Mapping dependencies of BOLD signal change to end-tidal CO2: Linear and nonlinear modeling, and effect of physiological noise correction.

BACKGROUND: Disentangling physiological noise and signal of interest is a major issue when evaluating BOLD-signal changes in response to breath holding. Currently-adopted approaches for retrospective noise correction are general-purpose, and have non-negligible effects in studies on hypercapnic challenges. NEW METHOD: We provide a novel approach to the analysis of specific and non-specific BOLD-signal changes related to end-tidal CO2 (PETCO2) in breath-hold fMRI studies. Multiple-order nonlinear predictors for PETCO2 model a region-dependent nonlinear input-output relationship hypothesized in literature and possibly playing a crucial role in disentangling noise. We explore Retrospective Image-based Correction (RETROICOR) effects on the estimated BOLD response, applying our analysis both with and without RETROICOR and analyzing the linear and non-linear correlation between PETCO2 and RETROICOR regressors. RESULTS: The RETROICOR model of noise related to respiratory activity correlated with PETCO2 both linearly and non-linearly. The correction affected the shape of the estimated BOLD response to hypercapnia but allowed to discard spurious activity in ventricles and white matter. Activation clusters were best detected using non-linear components in the BOLD response model. COMPARISON WITH EXISTING METHOD: We evaluated the side-effects of standard physiological noise correction procedure, tailoring our analysis on challenging understudied brainstem and subcortical regions. Our novel approach allowed to characterize delays and non-linearities in BOLD response. CONCLUSIONS: RETROICOR successfully avoided false positives, still broadly affecting the estimated non-linear BOLD responses. Non-linearities in the model better explained CO2-related BOLD signal fluctuations. The necessity to modify the standard procedure for physiological-noise correction in breath-hold studies was addressed, stating its crucial importance.

Ld-EEG Effective Brain Connectivity in Patients With Cheyne-Stokes Respiration.

The characterization of brain cortical activity in heart-failure patients affected by Cheyne-Stokes Respiration might provide relevant information about the mechanism underlying this pathology. Central autonomic network is gaining increasing attention for its role in the regulation of breathing and cardiac functions. In this scenario, evaluating changes in cortical connectivity associated with Cheyne-Stokes Respiration may be of interest in the study of specific brain-activity related to such disease. Nonetheless, the inter subject variability, the temporal dynamics of Central-Apnea/Hyperpnea cycles and the limitations of clinical setups lead to different methodological challenges. To this aim, we present a framework for the assessment of cortico-cortical interactions from Electroencephalographic signals acquired using low-density caps and block-design paradigms, arising from endogenous triggers. The framework combines ICA-decomposition, unsupervised clustering, MVAR modelling and a permutation-bootstrap strategy for evaluating significant connectivity differences between conditions. A common network, lateralized towards the left hemisphere, was depicted across 8 patients exhibiting Cheyne-Stokes Respiration patterns during acquisitions. Significant differences in connectivity at the group level were observed based on patients' ventilatory condition. Interactions were significantly higher during hyperpnea periods with respect to central apneas and occurred mainly in the delta band. Opposite-sign differences were observed for higher frequencies (i.e. beta, low-gamma).

Gotta Trace 'em All: A Mini-Review on Tools and Procedures for Segmenting Single Neurons Toward Deciphering the Structural Connectome.

Decoding the morphology and physical connections of all the neurons populating a brain is necessary for predicting and studying the relationships between its form and function, as well as for documenting structural abnormalities in neuropathies. Digitizing a complete and high-fidelity map of the mammalian brain at the micro-scale will allow neuroscientists to understand disease, consciousness, and ultimately what it is that makes us humans. The critical obstacle for reaching this goal is the lack of robust and accurate tools able to deal with 3D datasets representing dense-packed cells in their native arrangement within the brain. This obliges neuroscientist to manually identify the neurons populating an acquired digital image stack, a notably time-consuming procedure prone to human bias. Here we review the automatic and semi-automatic algorithms and software for neuron segmentation available in the literature, as well as the metrics purposely designed for their validation, highlighting their strengths and limitations. In this direction, we also briefly introduce the recent advances in tissue clarification that enable significant improvements in both optical access of neural tissue and image stack quality, and which could enable more efficient segmentation approaches. Finally, we discuss new methods and tools for processing tissues and acquiring images at sub-cellular scales, which will require new robust algorithms for identifying neurons and their sub-structures (e.g., spines, thin neurites). This will lead to a more detailed structural map of the brain, taking twenty-first century cellular neuroscience to the next level, i.e., the Structural Connectome.

On the Use of Linear-Modelling-based Algorithms for Physiological Noise Correction in fMRI Studies of the Central Breathing Control.

A full characterization of the physiological behavior of human central chemoreceptors through fMRI is crucial to understand the pathophysiology of central abnormal breathing patterns. In this scenario, physiological noise and activity of interest may be naturally correlated. Here, we examined the adequacy of linear-modelling-based retrospective physiological noise correction for studies of the central breathing control. We focused on the relationship between a nonlinear model of BOLD response, hypothesized to describe neuronal specific activity, and noise modelled by correction algorithms. Analyses were performed on fMRI acquisitions from healthy subjects during a breath hold task. A general linear model including static nonlinearities in the response to end-tidal CO2 was applied to data preprocessed both with and without physiological noise correction. Relations between physiological noise and PETCO2 were explored both with linear and nonlinear measures. Lastly, parametric maps of noise spatial distribution were extracted. Our results evidenced that correction algorithms based on linear modelling remove components that are both linearly and nonlinearly related to end-tidal CO2, whereas uncorrected data showed spurious activations in regions outside gray matter. Thus, despite a correction step is fundamental, these algorithms are shown to be over-conservative approaches to noise correction and need to be adapted to the specific purpose.

Exploring the supra linear relationship between PetCO2 and fMRI signal change with ICA.

The relationships between brain functions and the respiratory system are complex. Disentangling brain activity related to CO2 changes from nonspecific vasoreactivity is a challenge when studying brain activity involved in the control of breathing with fMRI. In this work, we analyzed a dose dependent relationship between arterial CO2 levels and brain response. To accomplish this goal, we developed a gas administration protocol, together with multi-subject ICA and specific nonlinear post-processing analysis. Our results highlighted a supra-linear response to CO2 challenges in brainstem, thalamus and putamen. Results were discussed in the light of current knowledge about the central respiratory network.

A Manual Segmentation Tool for Three-Dimensional Neuron Datasets.

To date, automated or semi-automated software and algorithms for segmentation of neurons from three-dimensional imaging datasets have had limited success. The gold standard for neural segmentation is considered to be the manual isolation performed by an expert. To facilitate the manual isolation of complex objects from image stacks, such as neurons in their native arrangement within the brain, a new Manual Segmentation Tool (ManSegTool) has been developed. ManSegTool allows user to load an image stack, scroll down the images and to manually draw the structures of interest stack-by-stack. Users can eliminate unwanted regions or split structures (i.e., branches from different neurons that are too close each other, but, to the experienced eye, clearly belong to a unique cell), to view the object in 3D and save the results obtained. The tool can be used for testing the performance of a single-neuron segmentation algorithm or to extract complex objects, where the available automated methods still fail. Here we describe the software's main features and then show an example of how ManSegTool can be used to segment neuron images acquired using a confocal microscope. In particular, expert neuroscientists were asked to segment different neurons from which morphometric variables were subsequently extracted as a benchmark for precision. In addition, a literature-defined index for evaluating the goodness of segmentation was used as a benchmark for accuracy. Neocortical layer axons from a DIADEM challenge dataset were also segmented with ManSegTool and compared with the manual "gold-standard" generated for the competition.

Clarifying CLARITY: Quantitative Optimization of the Diffusion Based Delipidation Protocol for Genetically Labeled Tissue.

Tissue clarification has been recently proposed to allow deep tissue imaging without light scattering. The clarification parameters are somewhat arbitrary and dependent on tissue type, source and dimension: every laboratory has its own protocol, but a quantitative approach to determine the optimum clearing time is still lacking. Since the use of transgenic mouse lines that express fluorescent proteins to visualize specific cell populations is widespread, a quantitative approach to determine the optimum clearing time for genetically labeled neurons from thick murine brain slices using CLARITY2 is described. In particular, as the main objective of the delipidation treatment is to clarify tissues, while limiting loss of fluorescent signal, the "goodness" of clarification was evaluated by considering the bulk tissue clarification index (BTCi) and the fraction of the fluorescent marker retained in the slice as easily quantifiable macroscale parameters. Here we describe the approach, illustrating an example of how it can be used to determine the optimum clearing time for 1 mm-thick cerebellar slice from transgenic L7GFP mice, in which Purkinje neurons express the GFP (green fluorescent protein) tag. To validate the method, we evaluated confocal stacks of our samples using standard image processing indices (i.e., the mean pixel intensity of neurons and the contrast-to-noise ratio) as figures of merit for image quality. The results show that detergent-based delipidation for more than 5 days does not increase tissue clarity but the fraction of GFP in the tissue continues to diminish. The optimum clearing time for 1 mm-thick slices was thus identified as 5 days, which is the best compromise between the increase in light penetration depth due to removal of lipids and a decrease in fluorescent signal as a consequence of protein loss: further clearing does not improve tissue transparency, but only leads to more protein removal or degradation. The rigorous quantitative approach described can be generalized to any clarification method to identify the moment when the clearing process should be terminated to avoid useless protein loss.