bioRxiv preprint doi: https://doi.org/10.1101/2022.10.20.513008; this version posted October 21, 2022. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. 1 Specific and non-uniform brain states during cold perception in mice 2 Haritha Koorliyil1, Jacobo Sitt2, Isabelle Rivals3, Yushan Liu3, Silvia Cazzanelli1,4, Adrien 3 Bertolo1,4, Alexandre Dizeux1, Thomas Deffieux1, Mickael Tanter1 and Sophie Pezet1*, 4 1Physics 5 6 2 7 8 3 9 4 Iconeus, 27 Rue du Faubourg Saint-Jacques, 75014 Paris, France 10 * Corresponding Author: Sophie PEZET 11 Address: Institute of Physics for Medicine Paris, ESPCI Paris, 17 rue Moreau, 75012, Paris, France 12 Email: sophie.pezet@espci.fr for Medicine Paris, INSERM, ESPCI Paris, CNRS, PSL Research University - Paris, France. Institut du Cerveau et de la Moelle épinière, ICM, Inserm U 1127, PICNIC Lab, F-75013, Paris, France Equipe de Statistique Appliquée, ESPCI Paris, PSL Research University, UMRS 1158, 10 rue Vauquelin, 75005 Paris, France. 13 14 Number of text pages: 47 15 Number of figures: 5 16 Number of tables: 0 17 Number of supplementary materials: 6 (3 figures +3 tables) 18 19 Keywords: Primary sensory cortex, ultrafast ultrasound imaging, functional connectivity, 20 Doppler, thermal sensitivity 21 22 Short title: Brain imaging of cold sensing 23 24 Conflict of interest 25 26 27 28 MT and TD are co-founders and shareholders of Iconeus company. MT and TD are co-inventor of several patents in the field of neurofunctional ultrasound and ultrafast ultrasound. MT and TD do not have any other financial conflict of interest, nor any non-financial conflict of interests. All the other authors do not have any financial or non-financial conflict of interests. 1 bioRxiv preprint doi: https://doi.org/10.1101/2022.10.20.513008; this version posted October 21, 2022. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. 29 30 Authors contribution statement 31 SP, MT and HK designed the experimental paradigm. 32 33 SC, AB and HK were involved in the awake functional ultrasound imaging and thermal experimental setup. 34 HK and SP wrote the manuscript. 35 HK performed the experiments and analyzed the ultrasound data. 36 MT, TD and JS supervised the signal processing of the ultrasound data. 37 IR and YL performed the statistical analysis. 38 AD was involved in the signal processing. 39 40 SP, MT, JS, HK and IR were involved in the interpretation of the data and wrote some parts of the manuscript. 41 42 Acknowledgments 43 The authors wish to thank Nathalie Ialy-Radio for animal husbandry and the CNRS, INSERM 44 and ESPCI for their financial support. This work was supported by a funding from the 45 European Union’s Horizon 2020 research and innovation program under the Marie 46 Skłodowska-Curie grant agreement No 754387 (PhD fellowship Miss Koorliyil) and from the 47 Agence Nationale de la recherche (Project ‘PINCH’, 18-CE37-0005-01). In addition, this work 48 was supported by the Chair in Biomedical Imaging of the AXA Research Fund and the 49 European Research Council (ERC) Advanced Grant FUSIMAGINE. 50 2 bioRxiv preprint doi: https://doi.org/10.1101/2022.10.20.513008; this version posted October 21, 2022. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. 51 ABSTRACT 52 53 The quest to decode the complex supraspinal mechanisms that integrate cutaneous thermal 54 information in the central system is still ongoing. The dorsal horn of the spinal cord is the 55 first hub that encodes thermal input which is then transmitted to brain regions via the 56 spinothalamic and thalamo-cortical pathways. So far, our knowledge about the strength of 57 the interplay between the brain regions during thermal processing is limited. To address this 58 question, we imaged the brains of awake and freely-moving mice using Functional 59 Ultrasound imaging during plantar exposure to constant and varying temperatures. Our 60 study, a synchronous large field investigation of mice brains reveals for the first time the 61 brain states and the specific dynamic interplay between key regions involved in thermal 62 processing. Our study reveals: i) a dichotomy in the response of the somato-motor-cingulate 63 cortices and the hypothalamus, which was never described before, due to the lack of 64 appropriate tools to study such regions with both good spatial and temporal resolutions. ii) 65 We infer that cingulate areas may be involved in the affective responses to temperature 66 changes. iii) Colder temperatures (ramped down) reinforces the disconnection between the 67 somato-motor-cingulate and hypothalamus networks. iv) Finally, we also confirm the 68 existence in the mouse brain of a dynamic brain mode characterized by low cognitive 69 strength, described previously only in non-human primates and humans. The present study 70 points towards the existence of a common hub between somato-motor and cingulate regions, 71 whereas hypothalamus functions are related to a secondary network. 72 73 3 bioRxiv preprint doi: https://doi.org/10.1101/2022.10.20.513008; this version posted October 21, 2022. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. 74 75 INTRODUCTION 76 Thermal sensation and perception are crucial for maintaining the structural and functional 77 integrity of all organisms (Gracheva and Bagriantsev, 2015). Thermal changes can elicit a 78 multitude of responses including rapid motor withdrawal reflex and thermoregulation to 79 maintain core body temperature. To cope with the changes in the thermal environment, 80 physiological and behavioral mechanisms are employed permanently (Tan and Knight, 81 2018). The complex mechanisms that result in the central and peripheral integration of 82 cutaneous thermal sensations is still not completely understood. 83 Thermal sensations felt on skin are encoded and transmitted to the central nervous system 84 by primary sensory neurons, such as non-myelinated C fibers and thinly myelinated Aδ fibers 85 whose terminals act as free nerve endings on the skin (Middleton et al., 2021; Xiao and Xu, 86 2021). The thermal information is transmitted by the sensory afferents to the dorsal horn via 87 the TRP channels, which are the molecular thermo-detectors (Peier et al., 2002; Clapham, 88 2003; Patapoutian et al., 2003; Bandell et al., 2004; Bautista et al., 2007; Dhaka et al., 2007; 89 Tan and McNaughton, 2016; Hoffstaetter et al., 2018; Vandewauw et al., 2018; Vilar et al., 90 2020), and can initiate well-defined responsive pathways, such as: a) activation of motor 91 neurons resulting in a rapid withdrawal reflex, b) transmission of thermal information via 92 the spinothalamic tract to various nuclei of the thalamus and finally to several cortical areas 93 such as the insular cortex and the primary somatosensory cortex where it takes the form of 94 a perceived temperature, c) initiation of thermoregulatory responses (Vriens et al., 2014). 95 Although the supraspinal pathways work synergistically to form a thermal perception, the 96 complex interplay among them is less explored. Studies have shown that several thalamic 4 bioRxiv preprint doi: https://doi.org/10.1101/2022.10.20.513008; this version posted October 21, 2022. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. 97 nuclei (Bushnell et al., 1993; Duncan et al., 1993; Craig et al., 1994; Davis et al., 1999), 98 somatosensory regions (Becerra et al., 1999; Moulton et al., 2012; Milenkovic et al., 2014) 99 and the insula (Craig et al., 2000; Olausson et al., 2005; Veldhuijzen et al., 2010; Peltz et al., 100 2011; Wager et al., 2013; Gogolla et al., 2014) are crucial for thermosensation. Although the 101 cingulate region is not a direct part of the thermosensory circuit, it is involved in the affective 102 responses to the nociceptive thermal stimulations (Vogt, 2005). The preoptic anterior 103 hypothalamus (POAH) has been linked to thermoregulatory behavior in numerous studies 104 (Ishiwata et al., 2002; DiMicco and Zaretsky, 2007; Wang et al., 2019). 105 The present study aimed at understanding the involvement of some of the aforementioned 106 brain regions using functional ultrasound (fUS) imaging, which is a relatively new versatile 107 neuroimaging modality that allows imaging and measurement of cerebral blood volume in 108 humans (Demene et al., 2017; Imbault et al., 2017; Soloukey et al., 2020), non-human 109 primates (Dizeux et al., 2019) and rodents (Macé et al., 2011; Sieu et al., 2015; Urban et al., 110 2015; Bergel et al., 2018; Rahal et al., 2020) with excellent spatial (100 to 300 µm) and 111 temporal resolutions (down to 20 ms). One of its most important characteristics is its high 112 sensitivity compared to fMRI (Boido et al., 2019). Indeed, during a task, due to neurovascular 113 coupling, the locally increased neuronal activity leads to a strong hemodynamic response 114 (Iadecola, 2017). In the past, fUS imaging proved sensitive enough to enable the 115 measurement of the cortical hemodynamic changes induced by sensory (Macé et al., 2011), 116 olfactory (Osmanski et al., 2014a) and visual (Macé et al., 2018) stimuli in anesthetized 117 animals. Another very important characteristic of fUS imaging consists in its ability to 118 perform acquisitions in awake and behaving animals (Montaldo et al., 2022), as 119 demonstrated for auditory stimuli in awake animals (Bimbard et al., 2018) or motor tasks 5 bioRxiv preprint doi: https://doi.org/10.1101/2022.10.20.513008; this version posted October 21, 2022. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. 120 (Sieu et al., 2015; Bergel et al., 2020). Taking advantage of the sensitivity of this technique 121 and the ability to study freely moving animal, this study aimed at improving our 122 understanding of the processing of warm and cold sensing by studying the changes of 123 intrinsic brain connectivity in freely moving mice during plantar exposure to warm, cold and 124 neutral surfaces, and to the variations of the surface temperature. The analysis of the static 125 and dynamic functional connectivity reveals that cold induces a strongly increased 126 connectivity in the somato-motor (SM) network, but also a decreased connectivity between 127 the SM areas and the hypothalamus. 128 6 bioRxiv preprint doi: https://doi.org/10.1101/2022.10.20.513008; this version posted October 21, 2022. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. 129 130 MATERIALS AND METHODS 131 ANIMALS 132 The experiments were conducted in compliance with the European Community Council 133 Directive of 22 September 2010 (010/63/UE) and the local ethics committee (Comité 134 d'éthique en matière d'expérimentation animale N° 59, “Paris Centre et Sud”, project 2018- 135 05). Accordingly, the number of animals in our study was kept to the minimum necessary. 136 Due to previous studies using a similar experimental design (Rabut et al., 2020), we 137 established that N=6 animals per group was the smallest number of animals required to 138 detect statistically significant differences in our imaging experiments. Finally, all methods are 139 in accordance with ARRIVE guidelines. 140 Animals arrived at the animal facilities one week before the beginning of experiments. 141 Twelve C57Bl/6 male mice (aged 7 weeks at the beginning of the experiments) were obtained 142 from Janvier labs (France) and housed under controlled temperature (22 ± 1°C), relative 143 humidity (55 ± 10%), with a 12-hour light/dark cycle. Finally, food and water were available 144 ad libitum. Experiments typically lasted for 4-6 weeks. 145 Constant temperature and temperature ramp experiments were conducted in two different 146 sets of N=8 and N=6 mice (Figure 1). When possible, animals were imaged more than once in 147 each experimental condition. Indeed, due to motion artifacts, some sessions had to be 148 discarded (see below). Details of data included from the various animals are listed in 149 supplementary table 1. 150 7 bioRxiv preprint doi: https://doi.org/10.1101/2022.10.20.513008; this version posted October 21, 2022. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. 151 152 Figure 1: Experimental design and timeline of the experiments 153 A) One week after their arrival in the laboratory, a metal plate was surgically attached to the 154 mice skulls. After 3 days of recovery, mice were handled and habituated for 2 weeks. Imaging 155 was then performed for the next 2 weeks or more, depending on the quality of the skull and 156 on the well-being of the mice. (B) Schematic showing the metal plate, probe holder, fUS probe 157 and (C-D) plane of imaging (Bregma -0.34 mm). C shows the Doppler image superimposed 158 with the delimitation of the mouse brain atlas (D, Paxinos and Franklin, 2011). The regions 159 of interest are: 1,8 primary somatosensory cortex, hindlimb part, 2,3,7,8 primary and 160 secondary motor cortices, 4,5 Cingulate cortex and 9,10 Hypothalamus. (E). Experimental 8 bioRxiv preprint doi: https://doi.org/10.1101/2022.10.20.513008; this version posted October 21, 2022. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. 161 setup with Bioseb and fUS Imaging (Iconeus). Mice were subjected to constant temperatures, 162 or to warm and cold ramps at fast and slow pace. (F) The doppler signal obtained from 163 imaging underwent thresholding to remove motion artefacts, concatenation, low-pass 164 filtering, smoothing, normalization and SVD filtering. The cleaned doppler signal was then 165 used for FC analysis. 166 167 SURGICAL IMPLANTATION OF METAL PLATE 168 Approximately one week after their arrival, the mice underwent surgery for the implantation 169 of the metal plate (Tiran et al., 2017; Rabut et al., 2020). A mixture of ketamine (100 mg/kg) 170 and medetomidine (1 mg/kg) was administered intraperitoneally and then the mouse was 171 placed on a stereotaxic frame where the skull bone was exposed after skin and periosteum 172 removal. The metal plate was fixed on the skull using Superbond C&B (Sun Medical, USA) and 173 small screws minimally drilled in the skull. The field of interest was approximately 5mm 174 wide, between the Bregma and Lambda points. The surgery took 45 to 60 minutes to be 175 completed. Subcutaneous injections of atipamezole (1 mg/kg, Antisedan) and Metacam (5 176 mg/kg/day) were given to reverse the anesthesia and to prevent postsurgical pain, 177 respectively. A protective cap was mounted on the metal plate using magnets to protect the 178 skull and to keep the field of imaging intact for 4-6 weeks (Bertolo et al., 2021). Altogether, 179 the metal plate and the cap did not interfere with the normal daily activity of the mice. After 180 a recovery period of 3 days, the mice proceeded towards the habituation phase. 181 182 HABITUATION AND TRAINING 9 bioRxiv preprint doi: https://doi.org/10.1101/2022.10.20.513008; this version posted October 21, 2022. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. 183 After recovering from the metal plate implantation, the mice were subjected to an extensive 184 habituation protocol. To make sure that the mice were not under any stress during the 185 experiment, it was crucial that they were at ease with the user and the setup. They were 186 initially handled by the user and then exposed to the Bioseb HC Plate. Their daily interaction 187 time in the Bioseb apparatus was gradually increased from 15 minutes to 30 minutes, 1 hour, 188 1 hour 30 minutes and finally 2 hours. Depending on the level of habituation of each mouse, 189 the user practiced the protective cap removal, the skull cleaning using saline and application 190 of echographic gel without anesthesia, by gently restricting the head movement. After each 191 session, the mice received a reward. The process lasted 2 weeks and, depending upon the 192 comfort level of the mice, we then proceeded to the imaging phase. 193 194 EXPERIMENTAL PARADIGM 195 The global aim of this study was to decipher how thermal sensations are encoded in the 196 mouse brain. As intrinsic functional connectivity was shown to measure the activity and 197 functionality of the brain networks, we postulated it could vary during exposure to cold, 198 warm and neutral floor surfaces. To address this issue, we imaged the brain using fUS in 199 awake and freely moving mice exposed to various thermal sensations (Figure 1 E). 200 We observed in previous experiments (Rabut et al., 2020) that motion artifacts deeply alter 201 the quality of the ultrasound signals, and that all efforts need to be made to prevent these 202 artifacts. In preliminary experiments, we have sought to establish the range of temperature 203 in which the animals were minimally uncomfortable. Combining the records of the animal’s 204 natural behavior (grooming, exploration, urination and freezing) and measurements of 10 bioRxiv preprint doi: https://doi.org/10.1101/2022.10.20.513008; this version posted October 21, 2022. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. 205 naturally emitted ultrasound vocalizations, we observed that, between 15°C and 35°C, the 206 animals were minimally uncomfortable and the experiments feasible. Any residual of motion 207 artifacts in the Doppler signal (due to head movements or behavioral movements such as 208 grooming or licking) were removed using a dedicated signal processing as explained in 209 Figure 1F. 210 We used the Bioseb ‘Hot-Cold Plate’ to conduct the experiments. The metal floor of the Bioseb 211 equipment can be kept at a constant temperature, or heated or cooled down at different rates. 212 i. Constant Temperature 213 A constant floor temperature of 15°C (cold), 25°C (neutral) or 35°C (warm) was applied for 214 20 minutes. Experiments at these temperatures were randomly repeated on two separate 215 days. At the beginning of all sessions, the floor temperature was held at 25°C, and the 216 transition to the desired temperature occurred within seconds. 217 ii. Varying Temperature at fast or slow pace 218 Floor temperature variations into the warm domain were made of two ramps, one up (from 219 25°C to 35°C) and one down (back to 25°C); variations into the cold domain were made of a 220 ramp down (from 25°C to 15 °C) and a ramp up (back to 25°C) (Figure1 E). In order to 221 determine the effect of the speed of the temperature change, these ramps were performed at 222 2 different rates: either at 0.5°C per minute (for 20 minutes) or at 1°C per minute (for 10 223 minutes). The mice were reimaged at least 3 times. In order to avoid any bias due to the order 224 of these variations, the order of warm and cold variations was randomized. They are denoted 225 as follows: 226 Cool Ramps: 11 bioRxiv preprint doi: https://doi.org/10.1101/2022.10.20.513008; this version posted October 21, 2022. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. 227 ▪ CFD: Cool Fast Down: 25°C to 15 °C (- 1°C per minute) 228 ▪ CFU: Cool Fast Up: 15°C to 25° (+ 1°C per minute) 229 ▪ CSD: Cool Slow Down: 25°C to 15 °C (+ 0.5°C per minute) 230 ▪ CSU: Cool Slow Up: 15°C to 25°C (- 0.5°C per minute) 231 Warm Ramps 232 ▪ WFU: Warm Fast Up: 25°C to 35°C (+1°C per minute) 233 ▪ WFD: Warm Fast Down: 35°C to 25°C (-1°C per minute) 234 ▪ WSU: Warm Slow Up: 25°C to 35°C (+0.5°C per minute) 235 ▪ WSD: Warm Slow Down: 35°C to 25°C (- 0.5°C per minute) 236 237 TRANSCRANIAL AWAKE FUS IMAGING 238 Three days prior to the first imaging session, the mice were anesthetized with isoflurane 239 (1.5%). The respective probe holders were magnetically clipped to the metal plate. Real time 240 transcranial Doppler images were acquired using the NeuroScan acquisition software 241 (Inserm Technological Research Accelerator and Iconeus, Paris France), and the position of 242 the probe was adjusted to select the Bregma -0.34 mm plane. The regions of interest included 243 the primary somatosensory cortex of the hindlimb, the primary and the secondary motor 244 cortex, the cingulate cortex and the hypothalamus (Figure 1 C-D). The skull was then 245 thoroughly inspected and cleaned to avoid any infection. The mice were put back in their 246 cages and imaged only 3 days later to avoid any interference with the isoflurane anesthesia. 12 bioRxiv preprint doi: https://doi.org/10.1101/2022.10.20.513008; this version posted October 21, 2022. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. 247 Unlike the previous awake imaging protocols described in (Tiran et al., 2017), mice were not 248 anesthetized to prepare the skull during the imaging phase. They were trained and 249 accustomed to the detachment of the protective cap, to the cleaning of the skull with saline, 250 and to the application of echographic gel with minimal force. They were then gently 251 introduced into the Bioseb apparatus and the probe holder was attached to the implanted 252 metal frame using the magnets on both pieces. Experiments began shortly after. 253 Real time vascular images were obtained by ultrafast compound doppler imaging technique 254 (Deffieux et al., 2021). Eleven successive tilted plane waves (-10° to +10° with 2° steps) were 255 used for insonification. Each image was obtained from 200 compounded frames acquired at 256 500Hz frame rate corresponding to a 5.5 kHz pulse repetition frequency. The tissue signal 257 was isolated from the cerebral blood volume signal using a spatio-temporal clutter filter 258 based on the singular value decomposition (SVD) of raw ultrasonic data (Demene et al., 2015) 259 to obtain a power Doppler image. 260 261 DOPPLER SIGNAL ANALYSIS 262 Imaging in awake mice required careful removal of motion artifacts due to head movements 263 or behavioral movements such as grooming. We followed the analysis previously described 264 in (Rabut et al., 2020) by first using a SVD clutter filter to separate blood motion from tissue 265 motion, and then by thresholding tissue motion and Doppler signal to identify the frames 266 with motion artifacts. Several thresholds were investigated by carefully examining the tissue 267 motion signal and the threshold that removed most of the motion artifacts was chosen. We 268 kept and concatenated epochs of at least 50 consecutive time points. The concatenated 13 bioRxiv preprint doi: https://doi.org/10.1101/2022.10.20.513008; this version posted October 21, 2022. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. 269 cleaned frames were filtered using a low-pass filter with a cut-off frequency of 0.1 Hz to 270 extract the steady-state. A polynomial fit of order 3 was applied to detrend the signal and, 271 finally, global variations in the brain were suppressed by removing the first eigenvector of 272 the dataset before connectivity analysis (Figure 1F). Acquisitions that did not match the 273 aforementioned criteria (<50 consecutive timepoints) were discarded. Supplementary Table 274 1 summarizes the identify of animals included in both parts of the study, and how many 275 sessions from each mouse were kept. Out of the N=8 animals included in the constant 276 experiments, N=8 acquisitions per experimental conditions were used in the analysis. As for 277 the second part of the study (ramp experiments), they included 6 to 10 acquisitions 278 (Supplementary Table 1), obtained in N=6 mice. 279 280 STATISTICAL ANALYSES 281 In order to understand the interactions between brain regions involved in the temperature 282 coding, we performed static and dynamic FC analyses. In the two types of analyses, the 283 following brain regions of the imaging plane were studied: Primary Somatosensory Hind 284 Limb part, Primary and Secondary Motor, Cingulate and Hypothalamus (Figure 1 C, D). This 285 imaging plane was chosen because of the known role of some of these regions in 286 thermosensation and thermoregulation. Ten ROIs were defined based on the Paxinos Atlas 287 (Paxinos, G and Franklin, 2012), numbered and coded as : 288 - 1 (S1HLL) & 8 (S1HLR) : primary somatosensory cortex left & right, 289 - 2 (M1L) & 7 (M1R) : primary motor cortex left & right, 290 - 3 (M2L) & 6 (M2R) : secondary motor cortex left & right, 14 bioRxiv preprint doi: https://doi.org/10.1101/2022.10.20.513008; this version posted October 21, 2022. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. 291 - 4 (CgL) & 5 (CgR) : cingulate cortex left & right, 292 - 9 (HyThL) & 10 (HyThR) : hypothalamus left & right. 293 294 1) 295 In order to study the resting-state FC in the different thermal conditions, the post-treated 296 time course of the cerebral blood volume (CBV) signal of the N=10 ROIs (Figure 1 C, D) were 297 extracted. Simply stated, the temporal signal during the calm periods (without motion 298 artefacts) was extracted from each ROI. The NxN Pearson correlation matrix of the ROI 299 signals was computed over time. First, each correlation coefficient of the correlation matrix 300 was individually compared between pairs of conditions. Since the measurements were not 301 always independent (because of intra and inter group comparison, and of repeated 302 measurements on some animals, see Supplementary Tables 1 and 3), these comparisons 303 were made with linear mixed models having the animal as random effect factor, and the 304 thermal condition as two-modality fixed effect factor, whose significance was tested. The 305 correlation coefficients being Fisher-transformed, the parameter estimation was made using 306 restricted maximum likelihood estimation, and the validity of the model was checked 307 posteriori by testing the normality of its residuals with Shapiro-Wilk’s test. Then, in order to 308 account for multiple testing (a NxN correlation matrix involves Nx(N-1)/2 = 45 correlation 309 coefficients), we performed Benjamini-Hochberg’s adjustment for multiple comparisons on 310 the p-values of significance of the thermal condition effect. A false discovery rate of 0.05 was 311 adopted. STATIC FC ANALYSIS 312 313 2) DYNAMIC FC ANALYSIS 15 bioRxiv preprint doi: https://doi.org/10.1101/2022.10.20.513008; this version posted October 21, 2022. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. 314 In order to study the dynamic behavior of FC, the above correlation matrices can be 315 decomposed into the contribution of each time point, i.e. the co-fluctuation matrices 316 [Esfahlani et al. 2020]. As a matter of fact, consider xi = [xi(1) … xi(T)]T the time series of the 317 CBV signal of ROI n°i, and xj = [xj(1) … xj(N)]T that of ROI n°j. On one hand, the correlation 318 coefficient between ROIs n°i and j can be computed by first z-scoring each xi according to zi 319 = (xi – mi)/si, where mi = 1/T ∑t xi(t) and si2 = 1/(n-1) ∑t (xi – mi)2 are the empiric mean and 320 variance of the time-series over time, and then by computing rij = ziT zj/(T-1). Hence a single 321 NxN correlation matrix with only Nx(N-1)/2 = 45 elements of interest since the matrix 322 symmetric with unit diagonal. On the other hand, it is possible to consider the element-wise 323 product of zi and zj as encoding the magnitude of the moment-to-moment co-fluctuations 324 between ROIs n°i and j, and the 3D array of the NxN couples of zi and zj as a time series of T 325 co-fluctutation matrices of size NxN, each with only Nx(N+1)/2 = 55 elements of interest 326 since the co-fluctuation matrices are symmetric. 327 To assess the repetitive nature of the dynamic characteristics of brain networks as a response 328 to thermal inputs in an unsupervised fashion, we performed k-means clustering of the co- 329 fluctuation matrices contributing to the static correlation matrices. The time series data from 330 all 92 acquisitions on all animals were concatenated together to form a single time series of 331 T = 84 499 time points for each ROI, yielding a 3D array of size 84 499 x 10 x 10. 332 Prior to k-means clustering, outliers were removed. To this end, the 84 499 x 84 499 L1 333 distance matrix between cofluctuation matrices considered as Nx(N+1)/2 vectors was 334 computed, and the matrices with mean distance to the others larger than 3 were discarded, 335 decreasing to T’ = 83 325 the number of co-fluctuation matrices to be clustered. K-means 336 clustering was performed on the T’ remaining matrices considered as Nx(N+1)/2 vectors 16 bioRxiv preprint doi: https://doi.org/10.1101/2022.10.20.513008; this version posted October 21, 2022. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. 337 using the L1 distance with k=3, 5, 6 and 7 clusters: for each value of k, to avoid local minima, 338 the algorithm was run 500 times with random initializations of centroid positions, the 339 configuration minimizing the total sum of intra-cluster distances being retained. Finally, each 340 time point was assigned to a cluster (or brain state), resulting in a dynamic characterization 341 of FC patterns. For each of the 4 choices of k, the occurrence rates of all brain states were 342 calculated for each animal in all conditions. Prior to the comparison of these occurrence rates 343 across thermal conditions, we checked the homogeneity of the animal distribution across the 344 brain states (see Supplementary Table-2). In order to establish the significance of the effect 345 of the thermal condition on the occurrence rate of each state, linear mixed models were used 346 for the same reasons as for the elements of the correlation matrices (intra and inter group 347 comparison, varying numbers of repeated measurements). As before, the animal was 348 considered as a random effect factor, and the thermal condition as a fixed effect factor, this 349 time with several modalities (the constant and varying temperature conditions). When the 350 latter could be considered significant with a type I error risk of 5%, two-by-two comparisons 351 were performed, and the corresponding p-values were adjusted using Benjamini-Hochberg’s 352 procedure, a false discovery rate of 0.05 being again adopted. 353 354 RESULTS 355 Changes in brain functional connectivity during exposure to sustained neutral / warm 356 or cold floor surface 357 In order to decipher the FC alterations in brain networks, which are indicators of dynamic 358 changes in the brain, we developed an experimental setup based on a previously established 17 bioRxiv preprint doi: https://doi.org/10.1101/2022.10.20.513008; this version posted October 21, 2022. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. 359 protocol on awake and freely moving mice (Rabut et al., 2020), but this time allowing the 360 imaging during the application of varying floor temperatures (Figure 1). 361 In the first part, we investigated the potential changes of FC in cortical and hypothalamic 362 areas located in the chosen imaging plane during 20 minutes of exposure to a constantly 363 neutral (25°C), warm (35°C) or cold (15°C) floor. After removing motion artifacts and 364 concatenating the cleaned signals, the time series were first analyzed in a stationary way, i.e. 365 averaged over the recording sessions. 366 Exposure to constant floor temperature: 35°C (warm) and 15°C (cool) temperatures for 20 367 minutes were compared to 25°C (neutral temperature). We observed large and statistically 368 significant differences of FC between these conditions, with the largest number of significant 369 differences for the cold (15°C) floor, for which the FC of 19 couples of ROI were statistically 370 significantly different (Figure 2 A, B, C). Twelve of these pairs concerned areas of the somato- 371 motor network (SMN), which had a stronger connectivity at 15°C than at 25°C (Figure 2 D). 372 A single ROI pair, concerning cingulate and hypothalamus, exhibited an increase in 373 connectivity at 15°C. The seven other pairs of ROI concerned areas between a ROI of the SMN 374 and the hypothalamus (Figure 2 D). Interestingly, in these pairs, the FC was altered in the 375 opposite way: the FC was significantly decreased (Figure 2 H, I). 376 Exposure to warm floors (35°C) induced mild changes of the brain FC (Figure 2 A, E, F). Only 377 seven ROI pairs displayed a significant alteration of the FC. Two of these were pairs of the 378 SMN, which displayed an increased FC (Figure 2 D). The five other ROI pairs concerned areas 379 of the SMN and the hypothalamic nuclei (Figure 2 D). They all displayed the opposite effect: 380 an increased FC when the mice were submitted to a warmer floor (Figure 2 J). 18 bioRxiv preprint doi: https://doi.org/10.1101/2022.10.20.513008; this version posted October 21, 2022. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. 381 To conclude, for both temperatures (15°C and 35°C), when the floor temperature is constant, 382 the somato-motor regions display an increased connectivity, while the hypothalamic- 383 somato-motor network connectivity decreases. By taking a closer look, it was observed that 384 the connectivity between somato-motor network is higher at 15°C and 35°C than at 25°C, and 385 that the hypothalamic-somato-motor network anti-correlation was also higher at 15°C and 386 35°C than at 25°C. 387 388 Figure 2: Functional connectivity comparison between constant 15°C, 25°C and 35°C 389 using correlation matrices show striking differences between neutral (25°C) and cold 390 (15°C) rather than neutral and warm (35°C). 19 bioRxiv preprint doi: https://doi.org/10.1101/2022.10.20.513008; this version posted October 21, 2022. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. 391 A) Average Pearson correlation matrix of N=8 imaging sessions at 25°C. (B, E) Same for 392 imaging sessions at 15°C (N=8) and 35°C (N=8) respectively. (C, F) Significance matrix 393 indicating the ROI pairs with significant differences between 25°C and 15°C and 35°C 394 respectively. In these matrices, ROI pairs with significant alterations (adjusted p < 0.05) 395 between any two conditions are indicated by white squares with the corresponding p-value. 396 (D, G) Boxplot representation of each ROI pair with a significant FC alteration between the 397 conditions 25°C vs 15°C, and 25°C vs 35°C. *p < 0.05, **p < 0.01 and ***p < 0.001 of linear 398 mixed model analysis of the thermal condition effect, followed by Benjamini-Hochberg’s 399 correction for multiple comparisons. (H-J) Summary representation in form of graphs of the 400 statistically significant changes between 25°C and 15°C (I) and 25°C and 35°C (J) shown as 401 Z-scores between these conditions. (H) Schematic of the imaging plane Bregma -0.34 mm in 402 relation to the 3D whole mouse brain. I: Imaging plane with the regions studied (SSpll1-l/r – 403 primary somatosensory cortex of hindlimb S1HL, MOp1-l/r – primary motor cortex M1, 404 MOs1-l/r – secondary motor cortex M2, ACAd1-l/r cingulate Cg and HY l/r Hypothalamus). 405 406 Alterations of the stationary FC during fast dynamic changes of temperature 407 In order to decipher the neurobiological mechanism that takes place during dynamic changes 408 of the temperature, we next compared the stationary FC during fast or slow, warm or cold 409 ramps. 410 Warm Fast ramps did not lead to any significant FC difference compared to 25°C and 35°C 411 conditions (Supplementary Figure 1). However, Down and Up Cold Fast ramps led to strong 412 significant FC alterations compared to 25°C and 15°C conditions in numerous ROI pairs 413 (Figure 3). The Cold Fast Ramps Down were the conditions that produced the largest number 414 of statistically different ROIs, with 18 pairs of regions modified with respect to the neutral 415 temperature (25°C), and 15 pairs when compared to the target temperature (15°C). 20 bioRxiv preprint doi: https://doi.org/10.1101/2022.10.20.513008; this version posted October 21, 2022. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. 416 When compared to 25°C (Figure 3 A, B, C), 5 pairs of regions involving the cingulate and SMN, 417 and 7 couples within the SMN displayed an increased FC when the temperature was lowered 418 (Figure 3 D). The last 6 ROI pairs concerned the hypothalamus and SMN or the cingulate 419 (Figure 3 D). They all had an initial anti-correlation at 25°C, which became even stronger 420 when the floor became colder (Figure 3 D, O). 421 The comparison to constant 15°C floor temperature revealed 15 pairs of regions with 422 significantly modified FC (Figure 3 A, E, F). Among them, 13 encompassed the SMN, where 423 the FC increased when the floor temperature decreased (Figure 3 G). Two (intra-motor 424 network and motor-S1HL) had the opposite behavior: slight but statistically significantly 425 decreased FC (Figure 3 G). Finally, as shown in panel D of Figure 3, networks between the 426 hypothalamus and the cingulate showed anticorrelations when the floor was getting colder 427 (Figure 3 G). 428 Overall, these results suggest that a fast decrease in temperature is associated with a 429 strengthening of the correlation of the somato-motor network, and a weakening of the link 430 between the hypothalamus and the somato-motor network (Figure 3 O). 21 bioRxiv preprint doi: https://doi.org/10.1101/2022.10.20.513008; this version posted October 21, 2022. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. 431 22 bioRxiv preprint doi: https://doi.org/10.1101/2022.10.20.513008; this version posted October 21, 2022. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. 432 433 434 Figure 3: Functional Connectivity comparison between the Cool Fast Down (25°C to 15°C) and Cool Fast Up (15°C to 25°C) ramps on one hand, and 25°C and 15°C on the other hand. 435 (A) Average Pearson correlation matrix of N=10 imaging sessions during Cool Fast Down. (B, 436 E) Same for imaging sessions at constant 25°C and 15°C respectively. (C,F) Significance 437 matrix indicating the ROI pairs with significant differences between 25°C and Cool Fast 438 Down, and 15°C and Cool Fast Down respectively. (D,G) Boxplot representation of ROI pairs 439 with a significant FC alteration between Cool Fast Down ramps, and 25°C and 15°C 440 respectively. (H) Averaged Pearson correlation matrix of N=10 imaging sessions during Cool 441 Fast Up. (I,L) Same for sessions at fixed 25°C and 15°C respectively. (J,M) Matrix indicating 442 the ROI pairs with significant differences between Cool Fast Up ramps, and 25°C and 15°C 443 respectively. (K,N) Boxplot representation of the ROI pairs with a significant FC alteration 444 between Cool Fast Up and 25°C and 15°C respectively. *p < 0.05, **p < 0.01 and ***p < 0.001 445 of linear mixed model analysis of the thermal condition effect, followed by Benjamini- 446 Hochberg’s correction for multiple comparisons. (O, P) Summary representation in form of 447 graphs of the statistically significant differences shown in C and J, i.e. Z-scores between the 448 conditions: CFD vs 25°C (O) and CFU vs 25°C (P). 449 450 The analysis of the Cold Fast ramp Up (Figure 3 H-N) showed a similar effect in a smaller 451 number of regions. Compared to the target temperature (25°C), 5 pairs of regions within the 452 SMN showed an increased FC during the ramp (Figure 3 J, K). Seven couples involving the 453 hypothalamus and areas of the SMN displayed an increased anti-correlation of their FC 454 (Figure 3 J, K). The comparison of this Fast-Cold Up ramp to the initial temperature (15°C) 455 revealed that only 6 pairs of regions (Figure 3 M, N) differed significantly (4 within the SMN 456 and 2 between the hypothalamus and the cingulate and motor cortex). 457 The striking difference between the two Cold Fast ramps (Down and Up) was the large 458 number of subnetworks within the SM-cingulate networks in which the FC was reinforced. 23 bioRxiv preprint doi: https://doi.org/10.1101/2022.10.20.513008; this version posted October 21, 2022. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. 459 In both cases (Up and Down), the hypothalamus-SM-cingulate networks which are slightly 460 anti-correlated in neutral conditions, display a stronger anti-correlation during these ramps 461 (Figure 3 O-P). These results highlight the complex inter-regional FC between the cingulate, 462 somatosensory and motor cortices and hypothalamus during exposure to cold. 463 464 Effect of the rate of temperature change 465 In order to further understand how changes of floor temperature are processed centrally, we 466 also investigated the effect of the temperature change at a smaller rate of 0.5°C/min (20 min 467 duration), called Cold Slow/ Warm Up/Down, that we compared with the fast ramp initially 468 used (1°C/min, 10 min duration). We observe that, as hypothesized, these contrasting 469 conditions produce strikingly different effects. As a matter of fact, the Warm Slow ramps only 470 lead to slight FC alterations (Supplementary Figure 2). 471 Only 8 pairs of ROI were significantly different between the Cold Slow Down ramp and 472 constant 25°C (Figure 4 A, B, C), which is roughly half of what we observed with the fast 473 equivalent of this ramp. As observed with the fast-cold ramp, the modified ROI pairs 474 concerned the SMN (in which the FC was reinforced), and the SMN and hypothalamus, where 475 the anti-correlation was also reinforced (Figure 4 D). 476 As for the Cold Up ramp, in contrast with the aforementioned Cold Slow Down ramp and very 477 similarly to the fast ramps, it displayed a large number of pairs of ROI modified. Most of them 478 were within the SMN (12 out of 21 (Figure 4 F,G) with a strengthening of the FC between 479 them. The anti-correlation between the hypothalamus and the SMN was significantly 480 reinforced here (Figure 4 G), as observed in (Figure 3 D). 24 bioRxiv preprint doi: https://doi.org/10.1101/2022.10.20.513008; this version posted October 21, 2022. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. 481 482 483 484 485 Figure 4: Functional Connectivity comparison between the 25°C condition, and the Cool Slow Down (25°C to 15°C) and Cool Up (15°C to 25°C) ramps. 486 Cool Slow Down (N=8) and Cool Slow Up (N=6) imaging sessions. (C, F) Significativity matrix 487 indicating the ROI pairs with significant differences between 25°C with Cool Slow Down and 488 Cool Slow Up ramps respectively. (D, G) Boxplot representation of the ROI pairs with 489 significant FC alterations between 25°C and Cool Slow Down and Cool Slow Up ramps 490 respectively. *p < 0.05, **p < 0.01 and ***p < 0.001 of linear mixed model analysis of the 491 thermal condition effect, followed by Benjamini-Hochberg’s correction for multiple 492 comparisons. 493 In conclusion, the analysis of the effect of the rate of temperature change shows that the 494 larger the rate, the larger the number of ROI with statistically significant changes, especially 495 in the Cold Down ramps. Another important difference is the lack of significant differences 496 between these slow ramps and the constant low temperature of 15°C, suggesting that when (A) Average Pearson correlation matrix of N=8 imaging sessions at 25°C. (B,E) Same for the 25 bioRxiv preprint doi: https://doi.org/10.1101/2022.10.20.513008; this version posted October 21, 2022. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. 497 the ramp is slow, the overall central processing does not differentiate well between a cold 498 floor and one that is slowly getting cooler. 499 500 Dynamic FC 501 The dynamic nature of the stimuli applied in this study and the rapid changes of brain states 502 occurring in awake and conscious animals naturally led to the analysis of the dynamic 503 connectivity. We predicted that the FC changes during thermal variations might be even 504 stronger than the differences between warm and cold conditions. 505 In a previous study, we established that fUS imaging can robustly identify brain states 506 extracted from k-means clustering of steady state fUS data in anesthetized rats (Rahal et al., 507 2020). In the current study, using a similar approach, but adapted to data discontinuity 508 generated by artifact removal, we could robustly identify 5 consistent brain states using k- 509 means with K=5, 6 or 7 (Figure 5 A-C). As a matter of fact, whatever the value of k, the 5 states 510 are not only reproducible, but also display a stable percentage of occurrence across time. 511 Finally, the statistical effects observed in our experimental groups were consistently 512 obtained (with mild variation for state 6). 513 First, the warm ramps did not produce any significant difference in FC compared to the 514 constant temperatures (Supplementary figure 3). Among the brain states (identified with the 515 k=7 algorithm and labeled 1 to 7, see Figure 5A), the strengths of the connections and the 516 statistical results suggest four groups of states. The first group comprises state #1 only, which 517 is by far the most frequent state (54–60% of the time, Figure 5A-C), has weak connections, 26 bioRxiv preprint doi: https://doi.org/10.1101/2022.10.20.513008; this version posted October 21, 2022. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. 518 and is consistently more present at neutral temperature (constant 25°C) compared to all 519 other experimental conditions (Figure 5A-B). 520 The second group is made up of states #3, #5 and #7 which are present 10-17% of the time 521 are characterized by a common pattern of connectivity in the SM-cingulate (SM-Cg) cortex 522 and an anti-correlation between the SM and the hypothalamic regions. Interestingly, these 523 three states were significantly more frequent during the CFD ramp, compared to all constant 524 temperatures. 525 The third group consisted of states #4 and #6. In addition to the dichotomy between the SM 526 and hypothalamic networks, these states (present in 10-13% of the time)present are 527 characterized by a low correlation between the cingulate and all other areas. Very 528 interestingly, states #4 and #6 were consistently more frequent in one condition: the low 529 constant temperature (15°C), suggesting a specific involvement of the cingulate cortex 530 during this prolonged (20 minutes) exposure to cold. 531 Finally, the last group consisted in the single state 2, which was present 16-19% of the time 532 and that was statistically equally represented in all experimental conditions. 533 27 bioRxiv preprint doi: https://doi.org/10.1101/2022.10.20.513008; this version posted October 21, 2022. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. 534 535 28 bioRxiv preprint doi: https://doi.org/10.1101/2022.10.20.513008; this version posted October 21, 2022. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. 536 Figure 5: Dynamic functional connectivity analysis of thermal coding using k-means 537 clustering for either k=7, 6, 5 or 3. (A) 7 brain state matrices (cofluctuation matrices) and 538 (B) 6 brain state matrices were ordered according to their decreasing level of connectivity 539 with their probability of occurrence in cold and neutral conditions, and significance of the 540 thermal condition effect using linear mixed models. *p < 0.05, **p < 0.01 and ***p < 0.001 of 541 linear mixed model analysis of the thermal condition effect. Brain States #3, #5 and #7 show 542 dichotomy between SM/Cg and hypothalamus and occur more often in Cold Fast Down 543 ramps, whereas states #4 and #6 predominantly occur during sustained cold exposure and 544 display a decreased connectivity between Cg and other regions. The weakly correlated state 545 #1 is present in all thermal conditions, but its significantly more frequent in the 25°C 546 condition. (C-D) k-means clustering of brain states into 5 and 3 states, respectively. 547 548 DISCUSSION 549 The peripheral and central neurophysiological mechanisms involved in thermal sensing have 550 been at the heart of many studies since the last century. In the present study, we aimed at 551 deciphering the interplay between the brain regions that are already shown to be crucial for 552 thermal processing. Taking advantage of the sensitivity and versatility of fUS Doppler 553 imaging in awake freely moving animals, we analyzed the stationary and dynamic changes of 554 FC, an indirect marker of brain network functionality and dynamic characteristics. Despite 555 the technical limitations of our approach, our study brings forward robust information 556 regarding the complexity of interplay between the somato-motor and the hypothalamic brain 557 networks. 558 29 bioRxiv preprint doi: https://doi.org/10.1101/2022.10.20.513008; this version posted October 21, 2022. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. 559 FC: A strong marker of strength of interaction between brain regions 560 The analysis of intrinsic brain activity has led to the definition of resting brain state networks. 561 Since then, the field of neuroscience has been widely studying these infra-slow oscillations, 562 called functional connectivity (FC), that characterize the spatiotemporal organization and 563 function of large-scale brain networks (Preti et al., 2017)(Preti et al., 2017). Interestingly, FC 564 can be studied using very different approaches, such as fMRI, electrophysiology, MEG, fiber 565 photometry, optical imaging and fUS (Pais-Roldán et al., 2021). Although the classical studies 566 focused on resting state FC, task or behavior related FC studies using fMRI has also been 567 increasing in numbers in the recent years (Barch et al., 2013; Cole et al., 2014; Di and Biswal, 568 2019). Although the mice are not subjected to a task in our study, they are physiologically 569 and behaviorally responding to the thermal stimulation which is either cold, warm or neutral. 570 fUS has been previously shown to be able to measure FC in anesthetized rats (Osmanski et 571 al., 2014b), task-evoked changes in FC (Ferrier et al., 2020), pharmacological studies in 572 rodents (Rabut et al., 2020), but also altered FC in preterm babies (Baranger et al., 2021). 573 Unlike the conventional analysis of time averaged FC that provides quantitative information 574 on the correlation (or anti-correlation) between pairs of regions of interest (ROI) in the 575 steady-state, there have been tremendous advancements in the study of the dynamic nature 576 of FC (Hutchison et al., 2013; Preti et al., 2017). The temporal evolution of FC can reveal how 577 FC reshapes according to the physiological (Tarun et al., 2021) or behavioral changes, at rest, 578 or during a task, or in case of neurodegenerative or neuro-psychiatric illnesses (Tian et al., 579 2018; Barttfeld et al., 2019; Demertzi et al., 2019; Gu et al., 2020). 580 30 bioRxiv preprint doi: https://doi.org/10.1101/2022.10.20.513008; this version posted October 21, 2022. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. 581 Warm conditions do not elicit FC alterations 582 The temperature conditions in the present study were carefully designed as not to induce 583 any discomfort in the mice during awake imaging. Although 25°C falls in a slightly low 584 temperature range, adaptation takes place before the beginning of the experiment and can 585 be considered neutral. Thermal psychophysics studies have shown that, when the skin is 586 adapted to temperature values ranging from ∼30 to ∼34°C, neither warm nor cool sensations 587 are experienced (Filingeri, 2016). Furthermore, the transition from ambient to high 588 temperature between 32-39°C and 26-34°C activates the TRPV3 and TRPV4 channels 589 respectively. The TRPM2 channel is activated at approximately 35°C by sensing 590 environmental temperatures (Tan and McNaughton, 2016). Therefore, it is likely that a 591 temperature of 35°C would not either have evoked a significantly different response from 592 that observed at neutral temperature. 593 594 Dichotomy between somato-motor-cingulate and hypothalamic networks during 595 exposure to cold 596 In neutral conditions, the stationary FC indicated that, as previously demonstrated in fMRI in 597 human (Zeng et al., 2012) and rodents (see for review (Pais-Roldán et al., 2021)), the SMN 598 has a strong positive interhemispheric connectivity, that was shown previously to be due to 599 a strong concomitant interhemispheric neuronal activity. Our study demonstrates a 600 reinforcement in the somato-motor FC during cold sensing, mostly in the Cold Fast ramps. 601 We hypothesize that such a FC increase is an indirect readout of the sensory discriminative 602 aspect of cold sensing. 31 bioRxiv preprint doi: https://doi.org/10.1101/2022.10.20.513008; this version posted October 21, 2022. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. 603 In contrast with this, the SM-hypothalamic network shows a decreased FC during cold 604 sensing, suggesting a dichotomy between these two networks. A similar dramatic opposing 605 effect was also observed when analyzing the dynamic FC. Indeed, the second type of dynamic 606 modes reproducibly obtained in our analysis were the states 3, 5 and 7, which are 607 characterized by a dichotomy between a strongly correlated signal within the SM-Cg network 608 and an anti-correlation in the SM-hypothalamic network. These three modes, which account 609 for approximately 10-17% of the time, have a higher occurrence during the Cold Fast Down 610 ramp. The significant differences with other conditions, including the Cold Slow Down ramps 611 and the constant cold condition (15°C), suggest that this dichotomy is a specific feature of 612 cold sensing. 613 614 Weakly connected dynamic mode in thermal sensing 615 By measuring the temporal fluctuations in FC, we were able to identify the dynamic patterns 616 using k-means clustering. The results were robust for k=5, 6, 7, the nature of the states and 617 the statistical effects observed being similar whatever k. These states, that we named 1 to 7 618 (clustering with k=7), encompass different neurobiological meaning and role. 619 The first group of states consists of state #1 alone. As previously described in monkeys 620 (Barttfeld et al., 2019) and human subjects (Demertzi et al., 2019) using the same type of 621 analysis but in fMRI, the most frequent dynamic state (50-60% of the recording time) is a 622 mode characterized by weak strengths of connection. Our current understanding of this state 623 is that it is associated with low-level cognitive functions (Barttfeld et al., 2019; Demertzi et 624 al., 2019). In our study, while present in the majority of all experimental groups, it is 32 bioRxiv preprint doi: https://doi.org/10.1101/2022.10.20.513008; this version posted October 21, 2022. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. 625 statistically more frequent when the animals were exposed to the neutral temperature of 626 25°C. This result reinforces previous suggestions of low FC, apart from the default mode 627 network during resting periods. 628 629 A secondary pathway involving hypothalamus 630 The hypothalamus was anti-correlated to the somato-motor network in all conditions and 631 the strength of this anti-correlation was the highest during cold sensing. This dramatic effect 632 was revealed in both stationary and dynamic FC analysis. Although the preoptic anterior 633 hypothalamus (POAH) has been linked to thermoregulatory behavior (Ishiwata et al., 2002; 634 Wang et al., 2019), the secondary thermosensory pathway from dorsal horn to the lateral 635 parabrachial nucleus (LPB) and then to the preoptic area (POA) of the hypothalamus has 636 been put forward by (Yahiro et al., 2017). Using selective lesions of either thalamic nuclei or 637 the POA and the LPB, they unraveled an important role of these nuclei in thermoregulatory 638 behaviors (Yahiro et al., 2017). Our hypothesis is that the changes observed in the 639 hypothalamus in our study are linked to this role in thermoregulation. 640 Only a small number of FC studies in rodents are documenting networks involving 641 hypothalamic nuclei. In the rare cases doing so, they define them as the ‘ventral midbrain’ 642 (Liska et al., 2015) or the ‘basal ganglia-hypothalamus’ (Becerra et al., 2011). In agreement 643 with our observations, these networks are different from the sensory network (Hutchison et 644 al., 2010; Zerbi et al., 2015). If we postulate that this anti-correlation is due to a decrease in 645 neuronal activity, this second structure would have an inhibitory action on hypothalamic 646 neurons. However, this anti-correlation can also be due to a time lag between signals of the 33 bioRxiv preprint doi: https://doi.org/10.1101/2022.10.20.513008; this version posted October 21, 2022. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. 647 SMN and the hypothalamic. In this last case, the hypothesized structure(s) would only change 648 the temporal shift, without affecting the intensity of neuronal activity. 649 650 Sustained cold and cold ramps are encoded differentially in the cingulate cortex 651 As previously described by us and by others (Osmanski et al., 2014b; Liska et al., 2015; Zerbi 652 et al., 2015), correlation between regions of the SMN and the cingulate cortex was much 653 weaker, as it belongs to the default mode network (Liska et al., 2015). During Fast Cooling, 654 interhemispheric connectivity within the cingulate cortices increased significantly, as well as 655 the connectivity between the SM and the cingulate cortices. When the cold ramp was slow, 656 however, the FC between these regions was not affected. The thermal psychophysics 657 associated with rapid temperature changes and the already established role of the cingulate 658 cortices in affective responses to unpleasant or nociceptive thermal sensations (Craig et al., 659 1996; Becerra et al., 1999; Brooks et al., 2002; Derbyshire et al., 2002) has led us to consider 660 that the concomitant FC increase between SM and cingulate cortex suggests a common hub 661 between the two. 662 In the dynamic brain states #4 and #6, on the other hand, the cingulate cortex is differently 663 connected to the other networks during static exposure to a lower temperature (15°C). The 664 decrease in connectivity with the somato-motor network in this state seems to be a 665 characteristic of sustained exposure to cold, and this suggests the differential role of cingulate 666 in sensing persistent cold sensations and cold ramps. 667 668 34 bioRxiv preprint doi: https://doi.org/10.1101/2022.10.20.513008; this version posted October 21, 2022. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. 669 Supplementary Figures 670 671 672 673 Supplementary figure 1: No changes in functional connectivity are observed between Warm Fast Up, Down ramps and either 35°C or 25°C conditions using correlation matrices. 674 (A, B) Averaged Pearson correlation matrices of WFU (N=8) and WFD (N=8) imaging 675 sessions. (C, E). Average Pearson correlation matrix of N=8 imaging sessions at 25°C. No 676 significant FC alteration was observed. 677 678 35 bioRxiv preprint doi: https://doi.org/10.1101/2022.10.20.513008; this version posted October 21, 2022. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. 679 680 681 682 Supplementary Figure 2: Functional connectivity comparison of Warm, Slow Up (A, B) and Slow Down ramps (C, D) with 35°C and 25°C conditions using correlation matrices show limited changes. 683 (A, C) Average Pearson correlation matrices during WSU (N=8) and WSD (N=8) imaging 684 sessions. (B, D) Matrix indicating the ROI pairs with significant differences between WSU and 685 25°C and WSD and 25°C respectively. No statistical differences were observed between the 686 constant 35°C temperature and the conditions WSU and WSD. 687 688 689 36 bioRxiv preprint doi: https://doi.org/10.1101/2022.10.20.513008; this version posted October 21, 2022. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. 690 691 Supplementary Figure 3: Lack of statistical difference in the occurrence rate of the various 692 dynamic functional connectivity states in warm experiments (constant and ramps). As 693 presented in figure 5, 694 clustering with k= 7, which are ordered from 1 to 7 in the order of decreasing occurrence 695 rate. The frequency of occurrence was calculated for each brain state for all thermal 696 conditions. No significant difference could be established using a linear mixed model analysis 697 of the thermal condition. dynamic functional connectivity was analyzed using k-means 698 699 700 701 702 703 704 705 37 bioRxiv preprint doi: https://doi.org/10.1101/2022.10.20.513008; this version posted October 21, 2022. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. 706 707 Supplementary Table 1: Reporting of included animals and their number of imaging 708 sessions. I. Number of imaging sessions kept in constant temperature experiments Nb Mouse M401 M402 M403 M404 M405 M406 M302 M304 Total sessions II. Constant 25°C 1 1 2 1 Constant 15°C 1 2 2 2 1 1 1 8 Constant 35°C 1 1 2 2 1 1 1 8 8 Number of imaging sessions kept in Ramp experiments M501 M502 M503 M504 M505 M506 Total sessions M501 M502 M503 M504 M505 M506 Total sessions Cold Fast Down 3 3 2 Cold Fast Up 3 3 2 Cold Slow Down 3 Cold Slow Up 3 2 1 2 2 3 2 10 10 8 6 Warm Fast Down 3 2 2 Warm Fast Up 3 2 2 Warm Slow Down 3 Warm Slow Up 3 2 2 2 2 3 3 9 9 8 8 Supplementary Table 1: Table presenting the mice (M) included in each part of the study and the number of sessions kept for each one of them. The numbers indicate how many sessions of each mouse was kept. The number following ‘M’ is the mouse number. Ex/ M401: Mouse #401. Grey boxes: due to artifacts, the signals obtained for this recording was too noisy and had therefore to be discarded (see materials and methods). 709 38 bioRxiv preprint doi: https://doi.org/10.1101/2022.10.20.513008; this version posted October 21, 2022. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. Supplementary Table 2: Distribution of the different animals in the various brain states (related to Figure 5) 6 states Distribution of mice in the 6 brain states 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% State1 M302 M304 State2 M401 M402 State3 M403 M404 State4 M405 M406 State5 M501 M502 state6 M503 M505 7 states 710 39 bioRxiv preprint doi: https://doi.org/10.1101/2022.10.20.513008; this version posted October 21, 2022. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. 711 Supplementary Table 3: Statistical results of the mixed linear model. State 1 State 1 T35 vs T25 T15 vs T25 T25 vs CFD T25 vs CFU T25 vs CSD T25 vs CSU State 2 Pvalue 0.028923 0.0030123 0.0037124 0.036529 0.042096 0.0037124 State 1 State 1 T35 vs T25 T15 vs T25 T25 vs CFD T25 vs CFU T25 vs CSD T25 vs CSU 712 State 2 Pvalue State 2 Pvalue 0.041336 0.0016305 0.0016305 0.029618 0.029618 0.0019494 State 2 7 states State 4 State 3 State 3 T35 vs CFD T15 vs CFD T25 vs CFD State 3 Pvalue State 3 T15 vs T25 T15 vs CFD Pvalue 0.023197 0.018167 0.018167 State 4 T15 vs T25 T15 vs CFD Pvalue State 5 0.0053255 T35 vs CFD 0.0053255 T35 vs CFU T35 vs CSU T15 vs CFD T15 vs CFU T25 vs CFD T25 vs CFU 6 states State 4 Pvalue 0.033361 0.023406 State 4 T35 vs CFD T35 vs CFU T15 vs CFD T25 vs CFD CFD vs CSD State 5 State 6 Pvalue 0.0018622 0.0095948 0.040352 0.0034579 0.028186 0.003143 0.022591 State 5 Pvalue 0.0050448 0.01535 0.01535 0.01535 0.01535 State 5 T15 vs T25 State 6 State 7 Pvalue State 7 Pvalue T35 vs CFD 0.0010095 T35 vs CFU 0.014969 T35 vs CSD 0.048972 T35 vs CSU 0.039508 T15 vs CFD 0.0035432 T15 vs CFU 0.037493 T25 vs CFD 0.00031661 T25 vs CFU 0.0036998 T25 vs CSD 0.01832 T25 vs CSU 0.016621 State 6 Pvalue 0.013455 State 6 Pvalue T35 vs CFD 0.00029407 T35 vs CFU 0.014855 T35 vs CSD 0.032581 T35 vs CSU 0.018216 T15 vs CFD 0.0026806 T25 vs CFD 4.6762e-05 T25 vs CFU 0.0030647 T25 vs CSD 0.0074009 T25 vs CSU 0.0046891 713 40 bioRxiv preprint doi: https://doi.org/10.1101/2022.10.20.513008; this version posted October 21, 2022. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. 714 Data availability statement 715 Source data are available on the repository website Dryad using the following link: 716 https://doi.org/10.5061/dryad.mkkwh713t. 717 718 Code availability statement 719 Classical codes used to generate the results are available in the following depository: 720 https://doi.org/10.5061/dryad.mkkwh713t. Custom codes used for the analysis of fUS data 721 used in this study are protected by INSERM. 722 41 bioRxiv preprint doi: https://doi.org/10.1101/2022.10.20.513008; this version posted October 21, 2022. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. 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