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A multicenter study of ketamine effects on functional connectivity: Large scale network relationships, hubs and symptom mechanisms.

ABSTRACT: Ketamine is an uncompetitive N-methyl-d-aspartate (NMDA) glutamate receptor antagonist. It induces effects in healthy individuals that mimic symptoms associated with schizophrenia. We sought to root these experiences in altered brain function, specifically aberrant resting state functional connectivity (rsfMRI). In the present study, we acquired rsfMRI data under ketamine and placebo in a between-subjects design and analyzed seed-based measures of rsfMRI using large-scale networks, dorsolateral prefrontal cortex (DLPFC) and sub-nuclei of the thalamus. We found ketamine-induced alterations in rsfMRI connectivity similar to those seen in patients with schizophrenia, some changes that may be more comparable to early stages of schizophrenia, and other connectivity signatures seen in patients that ketamine did not recreate. We do not find any circuits from our regions of interest that correlates with positive symptoms of schizophrenia in our sample, although we find that DLPFC connectivity with ACC does correlate with a mood measure. These results provide support for ketamine's use as a model of certain biomarkers of schizophrenia, particularly for early or at-risk patients.

SUBMITTER: Fleming LM

PROVIDER: S-EPMC6411494 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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A multicenter study of ketamine effects on functional connectivity: Large scale network relationships, hubs and symptom mechanisms.

Fleming Leah M LM Javitt Daniel C DC Carter Cameron S CS Kantrowitz Joshua T JT Girgis Ragy R RR Kegeles Lawrence S LS Ragland John D JD Maddock Richard J RJ Lesh Tyler A TA Tanase Costin C Robinson James J Potter William Z WZ Carlson Marlene M Wall Melanie M MM Choo Tse-Hwei TH Grinband Jack J Lieberman Jeffrey J Krystal John H JH Corlett Philip R PR

NeuroImage. Clinical 20190228

Ketamine is an uncompetitive N-methyl-d-aspartate (NMDA) glutamate receptor antagonist. It induces effects in healthy individuals that mimic symptoms associated with schizophrenia. We sought to root these experiences in altered brain function, specifically aberrant resting state functional connectivity (rsfMRI). In the present study, we acquired rsfMRI data under ketamine and placebo in a between-subjects design and analyzed seed-based measures of rsfMRI using large-scale networks, dorsolateral ...[more]

PMID: 30852397

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Project description:To characterize each cognitive function per se and to understand the brain as an aggregate of those functions, it is vital to relate dozens of these functions to each other. Knowledge about the relationships among cognitive functions is informative not only for basic neuroscientific research but also for clinical applications and developments of brain-inspired artificial intelligence. In the present study, we propose an exhaustive data mining approach to reveal relationships among cognitive functions based on functional brain mapping and network analysis. We began our analysis with 109 pseudo-activation maps (cognitive function maps; CFM) that were reconstructed from a functional magnetic resonance imaging meta-analysis database, each of which corresponds to one of 109 cognitive functions such as 'emotion,' 'attention,' 'episodic memory,' etc. Based on the resting-state functional connectivity between the CFMs, we mapped the cognitive functions onto a two-dimensional space where the relevant functions were located close to each other, which provided a rough picture of the brain as an aggregate of cognitive functions. Then, we conducted so-called conceptual analysis of cognitive functions using clustering of voxels in each CFM connected to the other 108 CFMs with various strengths. As a result, a CFM for each cognitive function was subdivided into several parts, each of which is strongly associated with some CFMs for a subset of the other cognitive functions, which brought in sub-concepts (i.e., sub-functions) of the cognitive function. Moreover, we conducted network analysis for the network whose nodes were parcels derived from whole-brain parcellation based on the whole-brain voxel-to-CFM resting-state functional connectivities. Since each parcel is characterized by associations with the 109 cognitive functions, network analyses using them are expected to inform about relationships between cognitive and network characteristics. Indeed, we found that informational diversities of interaction between parcels and densities of local connectivity were dependent on the kinds of associated functions. In addition, we identified the homogeneous and inhomogeneous network communities about the associated functions. Altogether, we suggested the effectiveness of our approach in which we fused the large-scale meta-analysis of functional brain mapping with the methods of network neuroscience to investigate the relationships among cognitive functions.

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Dataset Information

A multicenter study of ketamine effects on functional connectivity: Large scale network relationships, hubs and symptom mechanisms.

Publications

A multicenter study of ketamine effects on functional connectivity: Large scale network relationships, hubs and symptom mechanisms.

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