Dataset Information

Multiscale fingerprinting of neuronal functional connectivity.

ABSTRACT: Current cellular-based connectomics approaches aim to delineate the functional or structural organizations of mammalian brain circuits through neuronal activity mapping and/or axonal tracing. To discern possible connectivity between functionally identified neurons in widely distributed brain circuits, reliable and efficient network-based approaches of cross-registering or cross-correlating such functional-structural data are essential. Here, a novel cross-correlation approach that exploits multiple timing-specific, response-specific, and cell-specific neuronal characteristics as coincident fingerprint markers at the systems, network, and cellular levels is proposed. Application of this multiscale temporal-cellular coincident fingerprinting assay to the respiratory central pattern generator network in rats revealed a descending excitatory pathway with characteristic activity pattern and projecting from a distinct neuronal population in pons to its counterparts in medulla that control the post-inspiratory phase of the respiratory rhythm important for normal breathing, airway protection, and respiratory-vocalization coordination. This enabling neurotracing approach may prove valuable for functional connectivity mapping of other brain circuits.

SUBMITTER: Song G

PROVIDER: S-EPMC4305356 | biostudies-literature | 2015 Sep

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Multiscale fingerprinting of neuronal functional connectivity.

Song Gang G Tin Chung C Poon Chi-Sang CS

Brain structure & function 20140724 5

Current cellular-based connectomics approaches aim to delineate the functional or structural organizations of mammalian brain circuits through neuronal activity mapping and/or axonal tracing. To discern possible connectivity between functionally identified neurons in widely distributed brain circuits, reliable and efficient network-based approaches of cross-registering or cross-correlating such functional-structural data are essential. Here, a novel cross-correlation approach that exploits multi ...[more]

PMID: 25056933

Similar Datasets

Project description:Background. Datasets consisting of synthetic neural data generated with quantifiable and controlled parameters are a valuable asset in the process of testing and validating directed functional connectivity metrics. Considering the recent debate in the neuroimaging community concerning the use of these metrics for fMRI data, synthetic datasets that emulate the BOLD signal dynamics have played a central role by supporting claims that argue in favor or against certain choices. Generative models often used in studies that simulate neuronal activity, with the aim of gaining insight into specific brain regions and functions, have different requirements from the generative models for benchmarking datasets. Even though the latter must be realistic, there is a tradeoff between realism and computational demand that needs to be contemplated and simulations that efficiently mimic the real behavior of single neurons or neuronal populations are preferred, instead of more cumbersome and marginally precise ones. Methods. This work explores how simple generative models are able to produce neuronal datasets, for benchmarking purposes, that reflect the simulated effective connectivity and, how these can be used to obtain synthetic recordings of EEG and fMRI BOLD signals. The generative models covered here are AR processes, neural mass models consisting of linear and nonlinear stochastic differential equations and populations with thousands of spiking units. Forward models for EEG consist in the simple three-shell head model while the fMRI BOLD signal is modeled with the Balloon-Windkessel model or by convolution with a hemodynamic response function. Results. The simulated datasets are tested for causality with the original spectral formulation for Granger causality. Modeled effective connectivity can be detected in the generated data for varying connection strengths and interaction delays. Discussion. All generative models produce synthetic neuronal data with detectable causal effects although the relation between modeled and detected causality varies and less biophysically realistic models offer more control in causal relations such as modeled strength and frequency location.

Project description:Primary progressive aphasia is a syndrome characterized by progressive loss of language abilities with three main phenotypic clinical presentations, including logopenic, non-fluent/agrammatic, and semantic variants. Previous imaging studies have shown unique anatomic impacts within language networks in each variant. However, direct measures of spontaneous neuronal activity and functional integrity of these impacted neural networks in primary progressive aphasia are lacking. The aim of this study was to characterize the spatial and temporal patterns of resting state neuronal synchronizations in primary progressive aphasia syndromes. We hypothesized that resting state brain oscillations will show unique deficits within language network in each variant of primary progressive aphasia. We examined 39 patients with primary progressive aphasia including logopenic variant (n = 14, age = 61 ± 9 years), non-fluent/agrammatic variant (n = 12, age = 71 ± 8 years) and semantic variant (n = 13, age = 65 ± 7 years) using magnetoencephalographic imaging, compared to a control group that was matched in age and gender to each primary progressive aphasia subgroup (n = 20, age = 65 ± 5 years). Each patient underwent a complete clinical evaluation including a comprehensive battery of language tests. We examined the whole-brain resting state functional connectivity as measured by imaginary coherence in each patient group compared to the control cohort, in three frequency oscillation bands-delta-theta (2-8 Hz); alpha (8-12 Hz); beta (12-30 Hz). Each variant showed a distinct spatiotemporal pattern of altered functional connectivity compared to age-matched controls. Specifically, we found significant hyposynchrony of alpha and beta frequency within the left posterior temporal and occipital cortices in patients with the logopenic variant, within the left inferior frontal cortex in patients with the non-fluent/agrammatic variant, and within the left temporo-parietal junction in patients with the semantic variant. Patients with logopenic variant primary progressive aphasia also showed significant hypersynchrony of delta-theta frequency within bilateral medial frontal and posterior parietal cortices. Furthermore, region of interest-based analyses comparing the spatiotemporal patterns of variant-specific regions of interest identified in comparison to age-matched controls showed significant differences between primary progressive aphasia variants themselves. We also found distinct patterns of regional spectral power changes in each primary progressive aphasia variant, compared to age-matched controls. Our results demonstrate neurophysiological signatures of network-specific neuronal dysfunction in primary progressive aphasia variants. The unique spatiotemporal patterns of neuronal synchrony signify diverse neurophysiological disruptions and pathological underpinnings of the language network in each variant.

Dataset Information

Multiscale fingerprinting of neuronal functional connectivity.

Publications

Multiscale fingerprinting of neuronal functional connectivity.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets