Biology subjects

Gordon, E. M.

Publications and source records attributed to Gordon, E. M..

4 recordsLinked to original sources

bioRxiv2018

The community structure of functional brain networks exhibits scale-specific patterns of variability across individuals and time

The network organization of the human brain varies across individuals, changes with development and aging, and differs in disease. Discovering the major dimensions along which this variability is displayed remains a central goal of both neuroscience and clinical medicine. Such efforts can be usefully framed within the context of the brains modular network organization, which can be assessed quantitatively using powerful computational techniques and extended for the purposes of multi-scale analysis, dimensionality reduction, and biomarker generation. Though the concept of modularity and its utility in describing brain network organization is clear, principled methods for comparing multi-scale communities across individuals and time are surprisingly lacking. Here, we present a method that uses multi-layer networks to simultaneously discover the modular structure of many subjects at once. This method builds upon the well-known multi-layer modularity maximization technique, and provides a viable and principled tool for studying differences in network communities across individuals and within individuals across time. We test this method on two datasets and identify consistent patterns of inter-subject community variability, demonstrating that this variability - which would be undetectable using past approaches - is associated with measures of cognitive performance. In general, the multi-layer, multi-subject framework proposed here represents an advancement over current approaches by straighforwardly mapping community assignments across subjects and holds promise for future investigations of inter-subject community variation in clinical populations or as a result of task constraints.

neuroscience↗

bioRxiv2018

Reward-related regions form a preferentially coupled system at rest

Neuroimaging studies have implicated a set of striatal and orbitofrontal cortex (OFC) regions that are commonly activated during reward processing tasks. Resting-state functional connectivity (RSFC) studies have demonstrated that the human brain is organized into several functional systems that show strong temporal coherence even in the absence of goal-directed tasks. Here we use seed-based and graph-theory RSFC approaches to characterize the systems-level organization of putative reward regions of at rest. Peaks of connectivity from seed-based RSFC patterns for the nucleus accumbens (NAcc, anatomically defined) and orbitofrontal cortex (OFC, functionally defined) were used to identify candidate reward regions which were merged with a previously used set of regions (Power et al., 2011). Graph-theory was then used to determine system-level membership for all regions. Several regions previously implicated in reward-processing (NAcc, lateral and medial OFC, and ventromedial prefrontal cortex) comprised a distinct, preferentially coupled system. This RSFC system is stable across a range of connectivity thresholds and shares strong overlap with meta-analyses of task-based reward studies. This reward system shares between-system connectivity with systems implicated in cognitive control and self-regulation, including the fronto-parietal, cingulo-opercular, and default systems. Further, differences may exist in the pathways through which control systems interact with key regions of this reward system. Whereas NAcc regions of the reward system are functionally connected to cingulo-opercular and default systems, OFC regions of the reward system show stronger connectivity with the fronto-parietal system. We propose that future work may be able to interrogate group or individual differences in connectivity profiles using the regions delineated in the current work to explore potential relationships to appetitive behaviors, self-regulation failure, and addiction.

neuroscience↗

bioRxiv2018

Precision inhibitory stimulation of individual-specific cortical hubs slows information processing in humans

Non-invasive brain stimulation (NIBS) is a promising treatment for psychiatric and neurologic conditions, but outcomes are variable across treated individuals. This variability may be due in part to uncertainty in the selection of the stimulation site - a challenge complicated further by the variable organization of individual human brains. In principle, precise targeting of individual-specific brain areas serving outsized roles in cognition could improve the efficacy of NIBS. Network theory predicts that the importance of a node in network can be inferred from its connections; as such, we hypothesized that targeting individual-specific \"hub\" brain areas with NIBS would impact cognition more than non-hub brain areas. We first demonstrate that the spatial positioning of hubs is variable across individuals, but highly-reproducible when mapped with sufficient per-individual rsfMRI data. We then tested our hypothesis in healthy individuals using a prospective, within-subject, double-blind design. We found that inhibiting a hub with NIBS disrupted information processing during working-memory to a greater extent than inhibiting a non-hub area of the same gyrus. Furthermore, inhibition of hubs linking specific control networks and sensorimotor systems was retrospectively found to be most impactful. Based on these findings, we propose that precise mapping of individual-specific brain network features could inform future interventions in patients.\n\nSIGNIFICANCE STATEMENTThe network organization of every persons brain is different, but non-invasive brain stimulation (NIBS) interventions do not take this variation into account. Here we demonstrate that the spatial positions of brain areas theoretically serving important roles in cognition, called hubs, differs across individual humans, but are stable within an individual upon repeated neuroimaging. We found that administering NIBS to these individual-specific hub brain areas impacted cognition more than stimulation of non-hub areas. This finding indicates that future NIBS interventions can target individual-specific, but cognitively-relevant features of human brains.

neuroscience↗

bioRxiv2017

Local-Global Parcellation of the Human Cerebral Cortex From Intrinsic Functional Connectivity MRI

A central goal in systems neuroscience is the parcellation of the cerebral cortex into discrete neurobiological \"atoms\". Resting-state functional magnetic resonance imaging (rs-fMRI) offers the possibility of in-vivo human cortical parcellation. Almost all previous parcellations relied on one of two approaches. The local gradient approach detects abrupt transitions in functional connectivity patterns. These transitions potentially reflect cortical areal boundaries defined by histology or visuotopic fMRI. By contrast, the global similarity approach clusters similar functional connectivity patterns regardless of spatial proximity, resulting in parcels with homogeneous (similar) rs-fMRI signals. Here we propose a gradient-weighted Markov Random Field (gwMRF) model integrating local gradient and global similarity approaches. Using task-fMRI and rs-fMRI across diverse acquisition protocols, we found gwMRF parcellations to be more homogeneous than four previously published parcellations. Furthermore, gwMRF parcellations agreed with the boundaries of certain cortical areas defined using histology and visuotopic fMRI. Some parcels captured sub-areal (somatotopic and visuotopic) features that likely reflect distinct computational units within known cortical areas. These results suggest that gwMRF parcellations reveal neurobiologically meaningful features of brain organization and are potentially useful for future applications requiring dimensionality reduction of voxel-wise fMRI data. Multi-resolution parcellations generated from 1489 participants are available (https://github.com/ThomasYeoLab/CBIG/tree/master/stable_projects/brain_parcellation/Schaefer2018_LocalGlobal)

neuroscience↗