ライフサイエンスコーパス: fMRI

1 fMRI analyses reveal that putative gain signals affectin

2 Of the 52 fMRI participants (mean [SD] age, 34.0 [9.1] years), 30

3 ween patients with ASD (structural MRI: 911; fMRI: 188) and OCD (structural MRI: 928; fMRI: 247) and

4 11; fMRI: 188) and OCD (structural MRI: 928; fMRI: 247) and control subjects.

5 ta and tau are both associated with aberrant fMRI activity in the MTL during memory encoding in cogni

6 Here we acquired fMRI data in an incidental-viewing paradigm in which sub

7 Using a combination of advanced fMRI analysis techniques, including individual functiona

8 or placebo for 16 weeks and administered an fMRI alcohol cue reactivity task at baseline and after 2

9 r-old individuals (n = 106) who completed an fMRI attention task: 24 with BP, 29 at risk based on a f

10 To investigate this process, we performed an fMRI experiment in which five men and two women passivel

11 Here, we performed an fMRI study during which human subjects had to memorize v

12 mg) or placebo for 8 days, and underwent an fMRI alcohol cue-reactivity task (day 7; n=81) and a bar

13 a subset of participants (n=38) underwent an fMRI scan to assess neural correlates of inhibitory cont

14 Our simultaneous EEG and fMRI analysis on 21 human subjects (12 males, 9 females)

15 ovariations of concurrently recorded EEG and fMRI in a cued visual spatial attention task in humans,

16 imultaneous electroencephalography (EEG) and fMRI (EEG/fMRI) enables temporal characterization of bra

17 This correlation between fNIRS and fMRI was only present when data from the same story were

18 The combination of isoproterenol and fMRI offers a powerful approach for evaluating insula fu

19 n midline regions of the DMN in both MEG and fMRI, boosting confidence in a possible pathophysiologic

20 ical functional connectivity in both MEG and fMRI.

21 aningful RSNs could be identified in PET and fMRI data, respectively.

22 urrent transcranial magnetic stimulation and fMRI in healthy participants demonstrated that the later

23 using a novel intertemporal lottery task and fMRI.

24 Memory tests and fMRI scans were conducted at baseline and at the end of

25 This technique, known as fMRI neurofeedback, faces challenges as many participant

26 Moreover, model-based fMRI analyses identified the caudate nucleus as the key

27 ability has been conducted within task-based fMRI contexts on adults and older individuals, very litt

28 bining computational modelling of behaviour, fMRI and PET measurements of DA D1 availability.

29 Association between fMRI activation and genomic mutation status was obtained

30 ely associated with the time elapsed between fMRI assessment and delivery.

31 The interplay between fMRI connectivity, (1)H MRS and clinical data was explor

32 of simultaneously acquired whole-brain BOLD fMRI.

33 ssed by positive and negative shifts in BOLD fMRI signal.

34 pact of these findings on understanding BOLD-fMRI signals.

35 hich coding schemes are plausible given both fMRI's successes and its limitations in measuring neural

36 Using a multivariate approach to both fMRI and MEG, we characterize the functional neuroanatom

37 undus (AF) face patch, combining whole-brain fMRI with longitudinal single-unit recordings in a local

38 mics of the hemodynamic response measured by fMRI as a potential cause of the non-responder effect.

39 rovide evidence of bilateral CSD recorded by fMRI during bilateral aura symptoms.

40 both computational and standard categorical fMRI analyses, we show that when planning was influenced

41 Simultaneously collected fMRI data reveals tACS effects dependent on the relative

42 uperior temporal cortices (STC) we collected fMRI data from deaf and hearing participants (male and f

43 Our combined fMRI and MRS investigation showed that the stronger ATL

44 We compared fMRI results to DCS to help optimize noninvasive languag

45 ith (n=17) and without (n=20) ADHD completed fMRI scanning under each of three conditions: (a) smokin

46 Our computational fMRI analysis revealed that anterior dorsomedial prefron

47 Using computational fMRI, we show parallel encoding of effort and reward pre

48 movement variability, we find that cortical fMRI variability in parietal cortex of individual subjec

49 In a multi-day fMRI study, participants encoded trial-unique associatio

50 Block-design fMRI was used to assess the blood oxygen level-dependent

51 We developed fMRI markers predicting moment-by-moment intensity level

52 During fMRI, participants indicated their preferences between a

53 s played a virtual game ("Cyberball") during fMRI recording.

54 response to monetary wins than losses during fMRI), while the ventral SN connects with associative re

55 their liking of the presented person during fMRI scanning.

56 ask and four blocks of a control task during fMRI imaging.

57 ce) completed a context encoding task during fMRI scanning using a delayed match-to-sample design wit

58 lue-contingent cognitive control task during fMRI to assess how stakes-the value of a prospective out

59 s (n = 26) performed the same IC task during fMRI, followed by completing a laboratory-based smoking

60 ociated with either high or low-value during fMRI acquisition.

61 Using a combination of EEG, fMRI, and diffusion-weighted imaging, we show that activ

62 characterized in 29 patients; 26 had an EEG-fMRI GM localization that was correct in 11, 22 patients

63 rictal discharges were mapped using both EEG-fMRI hemodynamic responses and ESI.

64 ing resection was correctly predicted by EEG-fMRI GM in 8 of 20 patients, and by the ESI maximum in 1

65 rect in 17, and 12 patients had combined EEG-fMRI and ESI that was correct in 11.

66 The combined EEG-fMRI/ESI region entirely predicted outcome in 9 of 9 pat

67 rticipants (both sexes) using concurrent EEG-fMRI and a sustained selective listening task, in which

68 INTERPRETATION: EEG-fMRI combined with ESI provides a simple unbiased locali

69 Here we use simultaneous EEG-fMRI and computational modelling to identify EEG signals

70 Together, these combined EEG/fMRI results illuminate the dynamically interacting cort

71 s electroencephalography (EEG) and fMRI (EEG/fMRI) enables temporal characterization of brain-network

72 ngs enable reinterpretation of many existing fMRI datasets, and suggest a new way to explore the whit

73 -trial variability in these signals explains fMRI responses in posterior-medial frontal cortex.

74 and exclusion, using a novel social feedback fMRI paradigm in a population-derived sample of adolesce

75 1.1 x 1.1 x 1.1 mm(3) using ultra-high field fMRI (at 7 Tesla).

76 ults highlight the value of ultra-high-field fMRI in generating more refined, anatomically informed,

77 the bSTC by using ultra-high magnetic field fMRI at 7 Tesla.

78 For fMRI to succeed given its low temporal and spatial resol

79 poral processing can be readily derived from fMRI signal amplitudes in male and female subjects.

80 paminergic system in humans is emerging from fMRI data.

81 In addition, three further fMRI tasks captured semantic, phonological, and orthogra

82 s specifically influences regional or global fMRI signals.

83 Here, fMRI evidence measured at high field (7T) and high resol

84 on averaging mechanism that approximates how fMRI voxels locally average distinct neuronal tunings.

85 However, fMRI connectomes additionally involve negative edges, wh

86 rformed an associative memory task during hr-fMRI in which they encoded and later retrieved face-name

87 Here we used a novel approach in human fMRI and MEG studies to reveal supra-additive scene-obje

88 However, it is unknown to what level human fMRI is specific and sensitive enough to reveal directio

89 Using the complimentary techniques of human fMRI and monkey electrophysiology in a context-dependent

90 epresentational similarity analysis of human fMRI data to measure the common and idiosyncratic repres

91 Previous human fMRI studies have focused on its role in object memory,

92 We tested this hypothesis using human fMRI by tracking changes in item-specific BOLD activity

93 using psychophysics and functional imaging (fMRI).

94 using functional magnetic resonance imaging (fMRI) [1-4] and behavioral studies of patients with acqu

95 ution functional magnetic resonance imaging (fMRI) alongside a perceptual oddity task, modified from

96 state functional magnetic resonance imaging (fMRI) and (1)H magnetic resonance spectroscopy (MRS) dat

97 ining functional magnetic resonance imaging (fMRI) and magnetoencephalography (MEG) in the same group

98 using functional magnetic resonance imaging (fMRI) and positron emission tomography (PET) multimodal

99 ns to functional magnetic resonance imaging (fMRI) and show that noise correlations between heterogen

100 used functional magnetic resonance imaging (fMRI) and the monetary incentive delay (MID) task to ass

101 ue of functional magnetic resonance imaging (fMRI) as an objective measure to assess independently th

102 Functional magnetic resonance imaging (fMRI) based on the blood oxygen level dependent (BOLD) c

103 o the functional magnetic resonance imaging (fMRI) BOLD signal and electrocorticographic (ECoG) field

104 on of functional magnetic resonance imaging (fMRI) in a 2-drug, double-blind placebo-controlled cross

105 using functional magnetic resonance imaging (fMRI) in a large multisite sample (n = 1,188), we show h

106 with functional magnetic resonance imaging (fMRI) in volunteers as they performed a concurrent appet

107 used functional magnetic resonance imaging (fMRI) methods uniquely powered to compare the neural cor

108 ed on functional magnetic resonance imaging (fMRI) of the macaque indicating that space is predominan

109 ution functional magnetic resonance imaging (fMRI) response patterns in the human auditory cortex.

110 rwent functional magnetic resonance imaging (fMRI) scanning while performing a visceral interoceptive

111 Functional magnetic resonance imaging (fMRI) scans were acquired with a 3T MRI scanner using a

112 ) and functional magnetic resonance imaging (fMRI) scans while performing the n-back working memory t

113 BOLD) functional magnetic resonance imaging (fMRI) signal response to visual stimulation was measured

114 ever, functional magnetic resonance imaging (fMRI) studies have reported conflicting results, with bo

115 vious functional magnetic resonance imaging (fMRI) studies have revealed different brain responses to

116 Functional magnetic resonance imaging (fMRI) studies indicate that episodic simulation (i.e., i

117 nd 26 functional magnetic resonance imaging (fMRI) studies of inhibitory control was conducted compar

118 Functional magnetic resonance imaging (fMRI) studies performed during site-specific pharmacolog

119 state functional magnetic resonance imaging (fMRI) techniques in the last two decades have provided a

120 used functional magnetic resonance imaging (fMRI) to investigate how the human brain plans the short

121 human functional magnetic resonance imaging (fMRI) with a previously developed Stroop protocol that a

122 bined functional magnetic resonance imaging (fMRI) with resting-state magnetic resonance spectroscopy

123 ed by functional magnetic resonance imaging (fMRI), and neuronal precursor cells (BrdU+/Nestin+) were

124 using functional magnetic resonance imaging (fMRI), determining their moment-to-moment level of activ

125 ch as functional magnetic resonance imaging (fMRI), diffusion tensor imaging (DTI), and electroenceph

126 ty of functional magnetic resonance imaging (fMRI)-derived brain networks or "connectomes" can inform

127 eling functional magnetic resonance imaging (fMRI).

128 using functional magnetic resonance imaging (fMRI).

129 ts in functional magnetic resonance imaging (fMRI).

130 using functional magnetic resonance imaging (fMRI).

131 In fMRI analyses, during inhibition, right caudate anomalie

132 In fMRI, ASD patients showed disorder-specific reduced left

133 importance of accounting for time of day in fMRI protocols.SIGNIFICANCE STATEMENT This is one of the

134 obiological information of negative edges in fMRI connectomes are increasingly important.

135 fully account for orientation information in fMRI and demonstrates that fMRI also reflects fine-grain

136 arning from habit learning in 72 subjects in fMRI, we investigated the corticostriatal correlates of

137 he importance of computational techniques in fMRI analysis, especially machine learning, algorithmic

138 nt for about 15% of the observed variance in fMRI connectivity (and about 10% in alpha-band and 20% i

139 nce costimulation, reproducing the increased fMRI connectivity.

140 INTERPRETATION: Language fMRI is an effective tool for determining language later

141 Interestingly, using a large fMRI sample and meta-analytic tool with Neurosynth, we f

142 In this longitudinal fMRI study of stroke patients (N = 41) with a 6-month fo

143 To test this hypothesis, we measured fMRI BOLD responses to task-irrelevant stimuli acquired

144 at the most widely used neuroimaging method, fMRI, has coarse temporal resolution compared with the t

145 However, most fMRI studies ignored a major part of the brain, the whit

146 ted, N=99) were scanned with functional MRI (fMRI) (N=85), magnetoencephalography (N=33), or both (N=

147 S) [8-12] with resting-state functional MRI (fMRI) [13] to follow both changes in local activity and

148 urgical language mapping are functional MRI (fMRI) and direct cortical stimulation (DCS) of implanted

149 Employing a combination of functional MRI (fMRI) and positron emission tomography (PET), we investi

150 ividuals with PTSD underwent functional MRI (fMRI) at rest and while completing three tasks assessing

151 .4 years [SD=1.9]) underwent functional MRI (fMRI) before and after treatment; similarly, healthy fem

152 sease using an event-related functional MRI (fMRI) experiment design.

153 E STATEMENT In recent years, functional MRI (fMRI) has revolutionized all fields of neuroscience, ena

154 On two occasions, functional MRI (fMRI) scans were performed in the fasted state; the bloo

155 The goal of this functional MRI (fMRI) study was to examine task-induced activation and c

156 ividuals with PTSD underwent functional MRI (fMRI) while completing three tasks assessing emotional r

157 assessed using resting-state functional MRI (fMRI), to predict the onset of depression in adolescents

158 ns by examining the similarity of multivoxel fMRI patterns in visuomotor cortical regions during unil

159 leep monitoring and functional neuroimaging (fMRI) to explore whether trait-like variations in sleep

160 Using multivoxel pattern analysis of fMRI data, we found CS-related neural activation pattern

161 racking and multivariate pattern analysis of fMRI data, we measured participants' dimensional attenti

162 stimuli and multivariate pattern analysis of fMRI data, we show that reward boosts the representation

163 ral sound representations in the analysis of fMRI response patterns.

164 We applied a combination of fMRI, MR spectroscopy, and psychophysics to substantiate

165 More than two decades of fMRI research have revealed crucial insights on multisen

166 of this work was to measure the efficacy of fMRI for predicting whether a dog would be a successful

167 jects also exhibited different magnitudes of fMRI variability across movements in a variety of motor

168 Different from the general linear model of fMRI that predicts responses directly from the stimulus,

169 The high spatial and temporal resolution of fMRI affords detailed mapping of regional pharmacodynami

170 ence have not always matched the richness of fMRI data.

171 The success of fMRI places constraints on the nature of the neural code

172 l stream, are consistent with the success of fMRI, though functional smoothness breaks down in the la

173 ain functional connectivity and synchrony of fMRI activity in healthy humans during wakefulness and s

174 analgesia paradigm with brainstem-optimized fMRI and analysis using a probabilistic brainstem atlas.

175 Single pulse opto-fMRI minimizes adaptation, avoids heating artefacts and

176 ynamic causal modeling (DCM) for optogenetic fMRI experiments-which uniquely allow cell-type-specific

177 Previous studies measured single-cell or fMRI responses and obtained only aggregate measures that

178 that elicit larger broadband, multiunit, or fMRI responses are much less predictive of seizure activ

179 In the second phase, fMRI of response inhibition showed the expected activati

180 Pre-fMRI fullness and liking were rated on visual analog sca

181 A preliminary fMRI study also found disrupted neural responses to pote

182 We then present fMRI data from both sexes, showing that the amygdala tra

183 Previous fMRI studies have found increased activity in frontopari

184 In fact, the most prominent fMRI signal increase in the forebrain was observed in th

185 study were the qualitative and quantitative fMRI cortical activations produced by our population in

186 sing a clinician-administered questionnaire, fMRI during performance of a fractal n-back task, and (1

187 Recent fMRI studies have revealed stimulus-specific patterns of

188 formance Task while undergoing event-related fMRI at 1.5 T.

189 e tested this hypothesis using event-related fMRI in male and female human subjects by manipulating t

190 d graph theoretical analysis to task-related fMRI functional connectivity data from 20 human particip

191 for performing simultaneous high-resolution fMRI and TPM imaging at ultrahigh magnetic field.

192 We acquired high-resolution fMRI at high field (7T), testing for M- and P-influenced

193 These high-resolution fMRI findings suggest that model-free aspects of reward

194 is result indicates that standard-resolution fMRI may lead to wrong conclusions about the functional

195 ting network patterns in whole-brain resting fMRI data, where networks are defined as graphs of inter

196 Resting-state (rs)-fMRI and diffusion weighted imaging (DWI) scans were und

197 tions of electrophysiological signals and rs-fMRI via a new neuromodulation paradigm, which exploits

198 For each sliding window-based rs-fMRI sub-series, we construct a whole-brain associated h

199 is (TSA) approach for resting-state fMRI (rs-fMRI) blood oxygenation level-dependent (BOLD) data in d

200 Resting state fMRI (rs-fMRI) is commonly used to study the brain's intrinsic ne

201 connectivity (FC) is resting-state fMRI (rs-fMRI).

202 Here, we compared FC derived from rs-fMRI in female mice with the underlying monosynaptic str

203 ed to resting state networks derived from rs-fMRI.

204 te functional magnetic resonance imaging (rs-fMRI) time series between any pair of brain regions, sim

205 synchronized tACS significantly increased rs-fMRI connectivity within the stimulated RSN compared wit

206 It has been hypothesized that slow rs-fMRI oscillations (<0.1 Hz) are driven by underlying ele

207 The rs-fMRI sequence was repeated immediately afterward.

208 he results employ the readout of large-scale fMRI networks and, by indicating multiple functional dom

209 Simultaneous fMRI recordings revealed that the same subjects also exh

210 tion time were examined, and a sophisticated fMRI analytic approach was used to quantify precisely tr

211 Specifically, fMRI showed that in misophonic subjects, trigger sounds

212 Resting state fMRI (rs-fMRI) is commonly used to study the brain's int

213 ft analysis (TSA) approach for resting-state fMRI (rs-fMRI) blood oxygenation level-dependent (BOLD)

214 unctional connectivity (FC) is resting-state fMRI (rs-fMRI).

215 reviously reported, seed-based resting-state fMRI analyses revealed that the LC was functionally conn

216 Here we use resting-state fMRI data from 176 subjects to show that signals from th

217 We analyse resting-state fMRI data from 98 healthy adults previously categorized

218 -landscape analysis applied to resting-state fMRI data.

219 udying brain organization with resting-state fMRI in the future.

220 ed strategy to the analysis of resting-state fMRI measurements-a prototypic data modality that exhibi

221 hich occurs unavoidably during resting-state fMRI scans and may induce variability of the acquired da

222 All the subjects underwent resting-state fMRI scans and the data were analyzed by the fALFF appro

223 Resting-state fMRI was administered to 33 cirrhotic patients with MHE

224 connectivity (FC) derived from resting-state fMRI with gold standard structural connectivity measures

225 twork connectivity measured by resting-state fMRI, even in the absence of hypometabolism (measured wi

226 ticular thought domains during resting-state fMRI.

227 as evidenced by task-based and resting-state fMRI.

228 of locomotor adaptation using resting-state fMRI.

229 rticles were contacted to obtain statistical fMRI maps.

230 A subsequent fMRI-guided transcranial magnetic stimulation experiment

231 By applying ultra-high-resolution 7 T fMRI, we delineated the functional contribution of indiv

232 ntrols completed a right-hand, paced tapping fMRI paradigm.

233 ne with invasive studies, a previous 3 Tesla fMRI study suggests that the human bSTC is also topograp

234 Results from this study demonstrated that fMRI may play an important role in providing complementa

235 on information in fMRI and demonstrates that fMRI also reflects fine-grained patterns of neuronal sel

236 We found that fMRI pattern-based signatures of reward identity in late

237 f three complementary analyses revealed that fMRI response patterns were similar across right and lef

238 We show that fMRI response patterns in the motor cortices are similar

239 The fMRI activity in visual cortical regions contralateral t

240 The fMRI analysis showed that BL reduced responses to negati

241 The fMRI results paralleled those recently reported during t

242 Then, we compared the fMRI signal reflecting WM maintenance between hemispheri

243 In the fMRI, the cortical representations of the two digits cos

244 These fMRI variability magnitudes were also consistent across

245 In this fMRI study, participants voluntarily decided if they wan

246 In this fMRI study, we attempted to translate these findings to

247 In this fMRI study, we explore the joint involvement of occipita

248 In this fMRI study, we instantiated reliable unaware visual perc

249 In this fMRI study, we instantiated unaware visual perception co

250 Using real-time fMRI neurofeedback, we directly trained three brain node

251 We applied multivariate pattern analysis to fMRI data to investigate aspects of familiarity that are

252 Quality pre and post treatment fMRI data were collected from 16 of 19 patients.

253 ok two types of memory test while undergoing fMRI scanning.

254 21; mean age, 15.2 years [SD=2.4]) underwent fMRI on two occasions.

255 d in the human brain, participants underwent fMRI while learning about slot machines yielding hidden

256 ry team (i.e., Eagles or Rattlers) underwent fMRI while categorizing political and arbitrary in-group

257 In the present study, we use fMRI to examine both the multiple-demand and sensory-bia

258 Here we use fMRI to relate changes in single voxel receptive fields

259 We applied a commonly used fMRI-stress task in 80 healthy participants.

260 rons have yet to be reported in vivo We used fMRI and population receptive field (pRF) mapping to dem

261 We used fMRI and transcranial magnetic stimulation to investigat

262 Here, we used fMRI in combination with multivariate classification tec

263 Here, we used fMRI to directly test this assumption by comparing the B

264 We used fMRI to investigate neural correlates of responses to er

265 We used fMRI to investigate whether rules at different hierarchi

266 In this study, we used fMRI to map the BOLD response in a recently developed pu

267 We used fMRI to test the hypothesis that the human visual system

268 Using fMRI and multivariate pattern analysis, in three experim

269 Using fMRI and multivoxel pattern analysis, we searched for br

270 Using fMRI in humans and a simulation of London (UK), here we

271 Using fMRI, we found that the 'empathy for pain' network was i

272 Using fMRI, we found the typical selective hand-tool overlap (

273 Using fMRI, we identified several areas of parietal, occipitot

274 Using fMRI, we identify a cerebellar area that is active when

275 Using fMRI, we measured cortical responses to time-varying sti

276 Using fMRI, we showed that the pupil-linked bias reduction was

277 Preoperative mapping of language areas using fMRI greatly depends on the paradigms used, as different

278 nectivity and dynamic network features using fMRI data and an anatomically constrained Kuramoto model

279 Here, using fMRI and an ideal-observer analysis, we demonstrate that

280 gnition, and we tested this hypothesis using fMRI task-activation patterns.

281 ly varied ITD or ILD cues was measured using fMRI during spatial and nonspatial listening tasks.

282 during reward anticipation and outcome using fMRI (planned before data collection).

283 f the development of the visual system using fMRI are primarily confined to a subset of the visual sy

284 e phenomena in the human visual system using fMRI.

285 pocampal activity during a memory task using fMRI and subsequent longitudinal change in Abeta using P

286 neural responses to the stimulus from which fMRI responses are derived.

287 k) or old versus new (retrieval task), while fMRI and eye tracking data were recorded.

288 shocks while we recorded brain activity with fMRI.

289 ds while measuring their brain activity with fMRI.

290 implications, because it can be applied with fMRI to decipher neural computations in millisecond reso

291 l magnetic stimulation (TMS) concurrent with fMRI to examine whether predictive activation patterns r

292 al region for smoking behavior as found with fMRI data.

293 s what can be successfully investigated with fMRI.

294 tic face-detection task and combined it with fMRI-guided pharmacological inactivation of ML to test w

295 slow hemodynamic oscillations measured with fMRI relate to electrophysiological processes.

296 ault mode network (DMN) can be measured with fMRI when subjects shift thoughts between high-level int

297 hip to neural signals in dACC, measured with fMRI, and cognitive task performance.

298 lk of research addressing this question with fMRI has relied upon recognition memory for materials en

299 the youngest sample of children tested with fMRI to date.

300 Neurons within fMRI-defined face patches of the macaque brain exhibit s