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Introduction to the Imaging Research Center

Fumiko Hoeft, MD PhD Roeland Hancock, PhD Prof. Psychological Sciences, , Assis. Prof. Psychological Sciences Director of BIRC Associate Director, BIRC [email protected] [email protected] @FumikoHoeft @RHancockN BIRC: Brain Imaging Research Center

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E.Collin BIRC: Brain Imaging Research Center

• Housed in the Philips Communication Building, Storrs campus • Opened its door in 2015 • 3,200 square feet of renovated research space for • Provides a multidisciplinary hub for new research • Offers clinical services in collaboration with UCHC Radiology since Fall, 2018

E.Collin BIRC Vision

FOSTER MULTIDISCIPLINARY FACILITATE SCIENTIFIC ENGAGE THE COMMUNITY COLLABORATION DISCOVERY & ADVANCE THEORY

Clinical

Edu CogPsy UCHC Dis- covery Stage

UConn UConn Region Storrs Time Scale Kinesio BIRC SLHS Out- reach

Others: Developmental Comm- Yale, Impact uniity CT St Biological Level PNB Linguist

BME Cognitive Processes What can brain imaging tell us?

• Biomarkers. Brain patterns can be used for early identification & to predict treatment response. e.g. Alzheimer’s Disease • Training Effect. Brain pattern can track and change with treatment, instruction and training. E.g. exercise • Phenotypes. Identification of unexplored subtypes. E.g. autism • Inform Practice. R&D of practical tools such as an app • Others. Imaging , heritability. Cross-species comparison. . Laminar & columnar structure. Network analyses. Machine learning, pattern classification. e.g. Biomarkers: Predicting outcome by MRI and behavior are not 2 sides of the same coin

Brain patterns using machine learning predicts outcome better than Bx alone(>90% vs. <60%).

Behavior (Bx) and Brain combo (81%) predicted academic outcome better than Bx (65%) or Brain (57%) alone (vs. 81%).

Hoeft … Gabrieli. Beh neurosci 2007 Hoeft… Gabrieli. PNAS 2011 e.g. Intervention Effect: Different strategies: same reduction, different mechanism 15320 • J. Neurosci., November 18, 2015 • 35(46):15307–15325 Zeidan et al. • Mindfulness Meditation-BasedZeidan Pain et al.Relief• Mindfulness Meditation-Based Pain Relief J. Neurosci., November 18, 2015 • 35(46):15307–15325 • 15313

Figure 3. Psychophysical pain ratings (mean Ϯ SEM) in MRI Session B. Mindfulness meditation produced greater reductions in both pain intensity (left) and pain unpleasantness (right) compared with placebo. **Mindfulness meditation also was significantly (p Ͻ 0.05) more effective at reducing pain intensity (left) and pain unpleasantness (right) ratings than sham mindfulness Mindfulnessmeditation and control conditions. meditation *All cognitive manipulations-based were significantly pain (p Ͻ 0.004) morerelief effective at reducing pain intensity and unpleasantness ratings compared with the control group. 0.24, p Ͻ 0.001). Therefore, in addition to mean intensity normalization employs different neural mechanismsantness than (F(3,71) ϭ 0.10, p ϭ 0.962) ratings in response to the of our PCASL data, we first segmented each subject’s anatomical data “heat” series (Fig. 2). into white matter partial volume maps using the FAST algorithm in FSL One participant provided a rating of 0.30 (pain intensity and placebo(Smith et al.,and 2004) andsham extracted theirmindfulness respective white matter values.meditationunpleasantness)- in response to a “neutral” series. All other sub- We then included each individual’s respective white matter value as a jects provided a “0” to “neutral” series. Therefore, there were no nuisance covariate of no interest in the first-level analyses in MRI Session significant group differences for pain intensity (F ϭ 0.98, p ϭ inducedB(Fox et al.,analgesia 2005; Restom et al., 2006; Behzadi et al., 2007; Leber, 2010). (3,71) Subject-to-subject visual inspections of first-level PCASL analyses con- 0.406) or pain unpleasantness (F(3,71) ϭ 0.98, p ϭ 0.406) ratings firmed that this technique reduced white matter artifacts. in response to the “neutral” series. Analysis of physiological data Zeidan… Coghill. J NeurosciPost-intervention2015 In all ANOVAs examining physiological data, significant (p Ͻ 0.05) main Mindfulness meditation produces greater pain relief than pla- effects and interactions were investigated with planned post hoc tests com- cebo and sham mindfulness meditation. Figure 6. Brain activations and deactivations associated with the main effect of mindfulness meditation and sham mindfulness meditation. Sham mindfulness meditation producedparing significant the percent change in physiological outcomes between the mindful- Pain intensity ratings activation in the globus pallidus, putamen and right SI of the nose and significant deactivation of the subgenual ACC, PCC, cerebellum and mPFC compared with pre-manipulation.ness Compared meditation with and the three comparison groups (Toothaker, 1993; Cohen and Lea, 2004). All groups exhibited a significant change in pain intensity sham mindfulness meditation, mindfulness meditation produced greater activation in the right putamen/globus pallidus and the PCC. Compared with mindfulness meditation, sham mindfulness ratings from pre-manipulation to post-manipulation in re- meditation was associated with greater activation in the DLPFC, thalamus, PAG, and cerebellum. Conjunction analyses revealed significant overlapping activation in the bilateralRespiration putamen and SI rate sponse to “heat” series (F ϭ 10.06, p ϭ 0.002, ␩2 ϭ 0.12; ϫ (1,71) p corresponding to the nose and deactivation in the mPFC, PCC/precuneous, and cerebellum. Slice locations correspond to standard stereotaxic space. A 2 (pre-manipulation vs post-manipulation) 2 (“heat” vs “neu- Fig. 3). The significant group ϫ manipulation interaction on tral”) ϫ 4 (group) mixed-model ANOVA tested for changes in respira- 2 tion rate in MRI Session B. Prior work has demonstrated a positive pain intensity (F(3,71) ϭ 9.96, p Ͻ 0.001, ␩p ϭ 0.30) was asso- ciated with the significant decrease in pain intensity ratings some of the differences between mindfulness and sham mindful- 0.54), but no main effect of “heat” versus “neutral” stimulationrelationship between pain ratings and respiration rate (Grant and Rain- ville, 2009; Martin et al., 2012). Therefore, a three-factor ANOVA tested during mindfulness meditation (Ϫ27%; F(1,16) ϭ 13.00, p ϭ 2 2 ness meditation were associated with greater deactivation in one (F(1,71) ϭ 0.02, p ϭ 0.881). However, there was a significantthe main pre- effect of group, manipulation, and stimulation level (i.e., 0.002, ␩p ϭ 0.45), placebo (Ϫ11%; F(1,18) ϭ 5.74, p ϭ 0.028, ␩p condition compared with the other (Fig. 6). manipulation vs post-manipulation ϫ group interaction“heat,” (F(3,71) “neutral”)ϭ on respiration rate. Multiple regression analyses were ϭ 0.24), and sham mindfulness meditation (Ϫ8%; F(1,19) ϭ 2 performed to determine whether respiration rate predicted changes in 4.67, p ϭ 0.044, ␩2 ϭ 0.20) and the significant pain intensity Across all individuals, sham mindfulness meditation produced 28.39, p Ͻ 0.001, ␩p ϭ 0.55). These effects were associated with the p pain ratings within groups. ␩2 greater activation in the thalamus, left putamen, SMA, PCC, and SI significant percent reduction in respiration rate during mindfulness increase (ϩ14%; F(1,18) ϭ 7.52, p ϭ 0.013, p ϭ 0.30) during compared with pre-manipulation in the presence of noxious heat meditation and sham mindfulness meditation when comparedGlobal cerebral to blood flow the control condition. There was no significant main effect of group (F ϭ 1.20, p ϭ 0.315). stimulation. There was also greater deactivation of the ACC, mPFC, the pre-manipulation condition and comparison groupsIn (pϽ MRI0.001; Session B,a2(pre-manipulation vs post-manipulation) ϫ 2 (3,71) (“heat” vs “neutral”) ϫ 4 (group) mixed-model ANOVA tested for To interpret the significant manipulation ϫ group interac- and middle frontal gyrus (Fig. 7C). Importantly, regression analyses Table 1). There were no significant differences in respirationchanges rate in global CBF. tion, we calculated and compared the percent change in pain detected no significant relationship between interindividual changes from pre-manipulation to post-manipulation between the mindful- intensity and unpleasantness ratings between the mindfulness in pain intensity/unpleasantness ratings and sham mindfulness ness meditation and sham meditation group (p ϭ 0.316).Heart rate meditation and comparison groups (Toothaker, 1993). This A 2 (pre-manipulation vs post-manipulation) ϫ 2 (“heat” vs “neu- approach was used to test the hypothesis that mindfulness meditation-related brain activation (Fig. 7C). However, sham mind- tral”) ϫ 4 (group) mixed-model ANOVA tested for changes in heart rate fulness meditation-related pain relief was associated with reductions Respiration rate and pain ratings in MRI Session B. meditation would produce greater reductions in pain ratings in respiration rate (p ϭ 0.040, sr 2 ϭ 18%; see “Physiological data To determine whether respiration rate differentially predicted compared with the comparison groups. Our hypotheses were changes in pain between groups, a multiple regressionResults analysis supported because mindfulness-meditation-related pain in- findings” for more details). In contrast, mindfulness-based pain re- tensity reductions (Fig. 3)weresignificantlygreaterthan tested the relationship between respiration rate andBehavioral group on findings lief was associated with greater activity in higher-order brain regions Pre-intervention placebo analgesia (p ϭ 0.032), sham mindfulness-meditation- (i.e., OFC, sgACC, insula; Fig. 7A)andexhibitednosignificant(p ϭ post-manipulation pain intensity and pain unpleasantnessThere rat- were no significant pre-intervention differences among related pain relief (p ϭ 0.030), and the control condition (p Ͻ

0.426) relationship with changes in respiration rate (see “Physiolog- ings, respectively. We first entered pre-manipulation paingroups inten- for pain intensity (F(3,71) ϭ 0.002, p ϭ 1.00) or unpleas- 0.001). Importantly, all cognitive manipulations significantly ical data findings” for more details). sity or pain unpleasantness ratings into the regression model. Next, pre-manipulation and post-manipulation respiration rate Physiological data findings values were entered, respectively. Group was then entered to de- Mindfulness and sham mindfulness meditation significantly termine whether group uniquely contributed to the model. There reduced respiration rate was no significant relationship between group, respiration rate, The ANOVA conducted on respiration rate detected a significant and pain intensity ratings (p ϭ 0.302). However, group uniquely 2 2 main effect of group (F(3,71) ϭ 3.43, p ϭ 0.021, ␩p ϭ 0.13 pre- contributed (p ϭ 0.039, sr ϭ 0.02; Table 4) to the overall model 2 manipulation vs post-manipulation, F(1,71) ϭ 82.12, p Ͻ 0.001, ␩p ϭ on respiration rate and pain unpleasantness ratings. e.g. Intervention Effect: Rapid compensatory

ARTICLE changes NATURE COMMUNICATIONS | DOI: 10.1038/s41467-018-04627-5

a Left arcuate Left ILF Posterior CC NATURE COMMUNICATIONS | DOI:Session 10.1038/s41467-018-04627-5 Session Session ARTICLE 1234 1234 1234 Reading intervention in dyslexia 1.16 1.3 1.8 Left arcuate Left ILF Posterior CC /s) 2 110 110 110 m causes: µ 1.12 1 1.2 1 1 Typical readers 100 100 1.4100 • Rapid (~2.5wks) & specific 1.0 1.1 4 4 4 reorganization 90 90 90 of white matter Mean diffusivity ( 1.04 3 1.0 3 1.0 3 2Location 2 Location2 Location fibers. WJ basic reading 80 1 80 1 80 1 Occipital Intervention group • Reflects compensatory 70 Frontal Temporal 70 70 Left Right 1.07 1.1 1.13 1.18Temporal 1.23 1.28 1.35 1.47 1.59 mechanism, not normalization. 105 105 105 b Intervention Control Intervention Control Intervention Control • 0.05 1 0.05 1 0.05 1 Relationship with behavior 95 95 95 0.02 0.02 0.02 changes (so longitudinal –0.0285 –0.0285 –0.0285 research necessary, not Change in MD in Change * 4 4 * 4 –0.05 ** –0.05 ** ** –0.05 TOWRE index TOWRE 2 3 2 3 23 75 75 75 snapshot) c 0.02 1 0.02 ** **1 1 ** ** * * 0.02 65 65 65 1.07 1.1 1.13 1.18 1.23 1.28 1.35 1.47 1.59 Mean diffusivity (µm2/s) Huber… Yeatman. Nat Comm 2018 Change in FA in Change –0.02 –0.02 Fig. 3 Reading intervention does not normalize differences in the white matter. Reading skill is–0.02 plotted as a function of mean diffusivity for each session Fig.(1– 24)Change for the vs.left stability arcuate, in ILF, Tract and Pro posteriorfiles during callosal reading tracts intervention. in the interventiona Mean group.diffusivity The (MD) gray circlevalues and were lines mapped in each onto panel each show of the the 100 mean evenly and spaced standard nodeserror spanning at Session termination 1 for the subset points non-intervention at the gray–white control matter subjects boundary with to reading create skills a ‘Tract in the Pro typicalfile’ (see range Methods (poor andreading ref. controls78 for additional were excluded, details ofleaving this n = analysis).10 typical For reading visualization controls). purposes, The dashed the middle gray arrow 80 nodes shows are the plotted. expected Each trajectory line represents for MD the values group if the average intervention MD across group subjects, were to measured become more at four similar time to points:the typical pre-intervention reading controls (Session in terms 1), after of both~2.5 weeks reading of skills intervention and MD. (Session In contrast, 2), after the true ~5 weeks trajectory of intervention of change in(Session plotted 3), as and a colored after 8 arrow weeks in of each intervention panel. The (Sessionintervention 4). Shaded group error includes bars some give ±1 readers standard with error only of moderate the mean. reading Color values impairments indicate (and, session, therefore, ranging higher from darkest MD values), (Session and 1) so to the brightest group difference (Session 4) in for pre- eachintervention tract. The scoresx axis isshows less thanthe location would be where expected each tract for a was group clipped of good prior vs. to poor analysis readers. (corresponding Supplementary to black Fig. boundary7 reproduces lines the in renderings, intervention below). data alongside Tract renderingsthe same are mean shown and forSE plots an example for the subject. full sample The of middle control 60% subjects (bounded during by sessions black lines) 1–3, of to each further tract illustrate was analyzed the relative in b, c stability, to avoid of partial both white volume matter effects and thatbehavior occur atin endpointsthe control of group the tract, where it enters cortex. Both the arcuate fasciculus (AF) and inferior longitudinal fasciculus (ILF), but not the posterior callosal connections, show a systematic decrease in MD over the course of intervention. b, c Bars show model-predicted change (coefficients and standard errors from mixed effects model) in MD (b) and FA (c) for each session. Bar heights represent the magnitude of change observed in that session, relative to Sessionclear intervention-driven1 (pre-intervention) baseline, changes as determined in both MD by the and mixed FA. effects Within model.Skill As described included in as the a maincovariate text, both in the the model. AF and We ILF showed obtained signi theficant same changethe intervention between sessions group, for the signi interventionficant changes group (filled intissue bars) but properties not the controlresults group for(unfi thelledgroup-by-session bars). Asterisks indicate interaction a significant in decreaseall cases in (AF: MD (MD,b) oremerged increase in in FA the (cfi)rst for post-baseline each session relative measurement to the pre-intervention session, after baseline just atF a(1,65) Bonferroni-corrected= 7.72, p = 0.0071; *p < 0.05 FA, andF **(1,65)p < 0.01= 2.86, p = 0.095; ILF: (days)46.05 interaction.h (SD = 14.88) As above, ofintervention, we substitute “ overdays” thefor “interven- course of (1,125)MD, =F(1,65)0.36, =p =8.37,0.55).p = In0.0052; the ILF, FA, weF saw(1,65) no= signi6.71,ficantp = main0.012; tion2–3 hours weeks.” to In giveline with a meaningful the results predictor reported for above both for the the effectCC: of MD, groupF(1,65) (F(1,125)= 0.63,=p0.0015,= 0.43;p FA,= 0.97)F(1,65) or time= 2.42, (F(1,125)p = 0.12).= interventioncontinuous and predictor control (days), subjects. we In observed the left a AF, group-by-session we found a 1.93, p = 0.17) and no significant group-by-time interaction (F signiinteractionficant main for effect MD of in group the (AFF(1,125) (no= main7.047, effectp = 0.009) of session, but (1,125) = 0.15, p = 0.70). In the CC, we saw no significant main notF(1,67) of time= 2.12, (F(1,125)p ==0.15,1.033, orp = group,0.31) andF(1,67) a signi= 0.58,ficantp group-= 0.45, effectRelationship of group between (F(1,125) white= 0.23, matterp = 0.63) plasticity or time and ( remediationF(1,125) = . by-timesession-by-group interaction ( interaction,F(1,125) = 4.97,F(1,67)p ==0.028),7.75, consistentp = 0.0070) with and a 0.86,Onep possible= 0.36) interpretation and no signifi ofcant group group-by-time differences interaction in MD and (F FA decreasethe ILF in (no MD main over effect time of that session, was speciF(1,67)fic to= the1.77, interventionp = 0.19, or (1,125)between= 0.35, goodp and= 0.56). poor As readers shown is in that Supplementary these differences Table refl3ect, subjects.group, F Similarly,(1,67) = 0.044, in thep ILF,= 0.83, we saw session-by-group a significant main interaction, effect of F theabnormal group-by-time tissue properties interaction in approached poor readers. signi Inficance that case, for the one group(1,67) (F=(1,125)6.91, =p =10.29,0.011)p = but0.0017) not butthe not CC of (no time main (F(1,125) effect= of quadraticmight predict term for that FA remediationin the left AF of and reading ILF, but dif notficulties for MD would in 3.72,session,p =F0.056)(1,67) and= 1.029, a signip =fi0.31,cant maingroup-by-time effect of group, interactionF(1,67) (F= theinvolve AF or a ILF,“normalization or for either” parameterof deficits in in the white CC. matter structure. (1,125)5.99, p==9.53,0.017,p = no0.0025). session-by-group In the CC, we interaction, saw a signiF(1,67)ficant= main0.62, Alternatively,Given the observed plasticity non-linearity in the white of matterintervention-driven might reflect effects a com- effectp = 0.44) of group and ( forF(1,125) FA in= 6.69, the ILFp = (no0.011) main but effect not of of time session, (F inpensatory FA, we opted mechanism to use “session that supports number” theas alearning categorical process predictor12,52,53. (1,125)F(1,67)==0.900.65,p =p0.34)= 0.42, and or no group,significantF(1,67) group-by-time= 0.60, p inter-= 0.44, inIn the that analysis case, to white follow, matter sincetissue this approach properties summarizes in the remediated session- actionsession-by-group (F(1,125) = 0.027, interaction,p = 0.87),F(1,67) consistent= 6.45, withp = the0.013) stability but ofnot to-sessionreaders would differences not necessarily from baseline, look without more similar imposing to a those shape in on the MDthe values AF (no in main this effect tractin of session,all subjects.F(1,67) For= FA,0.0057, we observedp = 0.94, a or thetypical trajectory reading of change. control Sessions subjects. were systematically spaced over differentgroup, F pattern(1,67) = of1.57, results:p = In0.21, the AF,no session-by-group we saw no significant interaction, main time,We andfind this that timing intervention-driven was consistent changes across subjects; in white hence matter effectF(1,67) of= group2.85, (F(1,125)p = 0.096)= 0.31, or CCp = (no0.58) main or time effect (F(1,125) of session,= “sessiondeviate” fromwas the highly trajectory correlated predicted with by a“days normalization” (r(127) = account.0.97, 0.055,F(1,67)p ==0.82)0.26, andp = no0.61, signi orfi group,cant group-by-timeF(1,67) = 0.14, interactionp = 0.71, (F no p Figure< 0.001).3 Asshows shown changes in Fig. in2, both MD the and left reading AF and scores ILF showed for the session-by-group interaction, F(1,67) = 2.38, p = 0.13). An intervention group, relative to Session 1 data for the subset of 4 exploratory analysis ofNATURE this sameCOMMUNICATIONS session-by-group| (2018)9:2260 interaction non-intervention| DOI: 10.1038/s41467-018-04627-5 controls who| hadwww.nature.com/naturecommunications reading skills in the typical for all available tracts is given in Supplementary Table 10. Finally, range. We defined “Typical Readers” as Control Group subjects to ensure that the interaction was not driven by differences in the with timed (TOWRE Index) and untimed (WJ Basic Reading stability of our measurements in good vs. poor readers, given that Score) reading accuracy within a standard deviation of the the control group included both typical readers and subjects with population mean (at or above 85 on both measures). For the dyslexia, we repeated the above analysis with baseline Reading intervention group, we plot changes in both WJ Basic Reading

NATURE COMMUNICATIONS | (2018)9:2260 | DOI: 10.1038/s41467-018-04627-5 | www.nature.com/naturecommunications 5 e.g. From to App R&D

Leverage multidisciplinary collaboration, technology, and insights from neuroscience for R&D of a school readiness & dyslexia- screener app

by F.Hoeft in collaboration with brainLENS, UCSF, Curious Learning, MIT, UConn Techniques Available

• Complementary UConn BIRC UConn CSSERL techniques available Haskins Labs • Multiple techniques probe brain function at different temporal and spatial tDCS scales

• Allows for a complete TMS

picture of macroscopic Shading indicates degree of invasiveness brain function Modified by R.Hancock from Kameyama et al. 2016 MRI: Magnetic Resonance Imaging

• Magnetic Resonance Imaging (MRI) • Best commercially available high field MRI (Siemens 3T Prisma) • MRI uses a powerful magnet to image the body (x600 strength of fridge!) • Completely non invasive, no ionizing radiation exposure • Measures anatomy and brain function functional MRI (fMRI), structural MRI, diffusion weighted imaging (DWI), magnetic resonance spectroscopy (MRS), etc… Other Equipment

• High density electroencephalography (EEG) • 2x 256 channel systems • Records electrical activity in the brain • Systems for use in and out of the MRI • Transcranial magnetic stimulation (TMS) • Modulates brain activity. Blocks & enhances. • High density transcranial electrical stimulation (tES) • Eye tracking • Records eye movement during behavior • Systems for use in and out of the MRI Support for Participants: Mock MRI etc

• Mimics the feel and sound of an MRI • Acclimates participants to the MRI • Particularly helpful for children

• Staging of MRI & MRI prep video Support for Users: Workspace and Data Analysis

• Shared space for data analysis • Computing resources • Access to high performance computing systems Training: IBRAiN Fellowships

• IBACS-BIRC Research Assistantships in Neuroimaging • Graduate training in neuroimaging • 1-2 year 10hr/wk fellowships • MRI scan time for fellows to develop their own projects • Fellows support the community by consulting and mentoring faculty and students

• Your students can apply to Inaugural IBRAiN Cohort (2018) become experts • Your team can get support to jump start your project Summary of BIRC

• BIRC provides a multidisciplinary hub for research in cognitive science and other fields • Supports a wide range of basic and applied research • Provides training opportunities and support to users • And… MRI is one of the most expensive research tools that IDC applies (MRI: $500-1,000/sj, Bx: $0-10/sj) J

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Institute for Collaboration on Health, Intervention, and Policy @UCONNInCHIP FY19 Research Seed Grant

l The Institute for Collaboration on Health Intervention and Policy (InCHIP) and the Brain Imaging Research Center (BIRC) are offering a $30,000 seed grant for an innovative pilot project in health with a neuroimaging component. Ø $15,000 exclusively for BIRC equipment usage. Ø $15,000 for other research costs associated with the pilot project. l The funded pilot project must involve neuroimaging-related research that includes MRI, TMS, tDCS/tACS, and/or EEG usage at BIRC. l Click here for more information about this seed grant.

$30,000 seed grant opportunity! LOI due 4/26