it can be used to predict brain state with accuracies up to 97%.Recent studies have made significant progress in characterizing state-evoked changes in functional connectivity elicited by task performance (20-28). However, none of these works explicitly examined the possibility that networks spatially reconfigure: (i) The majority of these studies explicitly assumed that networks remain spatially unchanged across tasks (21,22,29). These analyses were restricted to investigating differences in connectivity between networks, and not whether networks spatially reconfigure across tasks. (ii) Another line of research takes a more abstract perspective by computing global network measures (such as modularity and participation coefficient) and comparing these measures across different states (27,28,(30)(31)(32). Such approaches address the modular reconfiguration of the brain as a whole by defining a new set of networks for each state, but they do not quantify how or whether the same networks change across states. Such studies also typically define a small number of networks (~3-7) whose correspondence to the putative resting-state networks are unclear. These approaches cannot answer how specific networks reorganize as a function of brain state. (iii) Finally, many studies do not directly examine cross-subject variations in network reorganization (20), or treat crosssubject and cross-session changes as similar notions for defining reorganization (33). However, functional organization varies significantly across subjects, yielding large individual differences in network definitions (34,35). There is a clear need to account for both cross-subject and crossstate variability in functional network organization when characterizing brain dynamics.Here, we use a novel approach to dynamically map the brain's functional networks across taskevoked and resting states and across individuals. We operationalize the brain's functional subunits as a set of 268 pre-defined nodes (6). We then apply our recently developed exemplarbased parcellation method (36) to assign a set of pre-defined nodes to individualized, stat...

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An exemplar-based approach to individualized parcellation reveals the need for sex specific functional networks

Cited by 45 publications

References 81 publications

There is no single functional atlas even for a single individual: Parcellation of the human brain is state dependent

There is no single functional atlas even for a single individual: Parcellation of the human brain is state dependent

The individual functional connectome is unique and stable over months to years

State-specific individualized functional networks form a predictive signature of brain state

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