A review of fMRI simulation studies

Simulation studies that validate statistical techniques for fMRI data are challenging due to the complexity of the data. Therefore, it is not surprising that no common data generating process is available (i.e. several models can be found to model BOLD activation and noise). Based on a literature search, a database of simulation studies was compiled. The information in this database was analysed and critically evaluated focusing on the parameters in the simulation design, the adopted model to generate fMRI data, and on how the simulation studies are reported. Our literature analysis demonstrates that many fMRI simulation studies do not report a thorough experimental design and almost consistently ignore crucial knowledge on how fMRI data are acquired. Advice is provided on how the quality of fMRI simulation studies can be improved

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

Transcranial Focused Ultrasound to the Right Prefrontal Cortex Improves Mood and Alters Functional Connectivity in Humans

Transcranial focused ultrasound (tFUS) is an emerging method for non-invasive neuromodulation akin to transcranial magnetic stimulation (TMS) and transcranial direct current stimulation (tDCS). tFUS offers several advantages over electromagnetic methods including high spatial resolution and the ability to reach deep brain targets. Here we describe two experiments assessing whether tFUS could modulate mood in healthy human volunteers by targeting the right inferior frontal gyrus (rIFG), an area implicated in mood and emotional regulation. In a randomized, placebo-controlled, double-blind study, participants received 30 s of 500 kHz tFUS or a placebo control. Visual Analog Mood Scales (VAMS) assessed mood four times within an hour (baseline and three times after tFUS). Participants who received tFUS reported an overall increase in Global Affect (GA), an aggregate score from the VAMS scale, indicating a positive shift in mood. Experiment 2 examined resting-state functional (FC) connectivity using functional magnetic resonance imaging (fMRI) following 2 min of 500 kHz tFUS at the rIFG. As in Experiment 1, tFUS enhanced self-reported mood states and also decreased FC in resting state networks related to emotion and mood regulation. These results suggest that tFUS can be used to modulate mood and emotional regulation networks in the prefrontal cortex

Towards a better understanding of the impact of heart rate on the BOLD signal: a new method for physiological noise correction and its applications

Functional magnetic resonance imaging (fMRI) based on blood oxygenation level-dependent (BOLD) contrast allows non-invasive examination of brain activity and is widely used in the neuroimaging field. The BOLD contrast mechanism reflects hemodynamic changes resulting from a complex interplay of blood flow, blood volume, and oxygen consumption. Heart rate (HR) variations are the most intriguing and less understood physiological processes affecting the BOLD signal, as they are the result of a wide variety of interacting factors. The use of the response function that best models HR-induced signal changes, called cardiac response function (CRF), is an effective method to reduce HR noise in fMRI. However, current models of physiological noise correction based on CRF, i.e. canonical and individual, either do not take into account variations in HR between subjects, and are thus inadequate for cohorts with varying HR, or require time-consuming quality control of individual physiological recordings and derived CRFs. By analyzing a large cohort of healthy individuals, the results presented in this thesis show that different HRs influence the BOLD signal and their corresponding spectra differently. A further finding is that HR plays an essential role in determining the shape of the CRF. Slower HRs produce a smoothed CRF with a single well-defined maximum, while faster HRs cause a second maximum. Taking advantage of this dependence of the CRF on HR, a novel method is proposed to model HR-induced fluctuations in the BOLD signal more accurately than current approaches of physiological noise correction. This method, called HR-based CRF, consists of two CRFs: one for HRs below 68 bpm and one for HRs above this value. HR-based CRFs can be directly applied to the fMRI data without the time-consuming task of deriving a CRF for each subject while accounting for inter-subject variability in HR response

Respiratory, postural and spatio-kinetic motor stabilization, internal models, top-down timed motor coordination and expanded cerebello-cerebral circuitry: a review

Human dexterity, bipedality, and song/speech vocalization in Homo are reviewed within a motor evolution perspective in regard to 

(i) brain expansion in cerebello-cerebral circuitry, 
(ii) enhanced predictive internal modeling of body kinematics, body kinetics and action organization, 
(iii) motor mastery due to prolonged practice, 
(iv) task-determined top-down, and accurately timed feedforward motor adjustment of multiple-body/artifact elements, and 
(v) reduction in automatic preflex/spinal reflex mechanisms that would otherwise restrict such top-down processes. 

Dual-task interference and developmental neuroimaging research argues that such internal modeling based motor capabilities are concomitant with the evolution of 
(vi) enhanced attentional, executive function and other high-level cognitive processes, and that 
(vii) these provide dexterity, bipedality and vocalization with effector nonspecific neural resources. 

The possibility is also raised that such neural resources could 
(viii) underlie human internal model based nonmotor cognitions. 
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Streamlining the Design and Use of Array Coils for In Vivo Magnetic Resonance Imaging of Small Animals

Small-animal models such as rodents and non-human primates play an important pre-clinical role in the study of human disease, with particular application to cancer, cardiovascular, and neuroscience models. To study these animal models, magnetic resonance imaging (MRI) is advantageous as a non-invasive technique due to its versatile contrast mechanisms, large and flexible field of view, and straightforward comparison/translation to human applications. However, signal-to-noise ratio (SNR) limits the practicality of achieving the high-resolution necessary to image the smaller features of animals in an amount of time suitable for in vivo animal MRI. In human MRI, it is standard to achieve an increase in SNR through the use of array coils; however, the design, construction, and use of array coils for animal imaging remains challenging due to copper-loss related issues from small array elements and design complexities of incorporating multiple elements and associated array hardware in a limited space. In this work, a streamlined strategy for animal coil array design, construction, and use is presented and the use for multiple animal models is demonstrated. New matching network circuits, materials, assembly techniques, body-restraining systems and integrated mechanical designs are demonstrated for streamlining high-resolution MRI of both anesthetized and awake animals. The increased SNR achieved with the arrays is shown to enable high-resolution in vivo imaging of mice and common marmosets with a reduced time for experimental setup

Removing motion and physiological artifacts from intrinsic BOLD fluctuations using short echo data

AbstractDiffering noise variance across study populations has been shown to cause artifactual group differences in functional connectivity measures. In this study, we investigate the use of short echo time functional MRI data to correct for these noise sources in blood oxygenation level dependent (BOLD)-weighted time series. A dual‐echo sequence was used to simultaneously acquire data at both a short (TE=3.3ms) and a BOLD-weighted (TE=35ms) echo time. This approach is effectively “free,” using dead-time in the pulse sequence to collect an additional echo without affecting overall scan time or temporal resolution. The proposed correction method uses voxelwise regression of the short TE data from the BOLD-weighted data to remove noise variance. In addition to a typical resting state scan, non-compliant behavior associated with patient groups was simulated via increased head motion or physiological fluctuations in 10 subjects. Short TE data showed significant correlations with the traditional motion-related and physiological noise regressors used in current connectivity analyses. Following traditional preprocessing, the extent of significant additional variance explained by the short TE data regressors was significantly correlated with the average head motion across the scan in the resting data (r2=0.93, p<0.0001). The reduction in data variance following the inclusion of short TE regressors was also correlated with scan head motion (r2=0.48, p=0.027). Task-related data were used to demonstrate the effects of the short TE correction on BOLD activation time series with known temporal structure; the size and strength of the activation were significantly decreased, but it is not clear whether this reflects BOLD contamination in the short TE data or correlated changes in blood volume. Finally, functional connectivity maps of the default mode network were constructed using a seed correlation approach. The effects of short TE correction and low-pass filtering on the resulting correlations maps were compared. Results suggest that short TE correction more accurately differentiates artifactual correlations from the correlations of interest in conditions of amplified noise

Motor imagery and action observation: cognitive tools for rehabilitation

Rehabilitation, for a large part may be seen as a learning process where old skills have to be re-acquired and new ones have to be learned on the basis of practice. Active exercising creates a flow of sensory (afferent) information. It is known that motor recovery and motor learning have many aspects in common. Both are largely based on response-produced sensory information. In the present article it is asked whether active physical exercise is always necessary for creating this sensory flow. Numerous studies have indicated that motor imagery may result in the same plastic changes in the motor system as actual physical practice. Motor imagery is the mental execution of a movement without any overt movement or without any peripheral (muscle) activation. It has been shown that motor imagery leads to the activation of the same brain areas as actual movement. The present article discusses the role that motor imagery may play in neurological rehabilitation. Furthermore, it will be discussed to what extent the observation of a movement performed by another subject may play a similar role in learning. It is concluded that, although the clinical evidence is still meager, the use of motor imagery in neurological rehabilitation may be defended on theoretical grounds and on the basis of the results of experimental studies with healthy subjects

High-density diffuse optical tomography for imaging human brain function

This review describes the unique opportunities and challenges for noninvasive optical mapping of human brain function. Diffuse optical methods offer safe, portable, and radiation free alternatives to traditional technologies like positron emission tomography or functional magnetic resonance imaging (fMRI). Recent developments in high-density diffuse optical tomography (HD-DOT) have demonstrated capabilities for mapping human cortical brain function over an extended field of view with image quality approaching that of fMRI. In this review, we cover fundamental principles of the diffusion of near infrared light in biological tissue. We discuss the challenges involved in the HD-DOT system design and implementation that must be overcome to acquire the signal-to-noise necessary to measure and locate brain function at the depth of the cortex. We discuss strategies for validation of the sensitivity, specificity, and reliability of HD-DOT acquired maps of cortical brain function. We then provide a brief overview of some clinical applications of HD-DOT. Though diffuse optical measurements of neurophysiology have existed for several decades, tremendous opportunity remains to advance optical imaging of brain function to address a crucial niche in basic and clinical neuroscience: that of bedside and minimally constrained high fidelity imaging of brain function

Intrinsic Inter-Subject Variability in Functional Neuroimaging: Verification Using Blind Source Separation Features

The holy grail of brain imaging is the identification of a biomarker, which can identify an abnormality that can be used to diagnose disease and track the effectiveness of treatment and disease progression. Typically approaches that search for biomarkers start by identifying mean differences between groups of patients and healthy controls. However, combining data from different subjects and groups to be able to make meaningful inferences is not trivial. The structure of the brain in each individual is unique in size and shape as well as in the relative location of anatomical landmarks (e.g. sulci and gyri). When looking for mean differences in functional images, this issue is exacerbated by the presence of variability in functional localization, i.e. variability in the location of functional regions in the brain. This is notably an important reason to focus on looking for inter-individual differences or variability. Inter-subject variability in neuroimaging experiments is often viewed as noise. The analyses are setup in a manner to ignore this variability assuming that a global spatial normalization brings the data into the same space. Nonetheless, functional activation patterns can be impacted by variability in multiple ways for e.g., there could be spatial variability of the maps or variability in the spectral composition of the timecourses or variability in the connectivity between the activation patterns identified. The overarching problem this thesis seeks to contribute to, is seeking improved measures to quantify biologically significant spatial, spectral and connectivity based variability and to identify associated cognitive or behavioral differences in the distribution of brain networks. We have successfully shown that different (spatial and spectral) measures of variability in blind source separated functional activation patterns underline previously unexplained characteristics that help in discerning schizophrenia patients from healthy controls. Additionally, we show that variance measures in dynamic connectivity between networks in healthy controls can justify relationship between connectivity patterns and executive functioning abilities