DOI: 10.5061/dryad.sxksn039m
title: Mediodorsal thalamus and ventral pallidum contribute to subcortical regulation of the default mode network

description: Humans and other animals readily transition from externally to internally
 focused attention, and these transitions are accompanied by coactivation
 of a group of brain regions collectively known as the default mode network
 (DMN). While the DMN was considered a cortical network, recent evidence
 suggests subcortical structures are part of the DMN. Here we investigated
 the role of ventral pallidum (VP) and mediodorsal thalamus (MD) in DMN
 regulation in the tree shrew, a close relative of primates. We combine
 electrophysiology and deep learning-based motion tracking to perform
 unsupervised classification of behavioral states. We found gamma
 oscillations in VP and MD coordinated with gamma in the anterior cingulate
 (AC) cortex specifically during DMN states. Similar enhancements were
 found for high gamma, but only at subcortical sites. Cross-frequency
 coupling between gamma and delta oscillations were higher during DMN than
 other behaviors, underscoring the engagement of MD, VP, and AC circuits.
 Our findings highlight the importance of VP in DMN regulation in the tree
 shrew, consistent with rodent studies, and demonstrate a role for MD
 thalamus in DMN regulation. Our results extend homologies in DMN
 regulation among mammals, and underline the importance of thalamus and
 basal forebrain to the regulation of DMN brain states.
description: <strong>Materials and methods</strong>
 The local ethical committee on animal experimentation (canton of
 Fribourg), approved all experimental procedures.
 <strong>Animals. </strong>Three adult tree shrews,
 <em>T. belangeri,</em> of either sex were housed under a 13/11
 LD cycle in a 3 m<sup>3</sup> cage with branches, some
 enrichment elements, and <em>ad libitum</em> access of food
 and water. The cage was connected to a nest box with a tube.
 <strong>Surgical Procedures. </strong>Animals first
 received i.m injections of Alfaxan (40 mg/kg) to induce anaesthesia and
 Atropine (0.08 mg/kg) to prevent secretions. Animals were then intubated
 using a modified otoscope (Bebird, Alhambra, CA),  ventilated at 100 bpm
 (Small Animal Ventilator, Harvard Apparatus, Cambridge, MA), and placed in
 a stereotactic frame (David Kopf Instruments Tujunga, CA). Anaesthesia was
 maintained with isoflurane (1-3%) in pure oxygen, and end tidal
 CO<sub>2 </sub>was monitored (Physiosuite, Kent Scientific
 Torrington CT) and maintained at ~4%. Lidocaine (0.5 ml 1%) was injected
 near the incision site, a midline incision was made and the skull was
 exposed. Three 1.5 mm stainless-steel bone screws (WPI Hertfordshire, UK)
 were implanted with two located above the cerebellum as a reference and
 ground. Burr holes were drilled, and epoxy coated tungsten electrodes (FHC
 Bowdoin ME)  with a tip resistance ~ 150 kW were lowered to the recording
 sites: Ventral pallidum (AP 7.6 mm, ML 3.0 mm, DV -7.8 mm), anterior
 cingulate cortex ( AP 11.1 mm, ML 0.8 mm, DV -1.5 mm) mediodorsal thalamus
 (AP 4.9 mm, ML 1.0 mm, DV -5.4 mm) and primary visual cortex (AP 2.0 mm,
 ML 1.4 mm, DV -1.0 mm). All coordinates are from the interaural line.
 Electrodes were fixed to the skull with super glue (LOCTITE, Westlake OH)
 and Paladur dental cement (Kulzur Inc. Hanau Germany). Electrodes were
 wired to a socket connector, and the connector was attached to the skull
 with dental acrylic. The incision was closed about the connector with
 sutures, and the animal was allowed to recover for at least one week prior
 to testing. <strong>Data
 Acquisition.</strong> LFP and accelerometer data were collected
 using a wireless battery-powered data logger (Neurologger 2A, Zürich
 Switzerland), additionally, an infrared receiver on the neurologger was
 used for aligning the LFP and accelerometer data with video recordings.
 All channels of neural signals and accelerometer data were digitized at
 400 Hz and no further filtering was performed on the LFP data.
 <strong>Home cage Recording.</strong> Video
 recordings of the animals in their home cage were made using a wide field,
 103⁰x58⁰, CMOS camera (DS-2CD2143G0-IS, HIKVISION, Hangzhou China) mounted
 on top of the cage. After connecting the Neurologger, tree shrews were
 initially kept in their nest box for 10 min in order to acclimate to the
 Neurologger device. Home cage recordings typically lasted for 5-6 hours,
 between 9:00-18:00 during the animals’ perspective daytime.
 <strong>DeepLabCut Tracking.</strong> DeepLabCut
 (DLC) (Mathis <em>et al.</em>, 2018; Nath <em>et
 al.</em>, 2019) was used to track the animal’s location in the home
 cage. First, videos were pre-processed by cropping appropriately and down
 sampling to 2 fps, and then cut into 20 minute segments. The model was
 trained for 500,000 iterations after manually labelling 50 frames from
 each video segment. For each frame, we labelled the nose and neck. The
 output consisted of body part coordinates in x,y coordinates and the
 corresponding likelihood estimate, using the same model to analyse all the
 video segments from the same animal. In most circumstances, DLC can
 accurately track the tree shrews in most of the frames. For those videos
 with obvious missing frames, an additional 50 frames were hand labelled,
 and the network was retrained with additional 200,000
 iterations. <strong>Preprocessing and Spectral
 Analysis. </strong>We partitioned the LFP data into 0.5-s epochs for
 further analysis. Power spectra were calculated by fast Fourier transforms
 (FFT). We calculated the band power by calculating the mean value of the
 power spectrum between 40 - 60 Hz (gamma band) and 60-150 Hz (high gamma
 band). <strong>Hidden Markov
 Model.</strong> We designed an HMM with three
 states to capture the three groups of behavioural states detailed in the
 results, and four output symbols corresponding to High/High, High/Low,
 Low/High, and Low/Low combinations of ACL and DLC signal values based on
 preliminary observations of our data. The thresholds between High and Low
 sensor values were determined based on the median value of the signal
 across the recording session. We used maximum likelihood estimation to
 find HMM state transition and output symbol emission probabilities and the
 Viterbi algorithm to compute the most probable state sequence given the
 estimated parameters.  <strong>Granger
 Causality.</strong> To test the information
 transfer between VP, AC, MD, and V1 brain regions, we used LFPs and a
 multivariate linear vector autoregressive (VAR) model from Matlab
 Multivariate Granger Causality (MVGC) toolbox (Barnett and Seth, 2014) for
 granger causality analyses. The maximum model order for model order
 estimation was 20 ms, and Akaike information criteria (AIC) was used. The
 model parameter for the VAR model estimation was the locally weighted
 linear regression (LWR). We used F-testing with a false discovery rate
 (Q &lt; 0.05) for the pairwise conditional Granger causality
 estimation. Kruskal-Wallis test was applied to determine the significance
 between the information transfer directions. T-test was applied to compare
 the significance of granger causality between the DMN state and the
 exploration state. <strong>Cross-frequency
 Coupling</strong> We first filtered our raw data
 with butterworth band pass filter into delta (0.5-4 Hz) and gamma (40-60
 Hz) bands. Then we applied the Hilbert transform to extract the
 instantaneous phase from the delta band and the instantaneous amplitude
 from the gamma band. We display the cross-frequency coupling in polar
 format (Fig. 7B). <strong>Statistical analyses
 reproducibility. </strong> Experiment details are
 provided in the text and Materials and Methods section. All the
 statistical analyses were performed in Matlab. In comparison of likelihood
 in Fig.1, we applied the non-parametric Wilcoxon signed rank test as the
 data did not follow a normal distribution. For the same reason, to compare
 granger causality values within VP, AC, and MD, we used Kruskal-Wallis
 test (Fig.4G). For normally distributed data with equal variance, t-test
 or ANOVA tests were applied according to group number. In Fig.6E, we
 applied the Warren-Sarle test in order to assess the bimodality of the
 delta band amplitude distribution for sleep posture epochs. In Fig.7G, we
 used the Circular Median test (Matlab CircStat Toolbox) for a
 non-parametric multi-sample test of equal medians for circular
 data.
description: **Variables** **Fig1B** Deep Lab Cut likelihood before and after
 relabeling of misclassified frames. This data set contains 4 variables.
 Likelihood from two body parts(neck and nose) before and after relabelling
 misclassified frames, 12 video segments, 2400 frames each. nk1: Neck
 position likelihood before relabelling misclassified frames. nk2: Neck
 position likelihood after relabelling misclassified frames. ns1: Nose
 position likelihood before relabelling misclassified frames. ns2: Nose
 position likelihood after relabelling misclassified frames. **Fig1C**
 Scatter plot shows the likelihood before and after relabeling from 12
 segments of a 4-hour session. This data set contains 4 variables. NK_bf:
 Average likelihood of neck position from each video segment before
 relabelling misclassified frames. NK_af: Average likelihood of neck
 position from each video segment after relabelling misclassified frames.
 NS_bf: Average likelihood of nose position from each video segment before
 relabelling misclassified frames. NS_af: Average likelihood of nose
 position from each video segment after relabelling misclassified frames.
 **Fig1D1E1G** D, a 3-hour segment showing both accelerometer (ACL) and
 nose movement speed signals from DeepLabCut (DLC). E, a 10-minute segment
 taken from D with a detailed view of the complementary movement sensor
 signals ACL and DLC. G. Three states assigned by Hidden Markov Model based
 on ACL/DLC input. This data set contains 6 variables. ACL3h: 3 hours of
 acceleration (m/s2) data in 2fps. DLC3h: 3 hours of DeepLabCut speed
 (a.u.) in 2fps aligned with ACL3h. ACL10m: 10 minutes of acceleration
 (m/s2) data in 2fps. DLC10m: 10 minutes of DeepLabCut speed (a.u.) in 2fps
 aligned with ACL10m. st/en each has 3 cells. Each cell contains the
 starting frame(st) or ending frame(en) of HMM state 1, state 2 or state 3.
 **Fig2A2B** ** ** A.  Group analysis of the transition probability for the
 Hidden Markov Model. B. Group analysis of the output probability
 distribution of the Hidden Markov Model. Here, Lo/Hi refers to low ACL and
 high DLC activation. This data set contains 6 variables. Tr_From_state1,
 Tr_From_state2, Tr_From_state3: The transition probability.
 Tr_From_state1: Contains two dimensions. First dimension refers to the
 state that transfer from state 1 to (state 1, state 2, state 3). Second
 dimension refers to different animals (1777, 1795, 2172), all the repeats
 of probability value are in each cell. Tr_From_state2: Contains two
 dimensions. First dimension refers to the state that transfer from state 2
 to (state 1, state 2, state 3). Second dimension refers to different
 animals (1777, 1795, 2172), all the repeats of probability value are in
 each cell. Tr_From_state3: Contains two dimensions. First dimension refers
 to the state that transfer from state 3 to (state 1, state 2, state 3).
 Second dimension refers to different animals (1777, 1795, 2172), all the
 repeats of probability value are in each cell. Output_state1,
 Output_state2, Output_state3: The observation probability distribution
 matrix. Output_state1: Contains two dimensions. First dimension refers to
 the output symbols (Low/Low, High/High, Low/High and High/Low combinations
 of ACL and DLC signal) distribution in state 1. Second dimension refers to
 different animals (1777, 1795, 2172), all the repeats of probability value
 are in each cell. Output_state2: Contains two dimensions. First dimension
 refers to the output symbols (Low/Low, High/High, Low/High and High/Low
 combinations of ACL and DLC signal) distribution in state 2. Second
 dimension refers to different animals (1777, 1795, 2172), all the repeats
 of probability value are in each cell. Output_state3: Contains two
 dimensions. First dimension refers to the output symbols (Low/Low,
 High/High, Low/High and High/Low combinations of ACL and DLC signal)
 distribution in state 3. Second dimension refers to different animals
 (1777, 1795, 2172), all the repeats of probability value are in each cell.
 ** ** **Fig3A** Shows an example of the typically high correspondence
 between the states output by the HMM, and the behavioral sets derived from
 the manually labelled data.   This data set contains 3 variables.
 sort_ann: Including starting frame, end frame and the manual behavior tag.
 on: Including three cells, onset frame of HMM state 1, state 2 and state
 3. off: Including three cells, offset frame of HMM state 1, state 2 and
 state 3. **Fig3B-3D** B-D, pie charts showing the composition of different
 behaviors observed in the 3 HMM states. This data set contains 3
 variables. SLP reflects different manual labels contribution in HMM State
 1. The order is sleep posture and other. DMN reflects different manual
 labels contribution in HMM State 2. The order is quiet awake,
 eating/drinking, grooming, in or near nest box, unclassified and other.
 EXP reflects different manual labels contribution in HMM State 3. The
 order is locomotion, exploration, unclassified and other. **Fig3E** Mean
 overlap of the HMM output and manually scored behavioral sets. This data
 set contains 1 variable. ovl_all has two dimensions. First dimension
 reflects 5 datasets. Second dimension reflects the overlap percentage of
 the HMM state (HMM state 1, HMM state 2, HMM state 3) output and manually
 scored behavior sets. **Fig3F** Overall distribution of time spent in the
 different states revealed by the HMM for all animals and all sessions.
 Timespent: Contains two dimensions. First dimension refers to the
 distribution of time spent in each HMM state (HMM state 1, HMM state 2,
 HMM state 3). Second dimension refers to different animals (1777, 1795,
 2172), all the repeats of probability value are in each cell. **Fig4A**
 Example PSD of LFPs recorded from a single animal in the VP during the
 three HMM derived states. This data set contains 4 variables showing PSD
 of an example site during three HMM-defined states. Freq reflects LFP
 frequency. Sleep_PSD, DMN_PSD, Exp_PSD reflect Power Spectrum Density of
 HMM-defined sleeping posture state, DMN activation state, and
 locomotion/exploration state respectively. **Fig4B** Scatter plots showing
 the spectral power at gamma frequencies (40-60 Hz) during DMN related
 behavioral activity vs exploratory behaviors in the four brain areas. This
 data set contains 8 variables. Each has three cells, represents three
 animals (1795, 2172, 1777), V1 only has data from 2172 and 1777. V1_DMNG,
 MD_DMNG, AC_DMNG, VP_DMNG show average Gamma band PSD from different brain
 regions (V1, MD, AC, VP) during HMM-defined DMN activation state (DMN).
 V1_ExpG, MD_ExpG, AC_ExpG, VP_ExpG show average Gamma band PSD from
 different brain regions (V1, MD, AC, VP) during HMM-defined
 locomotion/exploration state (Exp). **Fig4C** Average magnitude of the
 difference in gamma power between DMN and exploration behaviors in the
 four brain areas. "G" contains 4 cells show the magnitude of PSD
 difference in Gamma band between DMN activation state and
 locomotion/exploration state, each cell represents one brain region: V1,
 MD, AC and VP respectively. **Fig4D** Same as (B,C) but for the high gamma
 band (60-150 Hz). This data set contains 8 variables. Each has three
 cells, represents three animals (1795, 2172, 1777), V1 only has data from
 2172 and 1777. V1_DMNH, MD_DMNH, AC_DMNH, VP_DMNH show average High Gamma
 band PSD from different brain regions (V1, MD, AC, VP) during HMM-defined
 DMN activation state (DMN). V1_ExpH, MD_ExpH, AC_ExpH, VP_ExpH show
 average High Gamma band PSD from different brain regions (V1, MD, AC, VP)
 during HMM-defined locomotion/exploration state (Exp). **Fig4E** Same as
 (B,C) but for the high gamma band (60-150 Hz). "HG" contains 4
 cells show the magnitude of PSD difference in High Gamma band between DMN
 activation state and locomotion/exploration state, each cell represents
 one brain region: V1, MD, AC and VP respectively. **Fig4F** Average
 directional interaction strength (Granger causality) calculated for the
 gamma band (40-60 Hz) between VP, AC, MD, V1 during DMN-related states.
 Line thickness proportional to the magnitude of the Granger Causality.
 This data set contains 1 variable. c1 includes the average gamma band
 directional interaction strength of MD&gt;V1; AC&gt;V1; VP&gt;V1;
 V1&gt;MD; AC&gt;MD; VP&gt;MD; V1&gt;AC; MD&gt;AC; VP&gt;AC; V1&gt;VP;
 MD&gt;VP; AC&gt;VP respectively. **Fig4G** Directional interactions among
 DMN-related sites (VP, AC, MD) during DMN-related states for gamma, left,
 and high-gamma band, right. This data set contains 2 variables. G_Granger
 has two dimensions. First dimension is the 15 sessions. And second
 dimension corresponds to the gamma band directional interaction strength
 during DMN state, of MD&gt;AC; MD&gt;VP; AC&gt;MD; AC&gt;VP; VP&gt;MD;
 VP&gt;AC respectively. HG_Granger also has two dimensions. First dimension
 is the 15 sessions. And second dimension corresponds to the high gamma
 band directional interaction strength during DMN state, of MD&gt;AC;
 MD&gt;VP; AC&gt;MD; AC&gt;VP; VP&gt;MD; VP&gt;AC respectively. **Fig4H**
 Scatter plots comparing overall Granger causality values within VP, AC and
 MD between DMN and exploratory behavioral states. This data set contains 4
 variables. G_Exp is the gamma band directional interaction strength of six
 directions between MD, AC and VP during locomotion/exploration state.
 G_DMN is the gamma band directional interaction strength of six directions
 between MD, AC and VP during DMN state. HG_Exp is the high gamma band
 directional interaction strength of six directions between MD, AC and VP
 during locomotion/exploration state. HG_DMN is the high gamma band
 directional interaction strength of six directions between MD, AC and VP
 during DMN state. **Fig5A5B** A, example of average gamma power (40-60 Hz)
 for behavioral epochs in chronological order throughout a single session.
 B, average gamma amplitude of all epochs for the different behaviors in
 the session shown in A. This data set contains 4 variables. Gamma_example:
 the average gamma power in VP of all the behavior epochs from one example
 session. Behavior_example: corresponding behavior labels of all the
 epochs. mean_Gamma: mean gamma power of each behavior label (sleep, quiet
 awake, eating/drinking, grooming, nest box, locomotion, exploring). epo_n:
 number of epoch belongs to each behavior label. **Fig5C** Scatter plots
 showing gamma amplitude during each of the DMN related behaviors vs gamma
 power during active states (Locomotion and Exploration) in all five manual
 labelled datasets. This data set contains 2 variables. act_amp represents
 the average gamma amplitude during active states (locomotion and
 exploration). DMN_amp has two dimention, shows the average gamma amplitude
 during each DMN state. First dimension is five datasets, second dimension
 represents different DMN states (Quiet awake, Eating&drinking,
 Grooming, Nest box). **Fig6A** Example of LFP power spectral density (PSD)
 in AC for three behavioral states. This data set contains 4 variables
 showing PSD of an example site during three HMM-defined states. Freq
 reflects LFP frequency. Sleep_PSD, DMN_PSD, Exp_PSD reflect Power Spectrum
 Density of HMM-defined sleeping posture state, DMN activation state, and
 locomotion/exploration state respectively. **Fig6B** Scatter plots showing
 delta band (0.5-4 Hz) PSD during sleep posture vs active states (including
 DMN and Exploratory states) in the four brain areas. This data set
 contains 12 variables. Each has three cells, represents three animals
 (1795, 2172, 1777), V1 only has data from 2172 and 1777. V1_DMND, MD_DMND,
 AC_DMND, VP_DMND show average Delta band PSD from different brain regions
 (V1, MD, AC, VP) during HMM-defined DMN activation state (DMN). In Fig6B,
 they are shown as active state. V1_ExpD, MD_ExpD, AC_ExpD, VP_ExpD show
 average Delta band PSD from different brain regions (V1, MD, AC, VP)
 during HMM-defined locomotion/exploration state (Exp). In Fig6B, they are
 shown as active state. V1_SLPD, MD_SLPD, AC_SLPD, VP_SLPD show average
 Delta band PSD from different brain regions (V1, MD, AC, VP) during
 HMM-defined sleeping posture state (SLP). **Fig6C** Average magnitude of
 the difference between delta band PSD during sleep posture and two active
 states (DMN and Exploration). This data set contains 2 variables. "
 DMNd " contains 4 cells show the magnitude of PSD difference in Delta
 band between sleep posture state and DMN activation state, each cell
 represents one brain region: V1, MD, AC and VP respectively. " EXPd
 " contains 4 cells show the magnitude of PSD difference in Delta band
 between sleep posture state and locomotion/exploration state, each cell
 represents one brain region: V1, MD, AC and VP respectively. **Fig6D**
 Average delta amplitude of each epoch in chronological order throughout
 one session. This data set contains 2 variables. Delta_example: the
 average delta power in AC of all the behavior epochs from one example
 session. Behavior_example: corresponding behavior labels of all the
 epochs. **Fig6E** Warren-Sarle test of bimodality showing the delta
 amplitude distribution of sleep posture epochs was bimodal for the example
 session. This data set contains 4 variables. delta1: the delta amplitude
 range. occurences1: the occurrences in each delta amplitude range during
 sleep posture state. delta2: the delta amplitude range. occurences2: the
 occurrences in each delta amplitude range during other states (DMN state
 and locomotion/exploration state). **Fig6F** Across the datasets, the
 percentage of sleeping posture in epochs with high delta activity
 occurred. This data set contains 1 variable. perc_sws: The percentage of
 sleep posture state occurrence in epochs with high delta activities from 5
 datasets. **Fig7A** A raw segment of the LFP taken from MD thalamus with
 prominent delta and gamma activity is shown at top, at the bottom the
 amplitude of delta and gamma power for the same segment. This data set
 contains 4 variables. RawData reflects a LFP raw data segment from MD.
 Time reflects the time point of the data points. Delta shows the delta
 band (0.5-4 Hz) filtered from the example segments. Gamma_env shows the
 gamma band (40-60 Hz) envelope from the example segments. **Fig7B** The
 delta-gamma cross-frequency coupling in polar format. The cfc vector is
 computed to the centroid of the angular cfc distribution This data set
 contains 4 variables. px, py: coordinates of cfc distribution in polar
 format of an example dataset. centroidx, centroidy: centroid coordinate of
 the angular cfc distribution. **Fig7CDE** The cfc vector distribution for
 sleeping posture C, DMN behaviors D, locomotion/exploration E, in MD, AC
 and VP. This data set contains 3 variables. xycentroidSLP shows the
 centroid coordinates of the angular cfc distribution of each dataset in
 HMM-defined sleep posture state. It has two dimensions, first dimension is
 the brain regions (MD, AC, VP) and second dimension is the different
 datasets. xycentroidDMN shows the centroid coordinates of the angular cfc
 distribution of each dataset in HMM-defined DMN state. It has two
 dimensions, first dimension is the brain regions (MD, AC, VP) and second
 dimension is the different datasets. xycentroidEXP shows the centroid
 coordinates of the angular cfc distribution of each dataset in HMM-defined
 locomotion/exploration state. It has two dimensions, first dimension is
 the brain regions (MD, AC, VP) and second dimension is the different
 datasets. **Fig7FG** F, Cfc coupling strength in MD, AC and VP. G, cfc
 preferred angle in MD, AC and VP. This data set contains 4 variables.
 CouplStrength shows the mean cfc coupling strength. Data has two
 dimensions, first dimension is the brain regions (MD, AC, VP) and second
 dimension is the different HMM behavioral states (HMM-defined sleep
 posture state, HMM-defined DMN state, HMM-defined locomotion/exploration
 state). CouplStrengthSD shows the standard deviation of cfc coupling
 strength. Data has two dimensions, first dimension is the brain regions
 (MD, AC, VP) and second dimension is the different HMM behavioral states
 (HMM-defined sleep posture state, HMM-defined DMN state, HMM-defined
 locomotion/exploration state). PrefAngle shows the mean cfc preferred
 angle. Data has two dimensions, first dimension is the brain regions (MD,
 AC, VP) and second dimension is the different HMM behavioral states
 (HMM-defined sleep posture state, HMM-defined DMN state, HMM-defined
 locomotion/exploration state). PrefAngleSD shows the standard deviation of
 cfc preferred angle. Data has two dimensions, first dimension is the brain
 regions (MD, AC, VP) and second dimension is the different HMM behavioral
 states (HMM-defined sleep posture state, HMM-defined DMN state,
 HMM-defined locomotion/exploration state).