Assistant Professor, Department of Human Physiology
Ph.D. University of California, San Diego
B.A Tufts University
Ian Greenhouse’s research examines how humans initiate and cancel movement. His lab combines behavioral testing with electrophysiology, neuroimaging, and brain stimulation in healthy and clinical populations. His current research explores the relationship between the inhibitory neurochemical gamma-aminobutyric acid (GABA) and motor performance.
Dr. Greenhouse earned his BA in Psychology at Tufts University and his Ph.D. at the University of California, San Diego. He completed postdoctoral training at the University of California, Berkeley. He joined the Department of Human Physiology at the University of Oregon in the Fall of 2017.
A consensus guide to capturing the ability to inhibit actions and impulsive behaviors in the stop-signal task.
Elife. 2019 04 29;8:
Authors: Verbruggen F, Aron AR, Band GP, Beste C, Bissett PG, Brockett AT, Brown JW, Chamberlain SR, Chambers CD, Colonius H, Colzato LS, Corneil BD, Coxon JP, Dupuis A, Eagle DM, Garavan H, Greenhouse I, Heathcote A, Huster RJ, Jahfari S, Kenemans JL, Leunissen I, Li CR, Logan GD, Matzke D, Morein-Zamir S, Murthy A, Paré M, Poldrack RA, Ridderinkhof KR, Robbins TW, Roesch M, Rubia K, Schachar RJ, Schall JD, Stock AK, Swann NC, Thakkar KN, van der Molen MW, Vermeylen L, Vink M, Wessel JR, Whelan R, Zandbelt BB, Boehler CN
Response inhibition is essential for navigating everyday life. Its derailment is considered integral to numerous neurological and psychiatric disorders, and more generally, to a wide range of behavioral and health problems. Response-inhibition efficiency furthermore correlates with treatment outcome in some of these conditions. The stop-signal task is an essential tool to determine how quickly response inhibition is implemented. Despite its apparent simplicity, there are many features (ranging from task design to data analysis) that vary across studies in ways that can easily compromise the validity of the obtained results. Our goal is to facilitate a more accurate use of the stop-signal task. To this end, we provide 12 easy-to-implement consensus recommendations and point out the problems that can arise when they are not followed. Furthermore, we provide user-friendly open-source resources intended to inform statistical-power considerations, facilitate the correct implementation of the task, and assist in proper data analysis.
PMID: 31033438 [PubMed - indexed for MEDLINE]
Planning face, hand, and leg movements: anatomical constraints on preparatory inhibition.
J Neurophysiol. 2019 05 01;121(5):1609-1620
Authors: Labruna L, Tischler C, Cazares C, Greenhouse I, Duque J, Lebon F, Ivry RB
Motor-evoked potentials (MEPs), elicited by transcranial magnetic stimulation (TMS) over the motor cortex, are reduced during the preparatory period in delayed response tasks. In this study we examined how MEP suppression varies as a function of the anatomical organization of the motor cortex. MEPs were recorded from a left index muscle while participants prepared a hand or leg movement in experiment 1 or prepared an eye or mouth movement in experiment 2. In this manner, we assessed if the level of MEP suppression in a hand muscle varied as a function of the anatomical distance between the agonist for the forthcoming movement and the muscle targeted by TMS. MEP suppression was attenuated when the cued effector was anatomically distant from the hand (e.g., leg or facial movement compared with finger movement). A similar effect was observed in experiment 3 in which MEPs were recorded from a muscle in the leg and the forthcoming movement involved the upper limb or face. These results demonstrate an important constraint on preparatory inhibition: it is sufficiently broad to be manifest in a muscle that is not involved in the task, but it is not global, showing a marked attenuation when the agonist muscle belongs to a different segment of the body. NEW & NOTEWORTHY Using transcranial magnetic stimulation, we examined changes in corticospinal excitability as people prepared to move. Consistent with previous work, we observed a reduction in excitability during the preparatory period, an effect observed in both task-relevant and task-irrelevant muscles. However, this preparatory inhibition is anatomically constrained, attenuated in muscles belonging to a different body segment than the agonist of the forthcoming movement.
PMID: 30785815 [PubMed - indexed for MEDLINE]
Comparison of Multivendor Single-Voxel MR Spectroscopy Data Acquired in Healthy Brain at 26 Sites.
Radiology. 2020 Feb 11;:191037
Authors: Považan M, Mikkelsen M, Berrington A, Bhattacharyya PK, Brix MK, Buur PF, Cecil KM, Chan KL, Chen DYT, Craven AR, Cuypers K, Dacko M, Duncan NW, Dydak U, Edmondson DA, Ende G, Ersland L, Forbes MA, Gao F, Greenhouse I, Group
Background The hardware and software differences between MR vendors and individual sites influence the quantification of MR spectroscopy data. An analysis of a large data set may help to better understand sources of the total variance in quantified metabolite levels. Purpose To compare multisite quantitative brain MR spectroscopy data acquired in healthy participants at 26 sites by using the vendor-supplied single-voxel point-resolved spectroscopy (PRESS) sequence. Materials and Methods An MR spectroscopy protocol to acquire short-echo-time PRESS data from the midparietal region of the brain was disseminated to 26 research sites operating 3.0-T MR scanners from three different vendors. In this prospective study, healthy participants were scanned between July 2016 and December 2017. Data were analyzed by using software with simulated basis sets customized for each vendor implementation. The proportion of total variance attributed to vendor-, site-, and participant-related effects was estimated by using a linear mixed-effects model. P values were derived through parametric bootstrapping of the linear mixed-effects models (denoted Pboot). Results In total, 296 participants (mean age, 26 years ± 4.6; 155 women and 141 men) were scanned. Good-quality data were recorded from all sites, as evidenced by a consistent linewidth of N-acetylaspartate (range, 4.4-5.0 Hz), signal-to-noise ratio (range, 174-289), and low Cramér-Rao lower bounds (≤5%) for all of the major metabolites. Among the major metabolites, no vendor effects were found for levels of myo-inositol (Pboot > .90), N-acetylaspartate and N-acetylaspartylglutamate (Pboot = .13), or glutamate and glutamine (Pboot = .11). Among the smaller resonances, no vendor effects were found for ascorbate (Pboot = .08), aspartate (Pboot > .90), glutathione (Pboot > .90), or lactate (Pboot = .28). Conclusion Multisite multivendor single-voxel MR spectroscopy studies performed at 3.0 T can yield results that are coherent across vendors, provided that vendor differences in pulse sequence implementation are accounted for in data analysis. However, the site-related effects on variability were more profound and suggest the need for further standardization of spectroscopic protocols. © RSNA, 2020 Online supplemental material is available for this article.
PMID: 32043950 [PubMed - as supplied by publisher]
Big GABA II: Water-referenced edited MR spectroscopy at 25 research sites.
Neuroimage. 2019 05 01;191:537-548
Authors: Mikkelsen M, Rimbault DL, Barker PB, Bhattacharyya PK, Brix MK, Buur PF, Cecil KM, Chan KL, Chen DY, Craven AR, Cuypers K, Dacko M, Duncan NW, Dydak U, Edmondson DA, Ende G, Ersland L, Forbes MA, Gao F, Greenhouse I, Harris AD, He N, Heba S, Hoggard N, Hsu TW, Jansen JFA, Kangarlu A, Lange T, Lebel RM, Li Y, Lin CE, Liou JK, Lirng JF, Liu F, Long JR, Ma R, Maes C, Moreno-Ortega M, Murray SO, Noah S, Noeske R, Noseworthy MD, Oeltzschner G, Porges EC, Prisciandaro JJ, Puts NAJ, Roberts TPL, Sack M, Sailasuta N, Saleh MG, Schallmo MP, Simard N, Stoffers D, Swinnen SP, Tegenthoff M, Truong P, Wang G, Wilkinson ID, Wittsack HJ, Woods AJ, Xu H, Yan F, Zhang C, Zipunnikov V, Zöllner HJ, Edden RAE
Accurate and reliable quantification of brain metabolites measured in vivo using 1H magnetic resonance spectroscopy (MRS) is a topic of continued interest. Aside from differences in the basic approach to quantification, the quantification of metabolite data acquired at different sites and on different platforms poses an additional methodological challenge. In this study, spectrally edited γ-aminobutyric acid (GABA) MRS data were analyzed and GABA levels were quantified relative to an internal tissue water reference. Data from 284 volunteers scanned across 25 research sites were collected using GABA+ (GABA + co-edited macromolecules (MM)) and MM-suppressed GABA editing. The unsuppressed water signal from the volume of interest was acquired for concentration referencing. Whole-brain T1-weighted structural images were acquired and segmented to determine gray matter, white matter and cerebrospinal fluid voxel tissue fractions. Water-referenced GABA measurements were fully corrected for tissue-dependent signal relaxation and water visibility effects. The cohort-wide coefficient of variation was 17% for the GABA + data and 29% for the MM-suppressed GABA data. The mean within-site coefficient of variation was 10% for the GABA + data and 19% for the MM-suppressed GABA data. Vendor differences contributed 53% to the total variance in the GABA + data, while the remaining variance was attributed to site- (11%) and participant-level (36%) effects. For the MM-suppressed data, 54% of the variance was attributed to site differences, while the remaining 46% was attributed to participant differences. Results from an exploratory analysis suggested that the vendor differences were related to the unsuppressed water signal acquisition. Discounting the observed vendor-specific effects, water-referenced GABA measurements exhibit similar levels of variance to creatine-referenced GABA measurements. It is concluded that quantification using internal tissue water referencing is a viable and reliable method for the quantification of in vivo GABA levels.
PMID: 30840905 [PubMed - indexed for MEDLINE]