bioRxiv Science⌕ Search

bioRxiv · 10.1101/099820

Identification of Mechanisms of Functional Signaling Between Human Hippocampus Regions

Abstract

BackgroundStandard BOLD connectivity analyses depend on aggregating the signals of individual voxels within regions of interest (ROIs). In certain cases, this spatial aggregation implies a loss of valuable functional and anatomical information about subsets of voxels that drive the ROI level connectivity.\n\nNew MethodWe use the FGES algorithm, a data-driven score-based graphical search method, to identify subsets of voxels that are chiefly responsible for exchanging signals between ROIs. We apply the method to high-resolution resting state functional magnetic resonance imaging (rs-fMRI) data from medial temporal lobe regions of interest of a single healthy individual measured repeated times over a year and a half.\n\nResultsThe FGES algorithm recovered subsets of voxels within larger medial temporal lobe ROIs of entorhinal cortex and hippocampus subfields that show spatially consistency across different scanning sessions, and are statistically significant under tests that validate the role of these subsets as main drivers of effective connectivity between hippocampal regions of interest.\n\nComparison with Existing MethodsIn contrast to standard functional connectivity methods, the FGES algorithm is robust against false positive connections produced by transitive closures of adjacencies (correlation methods) and common effect conditioning (Markov random field methods).\n\nConclusionsThe FGES algorithm allows for identification of communication subsets of voxels driving the connectivity between regions of interest, recovering valuable anatomical and functional information that is lost when ROIs are aggregated. The FGES algorithm is specially suited for voxelwise connectivity research, given its short running time and scalability to big data problems.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Sanchez-Romero, R., Ramsey, J. D., Liang, J. C., Glymour, C.. 2017-01-12. Identification of Mechanisms of Functional Signaling Between Human Hippocampus Regions. https://doi.org/10.1101/099820

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

Why probe registration is crucial for infant fNIRS and HD-DOT? An evaluation across 49 newborns

Source-detector probe registration, i.e., the mapping of optode positions on the scalp to their true anatomical locations in head/brain space, is crucial for brain imaging with fNIRS or HD-DOT. While individual, MRI-based registration is desirable, it is not always feasible, motivating the development of alternative registration approaches. This study uses the STORM-Net registration protocol and quantifies the variation of cap alignment in a cohort of 49 newborns to evaluate how this affects reconstructed images using HD-DOT. Relative to a centrally aligned template, the cohort showed a median probe-position error of 10.12 mm, while the least and most misaligned subjects exhibited mean errors of 4.41 mm and 20.60 mm, respectively. The results demonstrate that the level of cap misalignment typically observed in newborn imaging can affect the spatial accuracy of reconstructed functional images, particularly in cases with larger probe displacements. These findings provide a quantitative basis for adopting more conservative interpretations of group-level functional brain activity and functional connectivity results, and strongly support the routine use of registration methods in infant HD-DOT studies.

neuroscience↗

Multimodal characterization of putative pan-inhibitory viral constructs in macaque monkeys

Transgenic approaches and validated viral vector toolboxes have advanced our understanding of how inhibitory neurons shape cognition and motor control in mice and other rodents. However, such advances have been elusive in non-human primates (NHPs). While many studies demonstrate expression of reporter proteins, few studies have provided clear quantitative evidence of whether these viral constructs can be reliably used for assessment of in vivo and in vitro function in primates, which, in turn, has hindered widespread adoption by NHP researchers. Here, we characterize putative pan-inhibitory cell targeting vectors engineered with regions from the Dlx5-6 intergenic domain. Pan-inhibitory viral vectors are attractive because they enable ground-truth optotagging and modulation of multiple inhibitory subtypes in the same animal and, when combined with machine learning approaches, enable cell-type classifiers for NHPs. We combined in vivo anesthetized electrophysiological recordings, in vitro slice electrophysiology, and immunohistochemistry across seven rhesus macaque monkeys to assess the specificity, completeness and efficacy of three constructs in multiple sensory and higher-order association cortices. The vectors reliably restricted transgene expression to inhibitory neurons (81-97%). However, of the local GABAergic neurons only 32-64% expressed the transgene, depending on the construct. This incompleteness arose in part from uneven coverage across subtypes, with strong transgene expression in parvalbumin-positive interneurons (~60-90%), sparse expression in calbindin-positive interneurons (~1-7%), and near-complete avoidance of calretinin-positive interneurons. A construct pairing DLX2.0 with ChrimsonR was excellent for in vivo optotagging, while h56D paired with bReaChES most effectively suppressed neural activity in vivo. In the slice, activating terminals of transgene-expressing neurons optically evoked inhibitory currents in coupled pyramidal neurons confirming transsynaptic functionality of these constructs and their use for inhibitory circuit-specific synaptic assessments and manipulation. Collectively, the results provide rigorous functional characterization, and quantitative guidance for NHP researchers seeking to deploy GABAergic cell-type-specific tools in their experiments.

neuroscience↗

Glider compositionality and information capacity in an E/I-balanced neuronal automaton

In cellular automata, localized activity patterns called gliders propagate and interact to carry and transform information. Whether neuronal networks can harness this computational power remains unclear. Here, we demonstrate that a cortical network model with separate excitatory and inhibitory populations, graded cellular activity and uniform local connectivity spontaneously generates diverse, interacting gliders. An analytical reduction identifies the cellular conditions sustaining propagation of the dominant G-glider. Distinct gliders can assemble into growing structures that emit other glider types. Near excitation/inhibition balance, sparse glider dynamics coexist with high computational capacity, measured by input differentiation and generalization, with local capacity varying across space and time. Strong mutual inhibition between interneurons instead permits metastatic proliferation with low computational capacity. These findings connect cellular automata to neuronal information processing and identify interacting gliders as a candidate mechanism for dynamic plasticity: the reorganization of computation within an unchanged synaptic architecture.

neuroscience↗