A proof-of-principle study of multi-site real-time functional imaging at 3T and 7T: Implementation and validation

Using causal methods to map symptoms to brain circuits in neurodevelopment disorders: moving from identifying correlates to developing treatments

A wide variety of model systems and experimental techniques can provide insight into the structure and function of the human brain in typical development and in neurodevelopmental disorders. Unfortunately, this work, whether based on manipulation of animal models or observational and correlational methods in humans, has a high attrition rate in translating scientific discovery into practicable treatments and therapies for neurodevelopmental disorders.With new computational and neuromodulatory approaches to interrogating brain networks, opportunities exist for "bedside-to bedside-translation" with a potentially shorter path to therapeutic options. Specifically, methods like lesion network mapping can identify brain networks involved in the generation of complex symptomatology, both from acute onset lesion-related symptoms and from focal developmental anomalies. Traditional neuroimaging can examine the generalizability of these findings to idiopathic populations, while non-invasive neuromodulation techniques such as transcranial magnetic stimulation provide the ability to do targeted activation or inhibition of these specific brain regions and networks. In parallel, real-time functional MRI neurofeedback also allow for endogenous neuromodulation of specific targets that may be out of reach for transcranial exogenous methods.Discovery of novel neuroanatomical circuits for transdiagnostic symptoms and neuroimaging-based endophenotypes may now be feasible for neurodevelopmental disorders using data from cohorts with focal brain anomalies. These novel circuits, after validation in large-scale highly characterized research cohorts and tested prospectively using noninvasive neuromodulation and neurofeedback techniques, may represent a new pathway for symptom-based targeted therapy.

A Guide to Literature Informed Decisions in the Design of Real Time fMRI Neurofeedback Studies: A Systematic Review

Background: Although biofeedback using electrophysiology has been explored extensively, the approach of using neurofeedback corresponding to hemodynamic response is a relatively young field. Real time functional magnetic resonance imaging-based neurofeedback (rt-fMRI-NF) uses sensory feedback to operantly reinforce patterns of neural response. It can be used, for example, to alter visual perception, increase brain connectivity, and reduce depression symptoms. Within recent years, interest in rt-fMRI-NF in both research and clinical contexts has expanded considerably. As such, building a consensus regarding best practices is of great value. Objective: This systematic review is designed to describe and evaluate the variations in methodology used in previous rt-fMRI-NF studies to provide recommendations for rt-fMRI-NF study designs that are mostly likely to elicit reproducible and consistent effects of neurofeedback. Methods: We conducted a database search for fMRI neurofeedback papers published prior to September 26th, 2019. Of 558 studies identified, 146 met criteria for inclusion. The following information was collected from each study: sample size and type, task used, neurofeedback calculation, regulation procedure, feedback, whether feedback was explicitly related to changing brain activity, feedback timing, control group for active neurofeedback, how many runs and sessions of neurofeedback, if a follow-up was conducted, and the results of neurofeedback training. Results: rt-fMRI-NF is typically upregulation practice based on hemodynamic response from a specific region of the brain presented using a continually updating thermometer display. Most rt-fMRI-NF studies are conducted in healthy samples and half evaluate its effect on immediate changes in behavior or affect. The most popular control group method is to provide sham signal from another region; however, many studies do not compare use a comparison group. Conclusions: We make several suggestions for designs of future rt-fMRI-NF studies. Researchers should use feedback calculation methods that consider neural response across regions (i.e., SVM or connectivity), which should be conveyed as intermittent, auditory feedback. Participants should be given explicit instructions and should be assessed on individual differences. Future rt-fMRI-NF studies should use clinical samples; effectiveness of rt-fMRI-NF should be evaluated on clinical/behavioral outcomes at follow-up time points in comparison to both a sham and no feedback control group.

No time for drifting: Comparing performance and applicability of signal detrending algorithms for real-time fMRI

As a consequence of recent technological advances in the field of functional magnetic resonance imaging (fMRI), results can now be made available in real-time. This allows for novel applications such as online quality assurance of the acquisition, intra-operative fMRI, brain-computer-interfaces, and neurofeedback. To that aim, signal processing algorithms for real-time fMRI must reliably correct signal contaminations due to physiological noise, head motion, and scanner drift. The aim of this study was to compare performance of the commonly used online detrending algorithms exponential moving average (EMA), incremental general linear model (iGLM) and sliding window iGLM (iGLMwindow). For comparison, we also included offline detrending algorithms (i.e., MATLAB's and SPM8's native detrending functions). Additionally, we optimized the EMA control parameter, by assessing the algorithm's performance on a simulated data set with an exhaustive set of realistic experimental design parameters. First, we optimized the free parameters of the online and offline detrending algorithms. Next, using simulated data, we systematically compared the performance of the algorithms with respect to varying levels of Gaussian and colored noise, linear and non-linear drifts, spikes, and step function artifacts. Additionally, using in vivo data from an actual rt-fMRI experiment, we validated our results in a post hoc offline comparison of the different detrending algorithms. Quantitative measures show that all algorithms perform well, even though they are differently affected by the different artifact types. The iGLM approach outperforms the other online algorithms and achieves online detrending performance that is as good as that of offline procedures. These results may guide developers and users of real-time fMRI analyses tools to best account for the problem of signal drifts in real-time fMRI.

Cassava geminivirus agroclones for virus-induced gene silencing in cassava leaves and roots

AIM

We report the construction of a Virus-Induced Gene Silencing (VIGS) vector and an agroinoculation protocol for gene silencing in cassava (Manihot esculenta Crantz) leaves and roots. The African cassava mosaic virus isolate from Nigeria (ACMV-[NOg]), which was initially cloned in a binary vector for agroinoculation assays, was modified for application as VIGS vector. The functionality of the VIGS vector was validated in Nicotiana benthamiana and subsequently applied in wild-type and transgenic cassava plants expressing the uidA gene under the control of the CaMV 35S promoter in order to facilitate the visualization of gene silencing in root tissues. VIGS vectors were targeted to the Mg2+-chelatase gene in wild type plants and both the coding and promoter sequences of the 35S::uidA transgene in transgenic plants to induce silencing. We established an efficient agro-inoculation method with the hyper-virulent Agrobacterium tumefaciens strain AGL1, which allows high virus infection rates. The method can be used as a low-cost and rapid high-throughput evaluation of gene function in cassava leaves, fibrous roots and storage roots.

BACKGROUND

VIGS is a powerful tool to trigger transient sequence-specific gene silencing in planta. Gene silencing in different organs of cassava plants, including leaves, fibrous and storage roots, is useful for the analysis of gene function.

RESULTS

We developed an African cassava mosaic virus-based VIGS vector as well as a rapid and efficient agro-inoculation protocol to inoculate cassava plants. The VIGS vector was validated by targeting endogenous genes from Nicotiana benthamiana and cassava as well as the uidA marker gene in transgenic cassava for visualization of gene silencing in cassava leaves and roots.

CONCLUSIONS

The African cassava mosaic virus-based VIGS vector allows efficient and cost-effective inoculation of cassava for high-throughput analysis of gene function in cassava leaves and roots.

OpenNFT: An open-source Python/Matlab framework for real-time fMRI neurofeedback training based on activity, connectivity and multivariate pattern analysis

Neurofeedback based on real-time functional magnetic resonance imaging (rt-fMRI) is a novel and rapidly developing research field. It allows for training of voluntary control over localized brain activity and connectivity and has demonstrated promising clinical applications. Because of the rapid technical developments of MRI techniques and the availability of high-performance computing, new methodological advances in rt-fMRI neurofeedback become possible. Here we outline the core components of a novel open-source neurofeedback framework, termed Open NeuroFeedback Training (OpenNFT), which efficiently integrates these new developments. This framework is implemented using Python and Matlab source code to allow for diverse functionality, high modularity, and rapid extendibility of the software depending on the user's needs. In addition, it provides an easy interface to the functionality of Statistical Parametric Mapping (SPM) that is also open-source and one of the most widely used fMRI data analysis software. We demonstrate the functionality of our new framework by describing case studies that include neurofeedback protocols based on brain activity levels, effective connectivity models, and pattern classification approaches. This open-source initiative provides a suitable framework to actively engage in the development of novel neurofeedback approaches, so that local methodological developments can be easily made accessible to a wider range of users.

Training Efficiency and Transfer Success in an Extended Real-Time Functional MRI Neurofeedback Training of the Somatomotor Cortex of Healthy Subjects

This study investigated the level of self-regulation of the somatomotor cortices (SMCs) attained by an extended functional magnetic resonance imaging (fMRI) neurofeedback training. Sixteen healthy subjects performed 12 real-time functional magnetic resonance imaging neurofeedback training sessions within 4 weeks, involving motor imagery of the dominant right as well as the non-dominant left hand. Target regions of interests in the SMC were individually localized prior to the training by overt finger movements. The feedback signal (FS) was defined as the difference between fMRI activation in the contra- and ipsilateral SMC and visually presented to the subjects. Training efficiency was determined by an off-line general linear model analysis determining the fMRI percent signal changes in the SMC target areas accomplished during the neurofeedback training. Transfer success was assessed by comparing the pre- and post-training transfer task, i.e., the neurofeedback paradigm without the presentation of the FS. Group results show a distinct increase in feedback performance (FP) in the transfer task for the trained group compared to a matched untrained control group, as well as an increase in the time course of the training, indicating an efficient training and a successful transfer. Individual analysis revealed that the training efficiency was not only highly correlated to the transfer success but also predictive. Trainings with at least 12 efficient training runs were associated with a successful transfer outcome. A group analysis of the hemispheric contributions to the FP showed that it is mainly driven by increased fMRI activation in the contralateral SMC, although some individuals relied on ipsilateral deactivation. Training and transfer results showed no difference between left- and right-hand imagery, with a slight indication of more ipsilateral deactivation in the early right-hand trainings.