Matthew S. Sherwood

Enhanced control of dorsolateral prefrontal cortex neurophysiology with real-time functional magnetic resonance imaging (rt-fMRI) neurofeedback training and working memory practice.

Real-time functional magnetic resonance imaging (rt-fMRI) neurofeedback can be used to train localized, conscious regulation of blood oxygen level-dependent (BOLD) signals. As a therapeutic technique, rt-fMRI neurofeedback reduces the symptoms of a variety of neurologic disorders. To date, few studies have investigated the use of self-regulation training using rt-fMRI neurofeedback to enhance cognitive performance. This work investigates the utility of rt-fMRI neurofeedback as a tool to enhance human cognition by training healthy individuals to consciously control activity in the left dorsolateral prefrontal cortex (DLPFC). A cohort of 18 healthy participants in the experimental group underwent rt-fMRI neurofeedback from the left DLPFC in five training sessions across two weeks while 7 participants in the control group underwent similar training outside the MRI and without rt-fMRI neurofeedback. Working memory (WM) performance was evaluated on two testing days separated by the five rt-fMRI neurofeedback sessions using two computerized tests. We investigated the ability to control the BOLD signal across training sessions and WM performance across the two testing days. The group with rt-fMRI neurofeedback demonstrated a significant increase in the ability to self-regulate the BOLD signal in the left DLPFC across sessions. WM performance showed differential improvement between testing days one and two across the groups with the highest increases observed in the rt-fMRI neurofeedback group. These results provide evidence that individuals can quickly gain the ability to consciously control the left DLPFC, and this training results in improvements of WM performance beyond that of training alone.

Neurocognitive Correlates of Learning in a Visual Object Recognition Task

Preliminary results of a longitudinal study aimed at understanding the neurocognitive correlates of learning in a visual object recognition task are reported. The experimental task used real-world novel stimuli, whereas the control task used real-world familiar stimuli. Participants practiced the tasks over 10 weeks and reached a high level of accuracy. Brain imaging data was acquired in weeks 2, 6, and 10 and eye-tracking data was acquired in the other seven weeks. Quantitative and qualitative changes in brain activity were observed over the course of learning and skill acquisition. Generally, in the experimental task, brain activity increased at week 6 and decreased at week 10, whereas in the control task, brain activity decreased at week 6 and further decreased at week 10 compared to week 2. New clusters of brain activity emerged at week 6 in the experimental task. Eye-fixation and pupil-dilation data showed that fast learners tend to inspect the stimuli more thoroughly even after a response was given. These results are used to inform the development of computational cognitive models of visual object recognition tasks.

Volitional down-regulation of the primary auditory cortex via directed attention mediated by real-time fMRI neurofeedback

The present work assessed the efficacy of training volitional down-regulation of the primary auditory cortex (A1) based on real-time functional magnetic resonance imaging neurofeedback (fMRI-NFT). A1 has been shown to be hyperactive in chronic tinnitus patients, and has been implicated as a potential source for the tinnitus percept. 27 healthy volunteers with normal hearing underwent 5 fMRI-NFT sessions: 18 received real neurofeedback and 9 sham neurofeedback. Each session was composed of a simple auditory fMRI followed by 2 runs of A1 fMRI-NFT. The auditory fMRI alternated periods of no auditory with periods of white noise stimulation at 90 dB. A1 activity, defined from a region using the activity during the preceding auditory run, was continuously updated during fMRI-NFT using a simple bar plot, and was accompanied by white noise (90 dB) stimulation for the duration of the scan. Each fMRI-NFT run alternated “relax” periods with “lower” periods. Subjects were instructed to watch the bar during the relax condition and actively reduce the bar by decreasing A1 activation during the lower condition. Average A1 de-activation, representative of the ability to volitionally down-regulate A1, was extracted from each fMRI-NFT run. A1 de-activation was found to increase significantly across training and to be higher in those receiving real neurofeedback. A1 de-activation in sessions 2 and 5 were found to be significantly greater than session 1 in only the group receiving real neurofeedback. The most successful subjects reportedly adopted mindfulness tasks associated with directed attention. For the first time, fMRI-NFT has been applied to teach volitional control of A1 de-activation magnitude over more than 1 session. These are important findings for therapeutic development as the magnitude of A1 activity is altered in tinnitus populations and it is unlikely a single fMRI-NFT session will reverse the effects of tinnitus.

Repetitive Transcranial Electrical Stimulation Induces Quantified Changes in Resting Cerebral Perfusion Measured from Arterial Spin Labeling

The use of transcranial electrical stimulation (TES) as a method to augment neural activity has increased in popularity in the last decade and a half. The specific application of TES to the left prefrontal cortex has been shown to produce broad cognitive effects; however, the neural mechanisms underlying these effects remain unknown. In this work, we evaluated the effect of repetitive TES on cerebral perfusion. Stimulation was applied to the left prefrontal cortex on three consecutive days, and resting cerebral perfusion was quantified before and after stimulation using arterial spin labeling. Perfusion was found to decrease significantly more in a matched sham stimulation group than in a group receiving active stimulation across many areas of the brain. These changes were found to originate in the locus coeruleus and were broadly distributed in the neocortex. The changes in the neocortex may be a direct result of the stimulation or an indirect result via the changes in the noradrenergic system produced from the altered activity of the locus coeruleus. These findings indicate that anodal left prefrontal stimulation alters the activity of the locus coeruleus, and this altered activity may excite the noradrenergic system producing the broad behavioral effects that have been reported.

Single Session Low Frequency Left Dorsolateral Prefrontal Transcranial Magnetic Stimulation Changes Neurometabolite Relationships in Healthy Humans

Background: Dorsolateral prefrontal cortex (DLPFC) low frequency repetitive transcranial magnetic stimulation (LF-rTMS) has shown promise as a treatment and investigative tool in the medical and research communities. Researchers have made significant progress elucidating DLPFC LF-rTMS effects—primarily in individuals with psychiatric disorders. However, more efforts investigating underlying molecular changes and establishing links to functional and behavioral outcomes in healthy humans are needed. Objective: We aimed to quantify neuromolecular changes and relate these to functional changes following a single session of DLPFC LF-rTMS in healthy participants. Methods: Eleven participants received sham-controlled neuronavigated 1 Hz rTMS to the region most activated by a 7-letter Sternberg working memory task (SWMT) within the left DLPFC. We quantified SWMT performance, functional magnetic resonance activation and proton Magnetic resonance spectroscopy (MRS) neurometabolite measure changes before and after stimulation. Results: A single LF-rTMS session was not sufficient to change DLPFC neurometabolite levels and these changes did not correlate with DLPFC activation changes. Real rTMS, however, significantly altered neurometabolite correlations (compared to sham rTMS), both with baseline levels and between the metabolites themselves. Additionally, real rTMS was associated with diminished reaction time (RT) performance improvements and increased activation within the motor, somatosensory and lateral occipital cortices. Conclusion: These results show that a single session of LF-rTMS is sufficient to influence metabolite relationships and causes widespread activation in healthy humans. Investigating correlational relationships may provide insight into mechanisms underlying LF-rTMS.

A Protocol for the Administration of Real-Time fMRI Neurofeedback Training

Neurologic disorders are characterized by abnormal cellular-, molecular-, and circuit-level functions in the brain. New methods to induce and control neuroplastic processes and correct abnormal function, or even shift functions from damaged tissue to physiologically healthy brain regions, hold the potential to dramatically improve overall health. Of the current neuroplastic interventions in development, neurofeedback training (NFT) from functional Magnetic Resonance Imaging (fMRI) has the advantages of being completely non-invasive, non-pharmacologic, and spatially localized to target brain regions, as well as having no known side effects. Furthermore, NFT techniques, initially developed using fMRI, can often be translated to exercises that can be performed outside of the scanner without the aid of medical professionals or sophisticated medical equipment. In fMRI NFT, the fMRI signal is measured from specific regions of the brain, processed, and presented to the participant in real-time. Through training, self-directed mental processing techniques, that regulate this signal and its underlying neurophysiologic correlates, are developed. FMRI NFT has been used to train volitional control over a wide range of brain regions with implications for several different cognitive, behavioral, and motor systems. Additionally, fMRI NFT has shown promise in a broad range of applications such as the treatment of neurologic disorders and the augmentation of baseline human performance. In this article, we present an fMRI NFT protocol developed at our institution for modulation of both healthy and abnormal brain function, as well as examples of using the method to target both cognitive and auditory regions of the brain.

Development of a Smart Tutor for a Visual-Aircraft Recognition Task

The goal of this project is to design an intelligent tutor to teach visual aircraft recognition (VACR) skills to military population. Under extreme cognitive demand, soldiers must learn to rapidly recognize and identify the aircraft prior to engagement. The goal of the smart tutor is to train the trainees to look at specific features (called Wings, Engine, Fuselage and Tail- WEFT) of the aircraft that will help them proceduralize the skill of aircraft recognition.

Automated and nonbiased regional quantification of functional neuroimaging data

PURPOSE In the quantification of functional neuroimaging data, region-of-interest (ROI) analysis can be used to assess a variety of properties of the activation signal, but taken alone these properties are susceptible to noise and may fail to accurately describe overall regional involvement. Here, the authors present and evaluate an automated method for quantification and localization of functional neuroimaging data that combines multiple properties of the activation signal to generate rank-order lists of regional activation results. METHODS The proposed automated quantification method, referred to as neuroimaging results decomposition (NIRD), begins by decomposing an activation map into a hierarchical list of ROIs using a digital atlas. In an intermediate step, the ROIs are rank-ordered according to extent, mean intensity, and total intensity. A final rank-order list (NIRD average rank) is created by sorting the ROIs according to the average of their ranks from the intermediate step. The authors hypothesized that NIRD average rank would have improved regional quantification accuracy compared to all other quantitative metrics, including methods based on properties of statistical clusters. To test their hypothesis, NIRD rankings were directly compared to three common cluster-based methods using simulated fMRI data both with and without realistic head motion. RESULTS For both the no-motion and motion datasets, an analysis of variance found that significant differences between the quantification methods existed (F = 64.8, p < 0.0001 for no motion; F = 55.2, p < 0.0001 for motion), and a post-hoc test found that NIRD average rank was the most accurate quantification method tested (p < 0.05 for both datasets). Furthermore, all variants of the NIRD method were found to be significantly more accurate than the cluster-based methods in all cases. CONCLUSIONS These results confirm their hypothesis and demonstrate that the proposed NIRD methodology provides improved regional quantification accuracy compared to cluster-based methods.

A Real-Time Functional Magnetic Resonance Imaging (fMRI) Neurofeedback System

Abstract In order to implement brain-computer interfaces (BCIs), the individual must be able to volitionally control brain function: a skill that can only be achieved through specialized training. Currently, this training is time-consuming and produces unreliable results. It is necessary to first understand the underlying neural mechanisms associated with this skill to develop effective, dependable training techniques. A real-time functional Magnetic Resonance Imaging (rtfMRI) system has been developed to research volitional control over neural activity and interpret the neural changes associated with increases in this skill. The developed system enables additional research such as the study of neuroplasticity and neural networks.

Benchmarking Medical Image Databases

Abstract An increase in the use of digital images in medicine and medical research has resulted in petabytes of digital images acquired each year. As more information is collected, there is a greater need to store, share, and retrieve images from various medical imaging modalities. Web-based medical image data management systems have been created to solve many issues caused by the Picture Archiving and Communication System (PACS). Five such medical image database systems are reviewed in this work. Though these systems have various features, they seem to fail to capture experimental design specifications necessary for the proper interpretation of the associated images. We summarize the paper by indicating a wish list of the features that are missing from these systems.