Learning and comparing functional connectomes across subjects

Functional connectomes capture brain interactions via synchronized fluctuations in the functional magnetic resonance imaging signal. If measured during rest, they map the intrinsic functional architecture of the brain. With task-driven experiments they represent integration mechanisms between specialized brain areas. Analyzing their variability across subjects and conditions can reveal markers of brain pathologies and mechanisms underlying cognition. Methods of estimating functional connectomes from the imaging signal have undergone rapid developments and the literature is full of diverse strategies for comparing them. This review aims to clarify links across functional-connectivity methods as well as to expose different steps to perform a group study of functional connectomes

The neural mechanisms of mindfulness-based pain relief: a functional magnetic resonance imaging-based review and primer.

The advent of neuroimaging methodologies, such as functional magnetic resonance imaging (fMRI), has significantly advanced our understanding of the neurophysiological processes supporting a wide spectrum of mind-body approaches to treat pain. A promising self-regulatory practice, mindfulness meditation, reliably alleviates experimentally induced and clinical pain. Yet, the neural mechanisms supporting mindfulness-based pain relief remain poorly characterized. The present review delineates evidence from a spectrum of fMRI studies showing that the neural mechanisms supporting mindfulness-induced pain attenuation differ across varying levels of meditative experience. After brief mindfulness-based mental training (ie, less than 10 hours of practice), mindfulness-based pain relief is associated with higher order (orbitofrontal cortex and rostral anterior cingulate cortex) regulation of low-level nociceptive neural targets (thalamus and primary somatosensory cortex), suggesting an engagement of unique, reappraisal mechanisms. By contrast, mindfulness-based pain relief after extensive training (greater than 1000 hours of practice) is associated with deactivation of prefrontal and greater activation of somatosensory cortical regions, demonstrating an ability to reduce appraisals of arising sensory events. We also describe recent findings showing that higher levels of dispositional mindfulness, in meditation-naïve individuals, are associated with lower pain and greater deactivation of the posterior cingulate cortex, a neural mechanism implicated in self-referential processes. A brief fMRI primer is presented describing appropriate steps and considerations to conduct studies combining mindfulness, pain, and fMRI. We postulate that the identification of the active analgesic neural substrates involved in mindfulness can be used to inform the development and optimization of behavioral therapies to specifically target pain, an important consideration for the ongoing opioid and chronic pain epidemic

Retrieving the hemodynamic response function in resting state fMRI: methodology and application

In this paper we present a procedure to retrieve the hemodynamic response function (HRF) from resting state functional magnetic resonance imaging (fMRI) data. The fundamentals of the procedures are further validated by considering simultaneous electroencephalographic (EEG) recordings. The typical HRF shape at rest for a group of healthy subject is presented. Then we present the modifications to the shape of the HRF at rest following two physiological modulations: eyes open versus eyes closed and propofol-induced modulations of consciousness

Advances in image acquisition and filtering for MRI neuroimaging at 7 tesla

Performing magnetic resonance imaging at high magnetic field strength promises many improvements over low fields that are of direct benefit in functional neuroimaging. This includes the possibility of improved signal-to-noise levels, and increased BOLD functional contrast and spatial specificity. However, human MRI at 7T and above suffers from unique engineering challenges that limit the achievable gains. In this thesis, three technological developments are introduced, all of which address separate issues associated with functional magnetic resonance neuroimaging at very high magnetic field strengths. First, the image homogeneity problem is addressed by investigating methods of RF shimming — modifying the excitation portion of the MRI experiment for use with multi-channel RF coils. It is demonstrated that in 2D MRI experiments, shimming on a slice-by slice basis allows utilization of an extra degree of freedom available from the slice dimension, resulting in significant gains in image homogeneity and reduced RF power requirements. After acceptable images are available, we move to address complications of high field imaging that manifest in the fMRI time series. In the second paper, the increased physiological noise present in BOLD time series at high field is addressed with a unique data-driven noise regressor scheme based upon information in the phase component of the MRI signal. It is demonstrated that this method identifies and removes a significant portion of physiological signals, and performs as good or better than other popular data driven methods that use only the magnitude signal information. Lastly, the BOLD phase signal is again leveraged to address the confounding role of veins in resting state BOLD fMRI experiments. The phase regressor technique (previously developed by Dr. Menon) is modified and applied to resting state fMRI to remove macro vascular contributions in the datasets, leading to changes in spatial extent and connectivity of common resting state networks on single subjects and at the group level

Methods for cleaning the BOLD fMRI signal

Available online 9 December 2016 http://www.sciencedirect.com/science/article/pii/S1053811916307418?via%3Dihubhttp://www.sciencedirect.com/science/article/pii/S1053811916307418?via%3DihubBlood oxygen-level-dependent functional magnetic resonance imaging (BOLD fMRI) has rapidly become a popular technique for the investigation of brain function in healthy individuals, patients as well as in animal studies. However, the BOLD signal arises from a complex mixture of neuronal, metabolic and vascular processes, being therefore an indirect measure of neuronal activity, which is further severely corrupted by multiple non-neuronal fluctuations of instrumental, physiological or subject-specific origin. This review aims to provide a comprehensive summary of existing methods for cleaning the BOLD fMRI signal. The description is given from a methodological point of view, focusing on the operation of the different techniques in addition to pointing out the advantages and limitations in their application. Since motion-related and physiological noise fluctuations are two of the main noise components of the signal, techniques targeting their removal are primarily addressed, including both data-driven approaches and using external recordings. Data-driven approaches, which are less specific in the assumed model and can simultaneously reduce multiple noise fluctuations, are mainly based on data decomposition techniques such as principal and independent component analysis. Importantly, the usefulness of strategies that benefit from the information available in the phase component of the signal, or in multiple signal echoes is also highlighted. The use of global signal regression for denoising is also addressed. Finally, practical recommendations regarding the optimization of the preprocessing pipeline for the purpose of denoising and future venues of research are indicated. Through the review, we summarize the importance of signal denoising as an essential step in the analysis pipeline of task-based and resting state fMRI studies.This work was supported by the Spanish Ministry of Economy and Competitiveness [Grant PSI 2013–42343 Neuroimagen Multimodal], the Severo Ochoa Programme for Centres/Units of Excellence in R & D [SEV-2015-490], and the research and writing of the paper were supported by the NIMH and NINDS Intramural Research Programs (ZICMH002888) of the NIH/HHS

A novel method of combining blood oxygenation and blood flow sensitive magnetic resonance imaging techniques to measure the cerebral blood flow and oxygen metabolism responses to an unknown neural stimulus.

Simultaneous implementation of magnetic resonance imaging methods for Arterial Spin Labeling (ASL) and Blood Oxygenation Level Dependent (BOLD) imaging makes it possible to quantitatively measure the changes in cerebral blood flow (CBF) and cerebral oxygen metabolism (CMRO(2)) that occur in response to neural stimuli. To date, however, the range of neural stimuli amenable to quantitative analysis is limited to those that may be presented in a simple block or event related design such that measurements may be repeated and averaged to improve precision. Here we examined the feasibility of using the relationship between cerebral blood flow and the BOLD signal to improve dynamic estimates of blood flow fluctuations as well as to estimate metabolic-hemodynamic coupling under conditions where a stimulus pattern is unknown. We found that by combining the information contained in simultaneously acquired BOLD and ASL signals through a method we term BOLD Constrained Perfusion (BCP) estimation, we could significantly improve the precision of our estimates of the hemodynamic response to a visual stimulus and, under the conditions of a calibrated BOLD experiment, accurately determine the ratio of the oxygen metabolic response to the hemodynamic response. Importantly we were able to accomplish this without utilizing a priori knowledge of the temporal nature of the neural stimulus, suggesting that BOLD Constrained Perfusion estimation may make it feasible to quantitatively study the cerebral metabolic and hemodynamic responses to more natural stimuli that cannot be easily repeated or averaged

An fMRI dataset in response to “The Grand Budapest Hotel”, a socially-rich, naturalistic movie

Naturalistic stimuli evoke strong, consistent, and information-rich patterns of brain activity, and engage large extents of the human brain. They allow researchers to compare highly similar brain responses across subjects, and to study how complex representations are encoded in brain activity. Here, we describe and share a dataset where 25 subjects watched part of the feature film “The Grand Budapest Hotel” by Wes Anderson. The movie has a large cast with many famous actors. Throughout the story, the camera shots highlight faces and expressions, which are fundamental to understand the complex narrative of the movie. This movie was chosen to sample brain activity specifically related to social interactions and face processing. This dataset provides researchers with fMRI data that can be used to explore social cognitive processes and face processing, adding to the existing neuroimaging datasets that sample brain activity with naturalistic movies