Marco Ganzetti

Can sliding-window correlations reveal dynamic functional connectivity in resting-state fMRI?

During the last several years, the focus of research on resting-state functional magnetic resonance imaging (fMRI) has shifted from the analysis of functional connectivity averaged over the duration of scanning sessions to the analysis of changes of functional connectivity within sessions. Although several studies have reported the presence of dynamic functional connectivity (dFC), statistical assessment of the results is not always carried out in a sound way and, in some studies, is even omitted. In this study, we explain why appropriate statistical tests are needed to detect dFC, we describe how they can be carried out and how to assess the performance of dFC measures, and we illustrate the methodology using spontaneous blood-oxygen level-dependent (BOLD) fMRI recordings of macaque monkeys under general anesthesia and in human subjects under resting-state conditions. We mainly focus on sliding-window correlations since these are most widely used in assessing dFC, but also consider a recently proposed non-linear measure. The simulations and methodology, however, are general and can be applied to any measure. The results are twofold. First, through simulations, we show that in typical resting-state sessions of 10 min, it is almost impossible to detect dFC using sliding-window correlations. This prediction is validated by both the macaque and the human data: in none of the individual recording sessions was evidence for dFC found. Second, detection power can be considerably increased by session- or subject-averaging of the measures. In doing so, we found that most of the functional connections are in fact dynamic. With this study, we hope to raise awareness of the statistical pitfalls in the assessment of dFC and how they can be avoided by using appropriate statistical methods.

Whole brain myelin mapping using T1- and T2-weighted MR imaging data

Despite recent advancements in MR imaging, non-invasive mapping of myelin in the brain still remains an open issue. Here we attempted to provide a potential solution. Specifically, we developed a processing workflow based on T1-w and T2-w MR data to generate an optimized myelin enhanced contrast image. The workflow allows whole brain mapping using the T1-w/T2-w technique, which was originally introduced as a non-invasive method for assessing cortical myelin content. The hallmark of our approach is a retrospective calibration algorithm, applied to bias-corrected T1-w and T2-w images, that relies on image intensities outside the brain. This permits standardizing the intensity histogram of the ratio image, thereby allowing for across-subject statistical analyses. Quantitative comparisons of image histograms within and across different datasets confirmed the effectiveness of our normalization procedure. Not only did the calibrated T1-w/T2-w images exhibit a comparable intensity range, but also the shape of the intensity histograms was largely corresponding. We also assessed the reliability and specificity of the ratio image compared to other MR-based techniques, such as magnetization transfer ratio (MTR), fractional anisotropy (FA), and fluid-attenuated inversion recovery (FLAIR). With respect to these other techniques, T1-w/T2-w had consistently high values, as well as low inter-subject variability, in brain structures where myelin is most abundant. Overall, our results suggested that the T1-w/T2-w technique may be a valid tool supporting the non-invasive mapping of myelin in the brain. Therefore, it might find important applications in the study of brain development, aging and disease.

Functional connectivity and oscillatory neuronal activity in the resting human brain

Perceptions, thoughts, emotions and actions emerge from interactions between neuronal assemblies distributed across the brain rather than from local computations in restricted brain areas. Indeed, the operation of every cognitive act requires the integration of distributed activity, as implemented through long-range neuronal communication via a network of structural connections. Functional interactions in the brain are very often studied in subjects at rest, since the resting state is a privileged condition in which brain activity is unbiased by any specific goal-directed task. Early resting state studies showed that electrophysiological oscillatory activity in specific frequency bands supports synchronization processes related to long-range neuronal communication. In turn, experimental evidence from neuroimaging studies revealed that the human brain is organized into multiple large-scale networks of regions showing correlated hemodynamic activity. Multimodal studies have begun to disclose relationships between functional connectivity, as revealed by hemodynamic signals, and underlying electrophysiological processes. Furthermore, functional connectivity studies directly based on electrophysiological signals have recently revealed fundamental information regarding long-range neuronal communication at behaviorally relevant time-scales. The integration of different lines of evidence from hemodynamic and electrophysiological studies suggests that rapid changes of synchronized oscillatory activity in distributed brain networks is relevant for the ongoing maintenance and modulation of the task representations that form the basis of our cognitive flexibility.

Mapping pathological changes in brain structure by combining T1- and T2-weighted MR imaging data

IntroductionA workflow based on the ratio between standardized T1-weighted (T1-w) and T2-weighted (T2-w) MR images has been proposed as a new tool to study brain structure. This approach was previously used to map structural properties in the healthy brain. Here, we evaluate whether the T1-w/T2-w approach can support the assessment of structural impairments in the diseased brain. We use schizophrenia data to demonstrate the potential clinical utility of the technique.MethodsWe analyzed T1-w and T2-w images of 36 schizophrenic patients and 35 age-matched controls. These were collected for the Function Biomedical Informatics Research Network (fBIRN) collaborative project, which had an IRB approval and followed the HIPAA guidelines. We computed T1-w/T2-w images for each individual and compared intensities in schizophrenic and control groups on a voxel-wise basis, as well as in regions of interest (ROIs).ResultsOur results revealed that the T1-w/T2-w image permits to discriminate brain regions showing group-level differences between patients and controls with greater accuracy than conventional T1-w and T2-w images. Both the ROIs and the voxel-wise analysis showed globally reduced gray and white matter values in patients compared to controls. Significantly reduced values were found in regions such as insula, primary auditory cortex, hippocampus, inferior longitudinal fasciculus, and inferior fronto-occipital fasciculus.ConclusionOur findings were consistent with previous meta-analyses in schizophrenia corroborating the hypothesis of a potential “disconnection” syndrome in conjunction with structural alterations in local gray matter regions. Overall, our study suggested that the T1-w/T2-w technique permits to reliably map structural differences between the brains of patients and healthy individuals.

Quantitative Evaluation of Intensity Inhomogeneity Correction Methods for Structural MR Brain Images

The correction of intensity non-uniformity (INU) in magnetic resonance (MR) images is extremely important to ensure both within-subject and across-subject reliability. Here we tackled the problem of objectively comparing INU correction techniques for T1-weighted images, which are the most commonly used in structural brain imaging. We focused our investigations on the methods integrated in widely used software packages for MR data analysis: FreeSurfer, BrainVoyager, SPM and FSL. We used simulated data to assess the INU fields reconstructed by those methods for controlled inhomogeneity magnitudes and noise levels. For each method, we evaluated a wide range of input parameters and defined an enhanced configuration associated with best reconstruction performance. By comparing enhanced and default configurations, we found that the former often provide much more accurate results. Accordingly, we used enhanced configurations for a more objective comparison between methods. For different levels of INU magnitude and noise, SPM and FSL, which integrate INU correction with brain segmentation, generally outperformed FreeSurfer and BrainVoyager, whose methods are exclusively dedicated to INU correction. Nonetheless, accurate INU field reconstructions can be obtained with FreeSurfer on images with low noise and with BrainVoyager for slow and smooth inhomogeneity profiles. Our study may prove helpful for an accurate selection of the INU correction method to be used based on the characteristics of actual MR data.

Detecting Large-Scale Brain Networks Using EEG: Impact of Electrode Density, Head Modeling and Source Localization

Resting state networks (RSNs) in the human brain were recently detected using high-density electroencephalography (hdEEG). This was done by using an advanced analysis workflow to estimate neural signals in the cortex and to assess functional connectivity (FC) between distant cortical regions. FC analyses were conducted either using temporal (tICA) or spatial independent component analysis (sICA). Notably, EEG-RSNs obtained with sICA were very similar to RSNs retrieved with sICA from functional magnetic resonance imaging data. It still remains to be clarified, however, what technological aspects of hdEEG acquisition and analysis primarily influence this correspondence. Here we examined to what extent the detection of EEG-RSN maps by sICA depends on the electrode density, the accuracy of the head model, and the source localization algorithm employed. Our analyses revealed that the collection of EEG data using a high-density montage is crucial for RSN detection by sICA, but also the use of appropriate methods for head modeling and source localization have a substantial effect on RSN reconstruction. Overall, our results confirm the potential of hdEEG for mapping the functional architecture of the human brain, and highlight at the same time the interplay between acquisition technology and innovative solutions in data analysis.

Corrigendum to "Can sliding-window correlations reveal dynamic functional connectivity in resting-state fMRI?" [NeuroImage 127 (2016) 242-256].

a Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain b Department of Health Sciences and Technology, ETH Zurich, Switzerland c Department of Experimental Psychology, University of Oxford, United Kingdom d Department of Physiology of Cognitive Processes, Max Planck Institute for Biological Cybernetics, Tübingen, Germany e Instituci Catalana de la Recerca i Estudis Avanats (ICREA), Universitat Pompeu Fabra, Barcelona, Spain

Intensity Inhomogeneity Correction of Structural MR Images: A Data-Driven Approach to Define Input Algorithm Parameters

Intensity non-uniformity (INU) in magnetic resonance (MR) imaging is a major issue when conducting analyses of brain structural properties. An inaccurate INU correction may result in qualitative and quantitative misinterpretations. Several INU correction methods exist, whose performance largely depend on the specific parameter settings that need to be chosen by the user. Here we addressed the question of how to select the best input parameters for a specific INU correction algorithm. Our investigation was based on the INU correction algorithm implemented in SPM, but this can be in principle extended to any other algorithm requiring the selection of input parameters. We conducted a comprehensive comparison of indirect metrics for the assessment of INU correction performance, namely the coefficient of variation of white matter (CVWM), the coefficient of variation of gray matter (CVGM), and the coefficient of joint variation between white matter and gray matter (CJV). Using simulated MR data, we observed the CJV to be more accurate than CVWM and CVGM, provided that the noise level in the INU-corrected image was controlled by means of spatial smoothing. Based on the CJV, we developed a data-driven approach for selecting INU correction parameters, which could effectively work on actual MR images. To this end, we implemented an enhanced procedure for the definition of white and gray matter masks, based on which the CJV was calculated. Our approach was validated using actual T1-weighted images collected with 1.5 T, 3 T, and 7 T MR scanners. We found that our procedure can reliably assist the selection of valid INU correction algorithm parameters, thereby contributing to an enhanced inhomogeneity correction in MR images.

Online EEG artifact removal for BCI applications by adaptive spatial filtering.

OBJECTIVE The performance of brain-computer interfaces (BCIs) based on electroencephalography (EEG) data strongly depends on the effective attenuation of artifacts that are mixed in the recordings. To address this problem, we have developed a novel online EEG artifact removal method for BCI applications, which combines blind source separation (BSS) and regression (REG) analysis. APPROACH The BSS-REG method relies on the availability of a calibration dataset of limited duration for the initialization of a spatial filter using BSS. Online artifact removal is implemented by dynamically adjusting the spatial filter in the actual experiment, based on a linear regression technique. MAIN RESULTS Our results showed that the BSS-REG method is capable of attenuating different kinds of artifacts, including ocular and muscular, while preserving true neural activity. Thanks to its low computational requirements, BSS-REG can be applied to low-density as well as high-density EEG data. SIGNIFICANCE We argue that BSS-REG may enable the development of novel BCI applications requiring high-density recordings, such as source-based neurofeedback and closed-loop neuromodulation.