YNIMG-12104; No. of pages: 11; 4C: 3, 5, 6, 7, 8, 9 NeuroImage xxx (2015) xxx–xxx

Contents lists available at ScienceDirect

NeuroImage journal homepage: www.elsevier.com/locate/ynimg

3Q2

R. Matthew Hutchison a,b,c,⁎, Nikoo Hashemi c,d, Joseph S. Gati c, Ravi S. Menon c,d, Stefan Everling c,d,e

4 5 6 7 8

a

9

a r t i c l e

10 11 12 13

Article history: Received 4 December 2014 Accepted 23 March 2015 Available online xxxx

14 15 16 17 18 19

Keywords: Cross-frequency coupling Functional connectivity fMRI Local field potentials Macaque

O

F

2

Electrophysiological signatures of spontaneous BOLD fluctuations in macaque prefrontal cortex Department of Psychology, Harvard University, Cambridge, MA, USA Center for Brain Science, Harvard University, Cambridge, MA, USA Robarts Research Institute, University of Western Ontario, London, ON, Canada d Neuroscience Graduate Program, University of Western Ontario, London, ON, Canada e Department of Physiology and Pharmacology, University of Western Ontario, London, ON, Canada b

a b s t r a c t

P

i n f o

R O

c

E

D

Spontaneous brain activity is ubiquitous across brain structures and states. Determining the role of these metabolically costly intrinsic events may be critical for understanding the brain's fundamental physiological principles that govern cognition and behavior. To date, most investigations of large-scale fluctuations and their coupling have been conducted using electro- or magneto-encephalography, modalities that are limited in their ability to spatially resolve the origin of the signals. Invasive, electrophysiological local field potential (LFP) recordings are limited in their spatial range and studies combining the approach with functional imaging have been primarily relegated to sensory/motor areas with little basis in which to extrapolate findings to evolutionarily newer prefrontal cortical regions. Here, we acquired spontaneous fMRI data in two anesthetized macaque monkeys (Macaca fascicularis) at 7 T together with simultaneous recordings of intracortical LFPs recorded bilaterally from the prefrontal cortex (area 9/46d). High (beta–low gamma) and low (delta–theta) band-limited power (BLP) ranges of the LFP frequencies were anticorrelated in the absence of any explicit stimuli. Beyond the high LFP–BLP signal being correlated with BOLD activity at the recording site, the high and low LFP–BLP envelopes were shown to be significantly correlated with spontaneous BOLD activity recorded from positively and negatively connected prefrontal network regions, respectively. The results suggest that complementary changes in low and high frequency bands may be an intrinsic property of LFPs, that local prefrontal cortical activity is related to spontaneous BOLD fluctuations, and further, that LFP–BLPs may be correlated at a network level. © 2015 Elsevier Inc. All rights reserved.

R

R

E

C

T

1Q1

Introduction

42 43

Spontaneous brain activity has been an area of active research across the neuroscience community since its initial detection in animals in the late 1800s (Caton, 1875, 1877) and the later confirmation of its existence in humans in the early 20th century (Berger, 1929; Gloor, 1969). It is now recognized that processing in the brain is carried out in the dynamics of multi-scale network interactions with spontaneous activity shaping ongoing neural and cognitive processes and contributing to overt behavior (Kelso, 1995; Vogels et al., 2005; Tognoli and Kelso, 2009; Ringach, 2009; von von der Malsburg et al., 2010; Rabinovich et al., 2012). Recently, a renaissance of sorts has emerged in evaluating intrinsic fluctuations at the large-scale. The slow varying activity can be captured using multiple modalities, but because of its superior spatial resolution, has been dominated by BOLD contrast MR imaging. Evaluation of the coupling of intrinsic BOLD signals measured in the absence

46 47 48 49 50 Q4 51 52 53 54 55

U

44 45

N C O

41

⁎ Corresponding author at: Center for Brain Science, Harvard University, Northwest Building—East Wing, 52 Oxford Street, Cambridge, MA 02138, USA. E-mail address: [email protected] (R.M. Hutchison).

20 21 22 23 24 25 26 27 Q3 28 29 30 31 32 33 34 35 36 37

of an explicit task (though present across multiple conditions and states) has proven to be instrumental in the characterization of distributed networks, providing critical insights into the mammalian brain's functional architecture (Greicius et al., 2003; Beckmann et al., 2005; Yeo et al., 2011; Buckner et al., 2011; for reviews, see Fox and Raichle, 2007; Raichle, 2011; Menon, 2011; Buckner et al., 2013). However, the low frequency BOLD signals used in these analyses are only a proxy for the ongoing neural activity, instead capturing complex changes in regional metabolism and hemodynamics (Logothetis, 2003). If we are to understand the possible roles of functional coupling in normal brain function and inthe disruption of disease, then it is first necessary to elucidate the relationship between these two signals (Laufs, 2008; Leopold and Maier, 2012; Schölvinck et al., 2013). While common features can be qualified between electrophysiological and hemodynamic signals (Nir et al., 2007, 2008; He et al., 2008), their simultaneous acquisition can provide the most direct assessment of the neural processes that underlie spontaneous BOLD-fMRI signals. Studies using electroencephalography (EEG)–fMRI in humans have shown a complex and oftentimes conflicting relationship between frequency bandwidths, resting-state fluctuations, and their correlational

http://dx.doi.org/10.1016/j.neuroimage.2015.03.062 1053-8119/© 2015 Elsevier Inc. All rights reserved.

Please cite this article as: Hutchison, R.M., et al., Electrophysiological signatures of spontaneous BOLD fluctuations in macaque prefrontal cortex, NeuroImage (2015), http://dx.doi.org/10.1016/j.neuroimage.2015.03.062

56 57 58 59 60 61 62 63 64 65 66 Q5 67 68 69 70 71 72 73 74 75

124 125

Animal implantation All surgical and experimental procedures were carried out in accordance with the Canadian Council of Animal Care policy on the use of laboratory animals and approved by the Animal Use Subcommittee of the University of Western Ontario Council on Animal Care. Data was collected from two adult macaque monkeys (Macaca fascicularis; Monkey 1: male, 9.5 kg, Monkey 2: female, 6.1 kg). Animals were bilaterally implanted (N5 weeks before the first scan) with MRI-compatible 16-channel multi-electrode laminar arrays with 150 μm spacing (A1 × 16-5 mm-150-703-MR_CM16, NeuroNexus, Ann Arbor, MI) in prefrontal area 9/46d. A bone flap was removed to allow access to the cortex in the caudal portion of the principal sulcus. The dura was cut and reflected to expose the cortex and visualize the posterior principal sulcus and upper arm of the arcuate sulcus to ensure correct placement of the electrode in area 9/46d (later verified in the anatomical images).

101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119

126 127 128 129 130 131 132 133 134 135 136 137 138

C

99 100

E

97 98

R

95 96

R

93 94

O

91 92

C

89 90

N

87 88

U

85 86

Animal preparation In preparation for image acquisition, monkeys were injected intramuscularly with atropine (0.5 mg/kg) and acepromazine (0.02 mg/kg mg/kg) followed by intramuscular of ketamine hydrochloride (7.5 mg/kg) and medetomidine (0.0125 mg/kg) and then intravenous administration of 2.5 mg/ml of propofol via the saphenous vein. Animals were then intubated and switched to 1.5% isoflurane mixed with oxygen. Each monkey was then placed in a custom-built monkey chair with its head immobilized using the head post, and inserted into the magnet bore, at which time the isoflurane level was lowered to 1%. At least 30 min was allowed for the isoflurane level and global hemodynamics to stabilize at the 1% concentration, during which the image localization, shimming, test EPIs, and anatomical acquisitions were performed. Animals were spontaneously ventilating throughout the duration of scanning and physiological parameters [temperature (36–37 °C), oxygen saturation (92–100%), heart rate (110–130 bpm), respiration (30–40 bpm), and end-tidal CO2 (24– 28 mm Hg)] were monitored to ensure values were within normal limits.

148

F

Animals

83 84

O

123

82

R O

Materials and methods

80 81

139 140

P

122

78 79

References and ground wires were placed on the dura underneath the skull, and the electrode was lowered under visual guidance while simultaneously monitoring the electrophysiological recordings to ensure placement of the electrode across cortical layers. The dura was drawn back and the bone defect was repaired with the previously removed bone flap and gel foam. Additionally, a custom-built acrylic head post was implanted to allow restraint of the head during data collection. Implants were anchored to the skull with 6-mm ceramic bone screws (Thomas Recording, Giessen, Germany) and dental acrylic.

141 142 143 144 145 146 147

149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166

MRI acquisition Monkeys 1 and 2 were scanned in 3 sessions each (separated by N10 days). Two sessions from Monkey 1 had to be discarded due to problems with gradient artifact removal in the local field potential recordings. Data were acquired on an actively shielded 7-T 68-cm horizontal bore scanner with a DirectDrive console (Agilent, Santa Clara, California) with a Siemens AC84 gradient subsystem (Erlangen, Germany) operating at a slew rate of 250 mT/m/s. An in-house designed and manufactured conformal 5-channel transceive primatehead RF coil was used for all experiments. Magnetic field optimization (B0 shimming) was performed using an automated 3D mapping procedure over the specific imaging volume of interest. Each functional run consisted of 150 continuous echo-planar imaging (EPI) functional volumes (TR = 2000 ms; TE = 18 ms; flip angle = 60°; slices = 40; matrix = 96 × 96; FOV = 96 × 96 mm; acquisition voxel size = 1 mm3). Acquisition time of each scan was 5 min and the number of runs ranged between 12 and 15 per session. EPI images were acquired with GRAPPA at an acceleration factor of 2. Every image was corrected for physiological fluctuations using navigator echo correction. A highresolution gradient-echo T2 anatomical image was acquired along the same orientation as the functional images (TR = 1100 ms, TE = 8 ms, matrix = 256 × 256, FOV = 96 × 96 mm, acquisition voxel size = 375 μm × 375 μm × 1000 μm). T1-weighted MP2RAGE anatomical images (TE = 2.5 ms, TR = 6250 ms, TI1 = 900 ms, TI2 = 2750 ms, FOV = 128 × 128 mm, acquisition voxel size = 750 μm3) were also acquired and averaged.

167 168

LFP acquisition LFPs were recorded continuously during fMRI acquisition using a BrainAmp MR + amplifier and BrainVision Recorder software (Brainproducts, Gilching, Germany). Signals were sampled at 5 kHz, band-pass filtered between 0.1 and 250 Hz.

193 194

T

120 121

network structures (e.g., Laufs et al., 2003; Ritter and Villringer, 2006; Mantini et al., 2007; Britz et al., 2010; Musso et al., 2010). The results suggest that contributions from frequency bands (and their bandlimited power [BLP]) vary across networks and even time and states (Tagliazucchi et al., 2012; Chang et al., 2013). To allow for greater spatial resolution and avoid signal dampening of higher frequencies from the dura and skull, intracranial strategies have also been employed. Animal models are most applicable here as they circumvent the safety concerns associated with an invasive surgery in human subjects. While select patient populations may undergo invasive recordings for diagnostic purposes, the location, type, and option of simultaneous recording is limited by the needs of the patient. The most convincing evidence for a link between spontaneous fMRI fluctuations and neural activity came from simultaneously acquired electrophysiological and fMRI data in anesthetized macaque monkeys that suggested both gamma-range LFP and spiking to be the primary contributor (Shmuel and Leopold, 2008), a finding that is gaining increasing support (Magri et al., 2012). However, reports of positive contributions from lower frequencies have also been shown (Lu et al., 2007; Schölvinck et al., 2010; Pan et al., 2011; Wang et al., 2012). Interestingly, Schölvinck et al. (2010) demonstrated that neurovascular coupling to the locally measured LFP signal was also prominent throughout the entire cortex (across occipital, parietal, and frontal lobes) within the gamma-range BLP in resting monkeys suggesting LFP–BLP signals, like low-frequency BOLD signals are also correlated at a large-scale level. With the exception of Schölvinck et al. (2010) there are limited reports of LFP–BOLD investigations that have recorded in areas other than sensory and motor cortex. Likely owing to the detailed understanding of the systems, the ease of access of the recording sites, and a rich literature of task-based correlate investigations where activation of these areas is easy to elicit, macaque and rat investigations have primarily recorded from visual and somatosensory cortex, respectively. This makes it difficult to ascertain whether BOLD–LFP relationships can be extrapolated to areas of evolutionarily expanded association cortex such as prefrontal cortical regions. To address this, we investigated the neurovascular coupling in prefrontal area 9/46d, a portion of the dorsolateral prefrontal cortex which lies just above the caudal portion of the principal sulcus in macaques and on the dorsal portion of the middle frontal gyrus in humans (Petrides and Pandya, 1999). It is often implicated in higher-order and executive functions, participating as a member of distributed frontoparietal and control networks (Yeo et al., 2011; Power et al., 2011). LFPs were recorded using bilaterally implanted 16-channel multi-electrode laminar arrays that were collected simultaneously with high-resolution BOLD data in a 7 T MR scanner.

D

76 77

R.M. Hutchison et al. / NeuroImage xxx (2015) xxx–xxx

E

2

169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192

195 196 197

Image preprocessing 198 Functional image preprocessing was implemented in the FMRIB Soft- 199 ware Library toolbox (FSL; http://www.fmrib.ox.ac.uk). This consisted 200

Please cite this article as: Hutchison, R.M., et al., Electrophysiological signatures of spontaneous BOLD fluctuations in macaque prefrontal cortex, NeuroImage (2015), http://dx.doi.org/10.1016/j.neuroimage.2015.03.062

R.M. Hutchison et al. / NeuroImage xxx (2015) xxx–xxx

221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256

F O R O

219 220

P

217 218

D

215 216

Seed-based analysis Spherical (radius = 1.5 mm) regions-of-interest (ROIs) were manually selected to encompass the area surrounding the electrodes placed within area 9/46d of the prefrontal cortex and in selected areas (frontal eye field (FEF), area 7 m, area TEO, primary auditory cortex). ROIs not within the PFC were placed at the same coordinates for both monkeys within the standard F99 atlas space. In Monkey 2, the electrode connectors caused local MR artifacts (see Suppl. Fig. 1), which prevented us from selecting seeds in the vicinity of the electrodes in area 9/46d. The electrode connectors in this animal had to be replaced before the first scan due to damage from the animal's cage mate, and the new connectors were not fully MR-compatible. The mean timecourse for each ROI was extracted for every scan of each animal and the extracted timecourses were then used as predictors in a model for multiple regression at the individual scan level in which nuisance covariates for white matter (WM) and cerebrospinal fluid (CSF) were included. The global signal was not included as a covariate. There are concerns that a WM mask can contain gray matter voxels if not sufficiently degraded or contain gray matter signal through spatial smoothing. We calculated the correlation between WM, CSF, and global signals across scans and animals. The average correlation of WM and CSF (r = 0.42), WM and global signal (r = 0.19), and CSF and global signal (r = −0.09) showed that these potential effects are minimal. The first level regression was followed by a second level fixed-effects analysis to calculate the significantly connected voxels shared between the scans. Corrections for multiple comparisons were implemented at the cluster level with Gaussian random field theory (z N 2.3; cluster significance: p b 0.05, corrected). The thresholded z-statistic maps representing brain regions significantly correlated with each seed region were then projected from volume data to the F99 cortical surface with the CARET (http://www.nitrc.org/ projects/caret) enclosed-voxel method (Van Essen et al., 2001).

E

213 214

T

212

C

210 211

E

208 209

R

207

R

205 206

N C O

203 204

of motion correction (6-parameter affine transformation), brain extraction, spatial smoothing (Gaussian kernel of full-width at half maximum 3 mm applied to each volume separately), high-pass temporal filtering (Gaussian-weighted least-squares straight line fitting with sigma = 100 s), and low-pass temporal filtering (half-width at half maximum = 2.8 s, Gaussian filter). Functional data were nonlinearly registered to the T2 anatomical (FNIRT; http://www.fmrib.ox.ac.uk/ fsl/fslwiki/FNIRT), and then registered to the T1 anatomical (6 DOF rigid transformation), and finally normalized (12 DOF linear affine transformation) to the F99 atlas template (Van Essen, 2004); see http://sumsdb.wustl.edu/sums/macaquemore.do). Global signal regression was not applied.

LFP analysis Offline MRI artifact correction was performed based on the average artifact subtraction (AAS) method as implemented in Vision Analyzer2 (Brain Products, Germany). After downsampling to 1000 Hz, bandlimited power (BLP) was computed by band-pass filtering the signal of each contact electrode in successive frequency bands (1:100, in 5 Hz bins), rectifying the signal, and resampling it every 2 s (by averaging the BLP in the 2 s). The BLP time series were convolved with a standard double-gamma hemodynamic response function (HRF) to account for the hemodynamic lag (see Fig. 1). Convolved BLP time series were correlated with the average BOLD time series of significantly (z N 6 and z b − 3) connected regions from the Area 9/46d seedanalysis as well as individual ROIs. Based on these analysis, the average

U

201 202

3

Fig. 1. Analysis pipeline of the simultaneous recorded LFP data. The top row displays a schematic of the electrode location relative to the arcuate (as) and principal (ps) sulcus (left) and its location in F99 template volume space (right). The yellow circle represents the spherical seed region used in the BOLD analysis. The workflow below displays the LFP processing steps. Following removal of the gradient-induced artifacts, the raw LFP signal was band-pass filtered in different frequency bands (1:100, in 5 Hz steps), rectified, resampled, and then convolved with a standard double-gamma hemodynamic response function (HRF) to account for the hemodynamic lag.

Please cite this article as: Hutchison, R.M., et al., Electrophysiological signatures of spontaneous BOLD fluctuations in macaque prefrontal cortex, NeuroImage (2015), http://dx.doi.org/10.1016/j.neuroimage.2015.03.062

275

To derive a whole-brain positive and negative network BOLD time series, the average timecourse of regions functionally connected to the area 9/46d ROI seed (immediately surrounding the electrode) was calculated for each animal for positively (z N 2.3; cluster significance: p b 0.05, corrected) and negatively (z b − 2.3; cluster significance: p b 0.05, corrected) connected voxels, respectively. Note that the network map from Monkey 1 was used for Monkey 2 to derive the average positive and negative network BOLD timecourses due to the artifacts around the electrode connector in this animal (see Materials and Methods, Seed-based Analysis). The electrode seed was highly correlated with the average positive-network BOLD signal (r ± SE = 0.65 ± 0.03). To avoid potential concerns related to signal drop out near the electrode and ROI selection bias, we used the average network time series in subsequent analysis unless otherwise noted. Binned BLP time series were highly correlated between interhemispheric electrodes (not shown) and across the 150 μm spaced contacts, showing similar relationships with the extracted BOLD signal from area 9/46d (Fig. 2a). Further analyses were carried out using the electrode with the highest signal power of each animal (e.g., dashed rectangle in Fig. 2a) across all sessions. The BLP envelopes recorded with the intracortical electrode were highly correlated across a large range of higher frequencies (10–50 Hz) and negatively correlated with lower frequencies (b10 Hz) (Fig. 2b). The average seed-derived BOLD network time series showed broad positive correlation with higher BLP bins (Figs. 2c,d, red) and a negative relationship with the low frequencies (Figs. 2c,d inset). The BOLD time series derived from regions negatively correlated with area 9/46d was positively correlated with low BLP in Monkey 1 and only weakly correlated with any bin in Monkey 2. This relationship was also observed when using individual region seeds as opposed to the entire networkaveraged pattern including the electrode seed (Fig. 3). Based on these analyses, the spectrum was divided into two broad BLP bins for further analysis, a “high” (16–44 Hz, beta–low gamma) and a “low” (2–7 Hz, delta–theta) bin. Fig. 2e displays representative traces of the convolved high and low frequency bins with the network-derived BOLD signal timecourse, illustrating the strong temporal coupling between higher LFP power and BOLD and the negative relationship with low LFP power envelops. The network pattern from the BOLD seed-region analysis of 9/46d from Monkey 1 is shown on the cortical surface in Fig. 4c and in volume space in Fig. 5 (first column). The analysis (irrespective of the hemisphere in which the seed was chosen) revealed strong, bilateral, and distributed positive FC that included areas within and around the principal sulcus, medial and lateral parietal lobe including the posterior cingulate, retrosplenial cortex, intraparietal sulcus, and areas in the

286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319

C

284 285

E

282 283

R

280 281

R

278 279

O

276 277

C

271 272

N

269 270

U

267 268

Discussion

344

F

Results

265 266

O

274

263 264

322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343

Local and distributed LFP–BOLD correlates in PFC

345

Using a simultaneous intracranial LFP and BOLD imaging approach, we found that local and network level prefrontal BOLD signals were correlated with LFP–BLP envelopes recorded from the area 9/46d electrode. Correlations were not solely restricted to gamma BLP as has been shown in some studies (Shmuel and Leopold, 2008; Keller et al., 2013), but instead across the beta–low gamma range (Mantini et al., 2007; Schölvinck et al., 2010; Magri et al., 2012; Wang et al., 2012). In a field of research that has conflicting reports on whether low, high, or even infraslow activity is preferentially related to spontaneous BOLD activity and it's functional relationships, interpreting whether the cut offs of the LFP–BLP bands reported here are specific to prefrontal cortex is difficult. In this and other cases of limitations discussed below, a greater number of recording electrodes distributed across the cortex are needed to better address these issues and should be the aim of future investigations. However, it is still the case that BOLD and LFP–BLP signals are strongly correlated and suggestive of coupling mechanisms that resemble those seen in sensory and motor cortex. Unfortunately, the signal around the electrodes in Monkey 2 were contaminated with artifacts, however, a network time series was still able to be extracted from the broader PFC-seed derived network pattern of Monkey 1, shown to be consistent in a larger cohort of animals (and also in Monkey 2 using a seed region in the anterior bank of the arcuate sulcus). This average network time series (or of that derived from individual network regions) was correlated in both animals across the same broad spectra of BLP. Critically, it was not the case that the specific power envelopes were globally correlated across cortical voxels that would then imply a relationship with the global signal (Schölvinck et al., 2010) as was observed previously in awake rhesus macaques injected with monocrystalline iron oxide nanoparticles (MION), but instead they showed highly specific spatial patterns that predominantly reflected anatomically connected regions of area 9/46d. Across the cortex, the positive high BLP-LFP (red, Fig. 6a) and negative low BLP-LFP (green, Fig. 6a) time series each had more extensive voxel-wise correlations than the BOLD timecourse extracted from the region from which they were recorded — a finding that would have not initially been predicted given the shared signal type and properties of the seed and the rest of the brain. This could indicate that the convolved BLP-LFP signals

346

T

273

Group maps To explore the reproducibility of the individual monkey's correlation maps and compare the FC pattern to humans, seed-based analysis was also performed in an independent group of monkeys (n = 11; Hutchison and Everling, 2013) and human subjects (n = 23; Hutchison et al., 2014a) from previously published data. Acquisition and processing are described therein. Note that global signal regression was not performed in either data set. The electrode seed location from Monkey 1 was used for the monkey data and a group average was calculated as previously described (Hutchison et al., 2012). In humans, the homologous area of area 9/46d was selected within the dorsolateral prefrontal cortex on the middle frontal gyrus (Petrides and Pandya, 1999) in MNI space (X/Y/Z = 38/22/42) and the group average correlation map calculated.

261 262

320 321

R O

260

superior temporal sulcus. The pattern is consistent with our earlier work in a larger sample of monkeys (caudal cluster in Hutchison and Everling, 2013) and reflective of results from tract-tracing investigations (Petrides and Pandya, 1999). Significant anticorrelations were found in the insula, regions within the cingulate sulcus, and subgenual cortical areas (32, 32/25) that closely resemble patterns for another broad functional division of the PFC (lateral cluster in Hutchison and Everling, 2013) and subgenual division of the anterior cingulate (limbic cluster in Hutchison et al., 2012). Some of these anticorrelated areas (insula, cingulate area 32) are also monosynaptically connected with area 9/46d (Petrides and Pandya, 1999). Both positive and negative functional connectivity patterns closely matched that derived from a homologous seed region analysis in a sample of awake human subjects (n = 23; Fig. 4a) and an independent group of macaque monkeys (n = 11; Fig. 4b). To visualize the correspondence between the high and low BLP envelopes and area 9/46d defined whole-brain network pattern, the two broad HRF-convolved BLP envelopes were used in a voxel-wise regression analysis with the BOLD runs (Figs. 4 and 5). As seen in Fig. 6, the high LFP connectivity maps matched that derived when using the BOLD time series of the electrode seed. The low frequency power envelope revealed the opposite pattern, being strongly anticorrelated with the positive network pattern from the BOLD analysis and positively coupled to anticorrelated regions.

P

259

BLP time series across multiple broad bins were used in a regression model for FC analysis, as described above for the seed regions, to derive voxel wise connectivity maps.

D

257 258

R.M. Hutchison et al. / NeuroImage xxx (2015) xxx–xxx

E

4

Please cite this article as: Hutchison, R.M., et al., Electrophysiological signatures of spontaneous BOLD fluctuations in macaque prefrontal cortex, NeuroImage (2015), http://dx.doi.org/10.1016/j.neuroimage.2015.03.062

347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382

5

N C O

R

R

E

C

T

E

D

P

R O

O

F

R.M. Hutchison et al. / NeuroImage xxx (2015) xxx–xxx

383 384 385 386 387 388 389 390 391 392 393 394 395 396

U

Fig. 2. Relationship of spontaneous BOLD signals and LFP–BLP envelopes. (a) Correlation of convolved BLP frequency bins for contacts of the intracortical electrode with the BOLD times series for all significant functionally connected voxels of area 9/46d (network time series) over one representative run in session for Monkey 2. The hatched line indicates the channel used for subsequent analysis. Blacked out rows indicate channels in which signals were not reliably recorded. (b) Cross correlation matrix of binned BLP time series from (a). Inset panel shows statistically significant correlations (black, p b 0.05). (c,d) The correlation of BLP 5-Hz bins with average positive (red) and negative (blue) network time series for each monkey. Error bars represent standard error across all runs. Matched, colored lines along the x-axis indicate statistically significant correlations of each line (p b 0.05). Note that the network pattern of Monkey 1 was used to derive the BOLD time series of Monkey 2. The inset image shows a zoomed in view of the lower frequencies bins. (e) Representative time series from BOLD, high LFP–BLP, and low LFP–BLP signals for two runs.

are a better estimate of network activity in the recorded region, the BOLD signal being more susceptible to physiological and hardware artifacts that could cause local distortions and impact the whole-brain regression. It could also suggest that through preprocessing and convolution with the HRF that the BLP-LFP time series has lost specificity and is more globally correlated. Whether either of these are this case will require further investigation, though the striking overlap does suggest that we have a good measure of network activity. Widespread coherence in the fluctuations in various frequency power envelopes measured with different modalities has been reported previously, with evidence of functional specificity — showing a moderate degree of correspondence to commonly reported resting-state (or intrinsic connectivity) networks (Nir et al., 2008; He et al., 2008; De Pasquale et al., 2010, 2012; Scheeringa et al., 2011; Brookes et al.,

2011a,b; Hipp et al., 2012; Wang et al., 2012). Given the broad frequency range of BOLD correlates and mixed findings across previous studies in terms of the frequency correlate of the spontaneous BOLD fluctuations and distributed functional interactions, it is likely that there are network-specific positive and negative coupling fingerprints (Mantini et al., 2007).

397

Anticorrelation of high and low LFP–BLP envelopes The delta–theta and beta–low gamma power envelopes are anticorrelated. Relationships between LFP–BLP can take various forms including phase and amplitude coupling (for reviews, see Jensen and Colgin, 2007; Siegel et al., 2012; Buzsáki and Watson, 2012). High amplitude, low frequency fluctuations have also been shown to modulate the power of lower amplitude, high frequency signals. Therefore, it

403 404

Please cite this article as: Hutchison, R.M., et al., Electrophysiological signatures of spontaneous BOLD fluctuations in macaque prefrontal cortex, NeuroImage (2015), http://dx.doi.org/10.1016/j.neuroimage.2015.03.062

398 399 400 401 402

405 406 407 408 409

R.M. Hutchison et al. / NeuroImage xxx (2015) xxx–xxx

U

N

C

O

R

R

E

C

T

E

D

P

R O

O

F

6

Fig. 3. Correlation of LFP–BLP bins with BOLD time series of positively (prefrontal cortex (PFC), FEF, 7 m, TEO) and negatively (primary auditory cortex, Aud) connected areas of the ipsilateral (solid line) and contralateral (dashed line) hemisphere (relative to the electrode location) for both monkeys. The seed locations are displayed on Monkey 1's area 9/46d connectivity map that is overlaid on the F99-template volume.

Please cite this article as: Hutchison, R.M., et al., Electrophysiological signatures of spontaneous BOLD fluctuations in macaque prefrontal cortex, NeuroImage (2015), http://dx.doi.org/10.1016/j.neuroimage.2015.03.062

R.M. Hutchison et al. / NeuroImage xxx (2015) xxx–xxx

7

410 411

Potential correlates of antagonistic networks relationships

426

R O

O

F

was not a requirement that the lower and higher frequencies be anticorrelated with one another. Previous evidence has suggested though, that this relationship exists in response to stimulus presentation (Fries et al., 2001; Nir et al., 2007; Scheeringa et al., 2011; Hwang and Andersen, 2011). Hwang and Andersen (2011) for example found the trial-by-trial fluctuation of LFP–BLP in response to the variation of a reach goal was anticorrelated between the lower (b 20 Hz) and higher frequency (N40 Hz) bands in monkeys. They did not find the correlation during the fixation period (0.5-s interval before stimulus onset), however, over longer recording durations it appears that that complementary changes in the low and high frequency bands are an intrinsic property of LFPs. Anesthetized recordings of cat primary visual cortex during constant stimulus intensity have also revealed a strong positive correlation between hemodynamic activity and neuronal synchronization in the gamma frequency range (N30 Hz) with a negative correlation of low frequency bands (b7 Hz) (Niessing et al., 2005).

U

N C O

R

R

E

C

T

E

D

P

While positive FC has been the primary focus of most investigations to date, negative interactions – regions anticorrelated with the time series of interest – are also observed, albeit often not interpreted. The most robust example of reliable antagonistic network relationships is between the default and dorsal-attention network regions. The dichotomy between these networks has been speculated to serve a functional role, segregating neuronal processes that subserve opposite goals or competing representations (Fox et al., 2005), a property that likely extends beyond these regions to multiple levels of functional brain organization. In support of this integrity of this negative relationship can bias behavioral variability (Kelly et al., 2008). However, the phenomenon has been challenged on several fronts, particularly by the observation that global signal regression (GSR), a common preprocessing step, shifts the distribution of connectivity values, artificially creating or enhancing negative correlations (Murphy et al., 2009). Despite this, robust and reliable anticorrelated patterns have been shown without GSR (Fox et al., 2009; Hutchison et al., 2012; Keller et al., 2013; Yan et al., 2013b), and there is evidence from multisite unit and local field potential (LFP) recordings in the feline (Popa et al., 2009) and ECoG recordings in humans (Keller et al., 2013) that have pointed toward a possible neural correlate involving modulations in LFP power. EEG–fMRI studies have also shown a negative relationship of BOLD and the lower frequencies (Laufs et al., 2003; Mantini et al., 2007) although this has not always been reproduced (Schölvinck et al., 2010). Beyond the relationship between default and dorsal-attention network regions, anticorrelated brain areas are often inconsistent or difficult to detect in human studies. This could be attributed to their weaker values, motion effects (Yan et al., 2013a), or variations in preprocessing. Further, periods of negative correlations between brain areas appear to be more transient than positive relationships, thereby decreasing their detectability in standard analysis approaches (Chang and Glover, 2010; Hutchison et al., 2013). While speculative given the correlational nature of the current approach, the findings lend support to the notion that antagonistic Fig. 4. BOLD and electrophysiological LFP–BLP regression maps. The top rows display group averaged BOLD FC map of area 9/46d in (a) human subjects (n = 23; z N 2.3; uncorrected), (b) an independent sample of monkeys (n = 11; z N 2.3; cluster significance: p b 0.05, corrected), and (c) in Monkey 1 (z N 2.3; cluster significance: p b 0.05, corrected). The Monkey 2 map is not shown due to poor signal near the electrode site (see Supplementary Fig. 1) and instead, the network pattern from the FEF ROI is displayed. The bottom rows display the voxel-wise correlation of the (d) high (16–44 Hz) and (e) low (2–7 Hz) LFP–BLP bands with the spontaneous BOLD signal for both monkeys (z N 2.3; cluster significance: p b 0.05, corrected). Functional maps are displayed on the lateral and medial inflated cortical surface. Note that the color bar is the same for all monkey maps. Asterisks indicate the seed/electrode location. as, arcuate sulcus; cas, calcarine sulcus; cis, cingulate sulcus; cs, central sulcus; hs, hippocampal sulcus; ifs, inferior frontal sulcus; ios, inferior occipital sulcus; ips, intraparietal sulcus; ls, lateral sulcus; lus, lunate sulcus; mfs, middle frontal sulcus; pocs, posterior central sulcus; pos, parieto-occipital sulcus; prcs, precentral sulcus; ps, principal sulcus; sfs, superior frontal sulcus; sts, superior temporal sulcus.

Please cite this article as: Hutchison, R.M., et al., Electrophysiological signatures of spontaneous BOLD fluctuations in macaque prefrontal cortex, NeuroImage (2015), http://dx.doi.org/10.1016/j.neuroimage.2015.03.062

412 413 414 415 416 417 418 419 420 421 422 423 424 425

427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459

R.M. Hutchison et al. / NeuroImage xxx (2015) xxx–xxx

U

N

C

O

R

R

E

C

T

E

D

P

R O

O

F

8

Fig. 5. Functional maps of BOLD and LFP–BLP signals shown in Fig. 4 displayed in F99 volume space. Monkey 2 BOLD is not shown due to poor signal near the electrode site.

Please cite this article as: Hutchison, R.M., et al., Electrophysiological signatures of spontaneous BOLD fluctuations in macaque prefrontal cortex, NeuroImage (2015), http://dx.doi.org/10.1016/j.neuroimage.2015.03.062

9

R

R

E

C

T

E

D

P

R O

O

F

R.M. Hutchison et al. / NeuroImage xxx (2015) xxx–xxx

461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477

network relationships captured by BOLD FC techniques reflect the ongoing balance of specialization and integration among large-scale functional units (Fox et al., 2005). The positive (higher frequency) interactions could bias the binding of neuronal populations relevant for upcoming internal or external events together, while the slower frequency power envelopes are out of phase, possibly ensuring the opposing regions have a negative weighting toward current involvement.

U

460

N C O

Fig. 6. Spatial overlap of network patterns. Overlaid maps of the binarized (z N 2.3; cluster significance: p b 0.05, corrected) spatial patterns derived from voxel-wise, whole-brain regression analysis using the BOLD seed timecourse and HRF-convolved high (16–44 Hz) and low (2–7 Hz) LFP–BLP frequency bins are displayed on the inflated surface for each monkey. (a) Overlap of voxels from positively connected voxels with the BOLD time series and high frequency bin, and negatively connected voxels with the low bin. (b) Overlap of voxels from negatively connected voxels with BOLD time series and high frequency bin, and positively connected voxels with the low bin. Asterisks indicate the seed/electrode location.

Anesthesia Simultaneous BOLD–LFP recordings were carried out while animals were anesthetized with isoflurane. Anesthetics, including isoflurane, are commonly used in resting-state fMRI investigations of nonhuman primates (e.g., Vincent et al., 2007; Teichert et al., 2010; Mars et al., 2011; Hutchison et al., 2011) affecting networks in a dose dependent manner (Hutchison et al., 2014b), and also when examining the electrophysiological correlates of BOLD activity (Logothetis et al., 2001; Lu et al., 2007; Shmuel and Leopold, 2008; Pan et al., 2011; Wang et al., 2012). They offer motion and stress free recordings of naïve animals in a state of stable vigilance, attention, and arousal. However, isoflurane

has co-occurring effects on cerebral blood flow (CBF), cerebral blood volume (CBV), and metabolic rate, particularly at higher dosages, that can result in neurovascular decoupling (reviewed in Masamoto and Kanno, 2012). Further, anesthetics can alter LFPs, shifting relative power from higher-frequency neural activity to slower oscillations (Franks, 2008; Williams et al., 2010) that with a sufficiently high dose can eventually lead to burst suppression activity. Therefore, we cannot rule out the potential influence of anesthesia on the relationship between two broad neural frequency bands and the spontaneous BOLD activity. It should be noted however, that a relatively low dosage (0.78 minimum alveolar concentration [MAC]; Tinker et al., 1977) was employed, likely preserving CBF autoregulation (Eger, 1984; Li et al., 2013). From the LFP recording, we can determine that burst suppression was not induced and power in high frequency activity was readily observable. Envelope interactions and BOLD-derived network patterns are relatively robust against global state changes such as anesthesia or task-state (Wang et al., 2012; Engel et al., 2013) and the relationship between the two network patterns was observable in a sample of awake human subjects. So while the results must be interpreted with caution,

Please cite this article as: Hutchison, R.M., et al., Electrophysiological signatures of spontaneous BOLD fluctuations in macaque prefrontal cortex, NeuroImage (2015), http://dx.doi.org/10.1016/j.neuroimage.2015.03.062

478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496

R.M. Hutchison et al. / NeuroImage xxx (2015) xxx–xxx

Conclusions

500

509 510

Spontaneous BOLD activity integrated over networks (and locally) in the PFC is correlated with the BLP of LFPs in the beta–low gamma range and anticorrelated with power envelopes in the delta–theta range. The complementary changes in low and high frequency bands may be an intrinsic property of the brain that represents a neural correlate of antagonistic regions/network relationships observed in BOLD FC profiles at rest, however, future work will be needed to establish the origin and functional significance of this large-scale feature of functional brain organization. Supplementary data to this article can be found online at http://dx. doi.org/10.1016/j.neuroimage.2015.03.062.

511

Acknowledgments

512

515

We thank Nicole Hague for the excellent technical assistance. This research was supported by the Canadian Institutes of Health Research (MOP-89785 to S.E.; PRG-165679 to R.S.M.; postdoctoral fellowship to R.M.H.).

516

Conflict of interest

505 506 507 508

513 514

517

The authors declare no conflicts of interest.

D

503 504

References

519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564

Beckmann, C.F., DeLuca, M., Devlin, J.T., Smith, S.M., 2005. Investigations into resting-state connectivity using independent component analysis. Philos. Trans. R. Soc. B Biol. Sci. 360, 1001. Berger, H., 1929. Uber das Elektrenkephalogramm des Menschen. Mitt. Arch. F Psychiat. 87, 527–570. Britz, J., Van De Ville, D., Michel, C.M., 2010. BOLD correlates of EEG topography reveal rapid resting-state network dynamics. NeuroImage 52, 1162–1170. Brookes, M.J., Hale, J.R., Zumer, J.M., Stevenson, C.M., Francis, S.T., Barnes, G.R., Owen, J.P., Morris, P.G., Nagarajan, S.S., 2011a. Measuring functional connectivity using MEG: methodology and comparison with fcMRI. NeuroImage 56, 1082–1104. Brookes, M.J., Woolrich, M., Luckhoo, H., Price, D., Hale, J.R., Stephenson, M.C., Barnes, G.R., Smith, S.M., Morris, P.G., 2011b. Investigating the electrophysiological basis of resting state networks using magnetoencephalography. Proc. Natl. Acad. Sci. U. S. A. 108, 16783–16788. Buckner, R.L., Krienen, F.M., Castellanos, A., Diaz, J.C., Yeo, B.T.T., 2011. The organization of the human cerebellum estimated by intrinsic functional connectivity. J. Neurophysiol. 106, 2322–2345. Buckner, R.L., Krienen, F.M., Yeo, B.T.T., 2013. Opportunities and limitations of intrinsic functional connectivity MRI. Nat. Neurosci. 16, 832–837. Buzsáki, G., Watson, B.O., 2012. Brain rhythms and neural syntax: implications for efficient coding of cognitive content and neuropsychiatric disease. Dialogues Clin. Neurosci. 14, 345–367. Caton, R., 1875. The electric currents of the brain. Br. Med. J. 2, 278. Caton, R., 1877. Interim report on investigation of the electric currents of the brain. Br. Med. J. (Suppl. I), 62–65. Chang, C., Glover, G.H., 2010. Time–frequency dynamics of resting-state brain connectivity measured with fMRI. NeuroImage 50, 81–98. Chang, C., Liu, Z., Chen, M.C., Liu, X., Duyn, J.H., 2013. EEG correlates of time-varying BOLD functional connectivity. NeuroImage 72, 227–236. De Pasquale, F., Della Penna, S., Snyder, A.Z., Lewis, C., Mantini, D., Marzetti, L., Belardinelli, P., Ciancetta, L., Pizzella, V., Romani, G.L., Corbetta, M., 2010. Temporal dynamics of spontaneous MEG activity in brain networks. Proc. Natl. Acad. Sci. U. S. A. 107, 6040–6045. De Pasquale, F., Della Penna, S., Snyder, A.Z., Marzetti, L., Pizzella, V., Romani, G.L., Corbetta, M., 2012. A cortical core for dynamic integration of functional networks in the resting human brain. Neuron 74, 753–764. Eger II, E.I., 1984. The pharmacology of isoflurane. Br. J. Anaesth. 56 (Suppl. 1), 71S–99S. Engel, A.K., Gerloff, C., Hilgetag, C.C., Nolte, G., 2013. Intrinsic coupling modes: multiscale interactions in ongoing brain activity. Neuron 80, 867–886. Fox, M.D., Raichle, M.E., 2007. Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging. Nat. Rev. Neurosci. 8, 700–711. Fox, M.D., Snyder, A.Z., Vincent, J.L., Corbetta, M., Van Essen, D.C., Raichle, M.E., 2005. The human brain is intrinsically organized into dynamic, anticorrelated functional networks. Proc. Natl. Acad. Sci. U. S. A. 102, 9673. Fox, M.D., Zhang, D., Snyder, A.Z., Raichle, M.E., 2009. The global signal and observed anticorrelated resting state brain networks. J. Neurophysiol. 101, 3270–3283.

U

N

C

O

R

R

E

C

T

518

E

501 502

F

499

Franks, N.P., 2008. General anaesthesia: from molecular targets to neuronal pathways of sleep and arousal. Nat. Rev. Neurosci. 9, 370–386. Fries, P., Reynolds, J.H., Rorie, A.E., Desimone, R., 2001. Modulation of oscillatory neuronal synchronization by selective visual attention. Science 291, 1560–1563. Gloor, P., 1969. Hans Berger and the discovery of the electroencephalogram. Electroencephalogr. Clin. Neurophysiol. (Suppl. 28), 1–36. Greicius, M.D., Krasnow, B., Reiss, A.L., Menon, V., 2003. Functional connectivity in the resting brain: a network analysis of the default mode hypothesis. Proc. Natl. Acad. Sci. U. S. A. 100, 253–258. He, B.J., Snyder, A.Z., Zempel, J.M., Smyth, M.D., Raichle, M.E., 2008. Electrophysiological correlates of the brain's intrinsic large-scale functional architecture. Proc. Natl. Acad. Sci. U. S. A. 105, 16039–16044. Hipp, J.F., Hawellek, D.J., Corbetta, M., Siegel, M., Engel, A.K., 2012. Large-scale cortical correlation structure of spontaneous oscillatory activity. Nat. Neurosci. 15, 884–890. Hutchison, R.M., Everling, S., 2013. Broad intrinsic functional connectivity boundaries of the macaque prefrontal cortex. NeuroImage 88C, 202–211. Hutchison, R.M., Leung, L.S., Mirsattari, S.M., Gati, J.S., Menon, R.S., Everling, S., 2011. Resting-state networks in the macaque at 7 T. NeuroImage 56, 1546–1555. Hutchison, R.M., Womelsdorf, T., Gati, J.S., Leung, L.S., Menon, R.S., Everling, S., 2012. Resting-state connectivity identifies distinct functional networks in macaque cingulate cortex. Cereb. Cortex 22, 1294–1308 (N Y N 1991). Hutchison, R.M., Womelsdorf, T., Gati, J.S., Everling, S., Menon, R.S., 2013. Resting-state networks show dynamic functional connectivity in awake humans and anesthetized macaques. Hum. Brain Mapp. 34, 2154–2177. Hutchison, R.M., Culham, J.C., Everling, S., Flanagan, J.R., Gallivan, J.P., 2014a. Distinct and distributed functional connectivity patterns across cortex reflect the domain-specific constraints of object, face, scene, body, and tool category-selective modules in the ventral visual pathway. NeuroImage 96, 216–236. Hutchison, R.M., Hutchison, M., Manning, K.Y., Menon, R.S., Everling, S., 2014b. Isoflurane induces dose-dependent alterations in the cortical connectivity profiles and dynamic properties of the brain's functional architecture. Hum. Brain Mapp. Hwang, E.J., Andersen, R.A., 2011. Effects of visual stimulation on LFPs, spikes, and LFPspike relations in PRR. J. Neurophysiol. 105, 1850–1860. Jensen, O., Colgin, L.L., 2007. Cross-frequency coupling between neuronal oscillations. Trends Cogn. Sci. 11, 267–269. Keller, C.J., Bickel, S., Honey, C.J., Groppe, D.M., Entz, L., Craddock, R.C., Lado, F.A., Kelly, C., Milham, M., Mehta, A.D., 2013. Neurophysiological investigation of spontaneous correlated and anticorrelated fluctuations of the BOLD signal. J. Neurosci. Off. J. Soc. Neurosci. 33, 6333–6342. Kelly, A.M.C., Uddin, L.Q., Biswal, B.B., Castellanos, F.X., Milham, M.P., 2008. Competition between functional brain networks mediates behavioral variability. NeuroImage 39, 527–537. Kelso, J.A.S., 1995. Dynamic Patterns: The Self-Organization of Brain and Behavior. The MIT Press. Laufs, H., 2008. Endogenous brain oscillations and related networks detected by surface EEG-combined fMRI. Hum. Brain Mapp. 29, 762–769. Laufs, H., Krakow, K., Sterzer, P., Eger, E., Beyerle, A., Salek-Haddadi, A., Kleinschmidt, A., 2003. Electroencephalographic signatures of attentional and cognitive default modes in spontaneous brain activity fluctuations at rest. Proc. Natl. Acad. Sci. U. S. A. 100, 11053. Leopold, D.A., Maier, A., 2012. Ongoing physiological processes in the cerebral cortex. NeuroImage 62, 2190–2200. Li, C.-X., Patel, S., Auerbach, E.J., Zhang, X., 2013. Dose-dependent effect of isoflurane on regional cerebral blood flow in anesthetized macaque monkeys. Neurosci. Lett. 541, 58–62. Logothetis, N.K., 2003. The underpinnings of the BOLD functional magnetic resonance imaging signal. J. Neurosci. Off. J. Soc. Neurosci. 23, 3963–3971. Logothetis, N.K., Pauls, J., Augath, M., Trinath, T., Oeltermann, A., 2001. Neurophysiological investigation of the basis of the fMRI signal. Nature 412, 150–157. Lu, H., Zuo, Y., Gu, H., Waltz, J.A., Zhan, W., Scholl, C.A., Rea, W., Yang, Y., Stein, E.A., 2007. Synchronized delta oscillations correlate with the resting-state functional MRI signal. Proc. Natl. Acad. Sci. U. S. A. 104, 18265–18269. Magri, C., Schridde, U., Murayama, Y., Panzeri, S., Logothetis, N.K., 2012. The amplitude and timing of the BOLD signal reflects the relationship between local field potential power at different frequencies. J. Neurosci. Off. J. Soc. Neurosci. 32, 1395–1407. Mantini, D., Perrucci, M.G., Del Gratta, C., Romani, G.L., Corbetta, M., 2007. Electrophysiological signatures of resting state networks in the human brain. Proc. Natl. Acad. Sci. 104, 13170. Mars, R.B., Jbabdi, S., Sallet, J., O'Reilly, J.X., Croxson, P.L., Olivier, E., Noonan, M.A., Bergmann, C., Mitchell, A.S., Baxter, M.G., et al., 2011. Diffusion-weighted imaging tractography-based parcellation of the human parietal cortex and comparison with human and macaque resting-state functional connectivity. J. Neurosci. 31, 4087. Masamoto, K., Kanno, I., 2012. Anesthesia and the quantitative evaluation of neurovascular coupling. J. Cereb. Blood Flow Metab. Off. J. Int. Soc. Cereb. Blood Flow Metab. 32, 1233–1247. Menon, V., 2011. Large-scale brain networks and psychopathology: a unifying triple network model. Trends Cogn. Sci. 15, 483–506. Murphy, K., Birn, R.M., Handwerker, D.A., Jones, T.B., Bandettini, P.A., 2009. The impact of global signal regression on resting state correlations: are anti-correlated networks introduced? NeuroImage 44, 893–905. Musso, F., Brinkmeyer, J., Mobascher, A., Warbrick, T., Winterer, G., 2010. Spontaneous brain activity and EEG microstates. A novel EEG/fMRI analysis approach to explore resting-state networks. NeuroImage 52, 1149–1161. Niessing, J., Ebisch, B., Schmidt, K.E., Niessing, M., Singer, W., Galuske, R.A.W., 2005. Hemodynamic signals correlate tightly with synchronized gamma oscillations. Science 309, 948–951.

O

there is reason to believe that they are not solely driven by effects of the anesthesia

P

497 498

R O

10

Please cite this article as: Hutchison, R.M., et al., Electrophysiological signatures of spontaneous BOLD fluctuations in macaque prefrontal cortex, NeuroImage (2015), http://dx.doi.org/10.1016/j.neuroimage.2015.03.062

565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 Q6 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650

R.M. Hutchison et al. / NeuroImage xxx (2015) xxx–xxx

D

P

R O

O

F

Tagliazucchi, E., von Wegner, F., Morzelewski, A., Brodbeck, V., Laufs, H., 2012. Dynamic BOLD functional connectivity in humans and its electrophysiological correlates. Front. Hum. Neurosci. 6, 339. Teichert, T., Grinband, J., Hirsch, J., Ferrera, V.P., 2010. Effects of heartbeat and respiration on macaque fMRI: implications for functional connectivity. Neuropsychologia 48, 1886–1894. Tinker, J.H., Sharbrough, F.W., Michenfelder, J.D., 1977. Anterior shift of the dominant EEG rhytham during anesthesia in the Java monkey: correlation with anesthetic potency. Anesthesiology 46, 252–259. Tognoli, E., Kelso, J.A.S., 2009. Brain coordination dynamics: true and false faces of phase synchrony and metastability. Prog. Neurobiol. 87, 31–40. Van Essen, D.C., 2004. Surface-based approaches to spatial localization and registration in primate cerebral cortex. NeuroImage 23 (Suppl. 1), S97–S107. Van Essen, D.C., Drury, H.A., Dickson, J., Harwell, J., Hanlon, D., Anderson, C.H., 2001. An integrated software suite for surface-based analyses of cerebral cortex. J. Am. Med. Inform. Assoc. 8, 443–459. Vincent, J.L., Patel, G.H., Fox, M.D., Snyder, A.Z., Baker, J.T., Van Essen, D.C., Zempel, J.M., Snyder, L.H., Corbetta, M., Raichle, M.E., 2007. Intrinsic functional architecture in the anaesthetized monkey brain. Nature 447, 83–86. Vogels, T.P., Rajan, K., Abbott, L.F., 2005. Neural network dynamics. Annu. Rev. Neurosci. 28, 357–376. von von der Malsburg, C., Phillips, W.A., Singer, W. (Eds.), 2010. Dynamic Coordination in the Brain: From Neurons to Mind. The MIT Press. Wang, L., Saalmann, Y.B., Pinsk, M.A., Arcaro, M.J., Kastner, S., 2012. Electrophysiological low-frequency coherence and cross-frequency coupling contribute to BOLD connectivity. Neuron 76, 1010–1020. Williams, K.A., Magnuson, M., Majeed, W., LaConte, S.M., Peltier, S.J., Hu, X., Keilholz, S.D., 2010. Comparison of alpha-chloralose, medetomidine and isoflurane anesthesia for functional connectivity mapping in the rat. Magn. Reson. Imaging 28, 995–1003. Yan, C.-G., Cheung, B., Kelly, C., Colcombe, S., Craddock, R.C., Di Martino, A., Li, Q., Zuo, X.-N., Castellanos, F.X., Milham, M.P., 2013a. A comprehensive assessment of regional variation in the impact of head micromovements on functional connectomics. NeuroImage 76, 183–201. Yan, C.-G., Craddock, R.C., Zuo, X.-N., Zang, Y.-F., Milham, M.P., 2013b. Standardizing the intrinsic brain: towards robust measurement of inter-individual variation in 1000 functional connectomes. NeuroImage 80, 246–262. Yeo, B.T.T., Krienen, F.M., Sepulcre, J., Sabuncu, M.R., Lashkari, D., Hollinshead, M., Roffman, J.L., Smoller, J.W., Zöllei, L., Polimeni, J.R., Fischl, B., Liu, H., Buckner, R.L., 2011. The organization of the human cerebral cortex estimated by intrinsic functional connectivity. J. Neurophysiol. 106, 1125–1165.

E

Nir, Y., Fisch, L., Mukamel, R., Gelbard-Sagiv, H., Arieli, A., Fried, I., Malach, R., 2007. Coupling between neuronal firing rate, gamma LFP, and BOLD fMRI is related to interneuronal correlations. Curr. Biol. 17, 1275–1285. Nir, Y., Mukamel, R., Dinstein, I., Privman, E., Harel, M., Fisch, L., Gelbard-Sagiv, H., Kipervasser, S., Andelman, F., Neufeld, M.Y., Kramer, U., Arieli, A., Fried, I., Malach, R., 2008. Interhemispheric correlations of slow spontaneous neuronal fluctuations revealed in human sensory cortex. Nat. Neurosci. 11, 1100–1108. Pan, W.-J., Thompson, G., Magnuson, M., Majeed, W., Jaeger, D., Keilholz, S., 2011. Broadband local field potentials correlate with spontaneous fluctuations in functional magnetic resonance imaging signals in the rat somatosensory cortex under isoflurane anesthesia. Brain Connect. 1, 119–131. Petrides, M., Pandya, D.N., 1999. Dorsolateral prefrontal cortex: comparative cytoarchitectonic analysis in the human and the macaque brain and corticocortical connection patterns. Eur. J. Neurosci. 11, 1011–1036. Popa, D., Popescu, A.T., Paré, D., 2009. Contrasting activity profile of two distributed cortical networks as a function of attentional demands. J. Neurosci. Off. J. Soc. Neurosci. 29, 1191–1201. Power, J.D., Cohen, A.L., Nelson, S.M., Wig, G.S., Barnes, K.A., Church, J.A., Vogel, A.C., Laumann, T.O., Miezin, F.M., Schlaggar, B.L., Petersen, S.E., 2011. Functional network organization of the human brain. Neuron 72, 665–678. Rabinovich, M.I., Friston, K.J., Varona, P. (Eds.), 2012. 1st ed Principles of Brain Dynamics: Global State Interactions. The MIT Press. Raichle, M.E., 2011. The restless brain. Brain Connect. 1, 3–12. Ringach, D.L., 2009. Spontaneous and driven cortical activity: implications for computation. Curr. Opin. Neurobiol. 19, 439–444. Ritter, P., Villringer, A., 2006. Simultaneous EEG–fMRI. Neurosci. Biobehav. Rev. 30, 823–838. Scheeringa, R., Fries, P., Petersson, K.-M., Oostenveld, R., Grothe, I., Norris, D.G., Hagoort, P., Bastiaansen, M.C.M., 2011. Neuronal dynamics underlying high- and low-frequency EEG oscillations contribute independently to the human BOLD signal. Neuron 69, 572–583. Schölvinck, M.L., Maier, A., Ye, F.Q., Duyn, J.H., Leopold, D.A., 2010. Neural basis of global resting-state fMRI activity. Proc. Natl. Acad. Sci. U. S. A. 107, 10238–10243. Schölvinck, M.L., Leopold, D.A., Brookes, M.J., Khader, P.H., 2013. The contribution of electrophysiology to functional connectivity mapping. NeuroImage 80, 297–306. Shmuel, A., Leopold, D.A., 2008. Neuronal correlates of spontaneous fluctuations in fMRI signals in monkey visual cortex: implications for functional connectivity at rest. Hum. Brain Mapp. 29, 751–761. Siegel, M., Donner, T.H., Engel, A.K., 2012. Spectral fingerprints of large-scale neuronal interactions. Nat. Rev. Neurosci. 13, 121–134.

T

651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690

11

U

N C O

R

R

E

C

731

Please cite this article as: Hutchison, R.M., et al., Electrophysiological signatures of spontaneous BOLD fluctuations in macaque prefrontal cortex, NeuroImage (2015), http://dx.doi.org/10.1016/j.neuroimage.2015.03.062

691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 Q7 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730