Striatal Astrocytes Influence Dopamine Dynamics and Behavioral State Transitions

Striatal Astrocytes Influence Dopamine Dynamics and Behavioral State Transitions | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results Striatal Astrocytes Influence Dopamine Dynamics and Behavioral State Transitions View ORCID Profile Iakovos Lazaridis , View ORCID Profile Gun Ahn , View ORCID Profile Kojiro Hirokane , View ORCID Profile Wonchang Choi , View ORCID Profile Ann M. Graybiel doi: https://doi.org/10.1101/2024.12.01.626240 Iakovos Lazaridis 1 McGovern Institute for Brain Research and Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology , Cambridge, MA 02139, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Iakovos Lazaridis Gun Ahn 1 McGovern Institute for Brain Research and Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology , Cambridge, MA 02139, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Gun Ahn Kojiro Hirokane 1 McGovern Institute for Brain Research and Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology , Cambridge, MA 02139, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Kojiro Hirokane Wonchang Choi 2 Department of Computer Science and Engineering, Sogang University , Seoul, Republic of Korea Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Wonchang Choi Ann M. Graybiel 1 McGovern Institute for Brain Research and Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology , Cambridge, MA 02139, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Ann M. Graybiel For correspondence: graybiel{at}mit.edu iaklaz{at}mit.edu Abstract Full Text Info/History Metrics Preview PDF Abstract We demonstrate here that astrocytes in the striatum interact with striatal dopamine in bidirectional signaling with dopamine release actively driving surges in astrocytic Ca ++ , which in turn modulate and reduce subsequent dopamine release. These Ca ++ surges accurately predict behavioral state changes from task-engaged to task-disengaged states, but fail to predict detailed action parameters. We propose that interactions between striatal astrocytes and dopamine are strong candidates to modulate nigro-striato-nigral loop function underlying on-going behavioral state dynamics. Astrocytes outnumber neurons in the brain and are now recognized as having remarkably active roles in signaling that affects neural circuits and behavior 1 – 18 . As an example, through expression of noradrenergic Adra1 receptors, neocortical astrocytes sense norepinephrine and, through this, mediate neuromodulator function and behavior 19 – 21 . We here examined interactions between astrocytes and dopamine in the subcortically situated striatum, monitoring their effects on circuit function in this major station of the basal ganglia. Striatal astrocytes express both D1 and D2 dopamine receptors 22 – 25 , and thus are potential key players in dopaminergic signaling 25 as already indicated in their relation to repetitive and habitual behaviors 26 , 27 . The precise nature of this astrocyte-dopamine communication, however, and its behavioral impact remain incompletely understood despite pioneering work done in the nearby ventral striatum 25 . Recognizing the critical need to identify control mechanisms underlying functions of the canonical nigro-striato-nigral loop system, which links the dorsal striatum and dopamine-containing substantia nigra and is implicated in motor and neuropsychiatric disorders, we focused on two key unsolved issues: how striatal astrocytes respond to dopaminergic signaling, and whether astrocytes, in turn, modulate dopaminergic signaling to influence behavior. We employed behavioral settings including self-initiated movement in open-chamber conditions and self-initiated performance of a reinforcement-based decision-making task in a T-maze context. We applied optogenetic stimulation to dopamine-containing neurons in the pars compacta of the substantia nigra (SNpc) by the use of nigral AAV-DIO-Chrimson injections in DAT-Cre mice, free to behave in an open field (30 x 30 cm), and we recording astrocytic Ca ++ activity with AAV- GFAP-GCaMP6f injected into in the dorsomedial (DMS) and dorsolateral (DLS) sectors of the striatum ( Fig. 1A ). We observed clear frequency-dependent Ca ++ responses in striatal astrocytes ( Fig. 1A-D ). In parallel experiments, this stimulation also triggered dopamine release, mirroring the frequency-dependent astrocytic activity ( Fig. 1E-H ), establishing a potential functional link between dopamine levels and astrocytic activity. Dopamine release levels remained consistent ( Fig. 1G ), whereas astrocytic Ca ++ responses diminished over time, indicating accommodation ( Fig. 1C ). This result suggests activation of an intrinsic regulatory mechanism in astrocytes in response to sustained dopaminergic input. Download figure Open in new tab Fig. 1 Striatal astrocytic response to optogenetic stimulation of SNpc dopamine-containing neurons. A, Schematic of experimental setup for optogenetic stimulation of the SNpc and simultaneous Ca ++ imaging of striatal astrocytes. B, Average astrocytic response to 10 optogenetic pulses (593 nm wavelength, 10 mW power, 5 ms pulse length, 40 Hz, 4 s train duration). C, Representative astrocytic response trace from panel B . D, Mean astrocytic responses across stimulation frequencies from a representative mouse (left), and astrocytic responses across stimulation frequencies from 6 mice (right). E-H, Dopamine release induced by the same stimulation protocol as in panels A-D . E, Schematic of experimental setup for optogenetic stimulation of SNpc with imaging striatal dopamine release. F, Average dopamine response to 10 optogenetic pulses (593 nm wavelength, 10 mW power, 5 ms pulse duration, 40 Hz, 4 s train duration). G, Representative dopamine response trace from panel F . H, Mean dopamine release across stimulation frequencies for a representative mouse (left), and dopamine release across stimulation frequencies from 6 mice (right). In panels D (left) and H (left), the mean z-score is shown for the pre-stimulation (−6 to 0 s) and stimulation (1 to 7 s) periods. Paired t- test: *p < 0.05, **p < 0.01, ***p < 0.001. I-K, Astrocytic response suppression by repeated SNpc stimulation and response loss under anesthesia. I, Representative paired-pulse ratio (PPR) for inter-pulse intervals (IPIs) ranging from 3.75 to 120 s (593 nm wavelength, 10 mW power, 5 ms pulse length). J, Overlaid astrocytic response traces from panel A . K, PPRs from 6 mice. L-M, Isoflurane anesthesia abolishes astrocytic responses but does not affect dopamine release in response to optogenetic stimulation of SNpc neurons. Astrocytic responses recover as mice awaken. L, Representative traces of astrocytic activity (green) and dopamine release (red) before, during, and after isoflurane anesthesia. M, Quantification of isoflurane’s effects on astrocytic activity (n = 3) and dopamine release (n = 3). Paired t-test: *p < 0.05, **p < 0.01, ***p < 0.001. We next applied a paired-pulse ratio (PPR) protocol with optogenetics. We delivered a second pulse at varying intervals after the first (pulse duration of 5 ms and interpulse intervals of 3.75, 7.75, 15, 30, 60 and 120 s). Astrocytic response to the second SNpc stimulation was diminished, indicating the presence of a refractory period in their responses ( Fig. 1I-K ), decreased with shorter intervals; peak suppression occurred between 7.5 and 15 s, persisting up to 120 s. Short intervals (e.g., 3.75 s) sometimes amplified the second response, likely due to overlap with the first response’s rising phase. Full recovery was not achieved even after a 2-min gap. With repeated stimulations (10 pulses, interpulse interval 120-160 s), the first pulse consistently triggered the strongest response ( Fig. 1K ), suggesting a novelty effect, but these diminished with subsequent simulations. We also examined astrocytic responses under isoflurane anesthesia, which suppresses striatal activity. With the initial 40 Hz protocol ( Fig. 1A-H ), dopamine release remained intact, but astrocytic Ca ++ responses were abolished ( Fig. 1L,M ). Astrocytic activity returned after recovery, suggesting that the suppression reflects network or state-dependent modulation. Direct isoflurane effects on the astrocytes may have occurred 28 , but our findings demonstrate that astrocytes are highly sensitive to network dynamics, potentially decoupling their activity from dopamine release. To explore the reverse relationship—how astrocytes, when activated, might modulate dopaminergic signaling—we injected AAV-GFAP-Cre and AAV-DIO-Chrimson for optogenetic stimulation of striatal astrocytes and monitored striatal dopamine release with AAV-hSyn-DA3h dopamine sensors implanted in the DLS and DMS. The stimulation protocol consisted of 10-s pulses, matching the slow and prolonged astrocytic Ca ++ responses. Separate optic fibers were used to stimulate astrocytes and to record either Ca ++ activity ( Fig. 2A ) or dopamine release ( Fig. 2C ), necessary to provide real-time measurement of potential astrocytic modulation of dopaminergic signaling. Download figure Open in new tab Fig. 2 Optogenetic stimulation of astrocytes reduces dopamine levels without altering motor kinematics. A, Schematic of optogenetic stimulation of astrocytes (593 nm wavelength, 5 mW power, 10 s pulse duration) during imaging of astrocytic Ca ++ activity. B, Astrocytic Ca ++ activity before and during 8-s optogenetic stimulation across trials (4 mice). C, Schematic of optogenetic stimulation of astrocytes 593 nm wavelength, 5 mW power, 10 s pulse duration) during imaging of dopamine release. D, Dopamine release (DA3h sensor) before and during optogenetic stimulation across trials (3 mice). E-K, Effects of astrocyte stimulation on motor kinematics: E, Trajectories of mice freely moving in open field arena (30 x 30 cm) before (green), during (red), and after (blue) optogenetic stimulation of astrocytes. F, Reoriented trajectories aligned to the axis defined by the base of the tail to the neck one frame before stimulation, before, during, and after optogenetic stimulation for 10 trials (colors as in E). G, Distance traveled during and after 10-s optogenetic stimulation relative to 10 s before stimulation. H, Time spent in different activity levels—immobile (6 cm/s)—before, during, and after optogenetic stimulation of astrocytes. I, Behavioral motif clusters in 3D space using UMAP. Nineteen unique motifs were identified by B-SOiD clustering analysis, with each color representing a distinct behavioral motif cluster (n = 5 mice). J, Box plot showing the ratio of motif usage between stimulation-on and stimulation-off periods for 5 mice. Motif usage rates were measured in frames/s and analyzed for statistical differences using a two-tailed t-test. No significant differences were found. K, A-SOiD clustering of motor actions (walking, immobility, sniffing, rearing, and grooming) before and during optogenetic stimulation of astrocytes. No significant differences were found. Optogenetic activation of astrocytes increased their Ca ++ activity ( Fig. 3A,B ) and led to a significant decrease in dopamine release within the striatum ( Fig. 3C,D ). This inverse relationship indicates that astrocytes actively modulate dopaminergic signaling, likely by regulating local dopamine release or its extracellular availability. The reduction in dopamine release during astrocyte activation offers strong evidence for bidirectional communication between astrocytes and dopamine levels, suggesting that astrocytes can shape the dopaminergic environment in response to their activation. Download figure Open in new tab Fig. 3 Astrocytes signal behavioral state shifts and engagement during task performance. A, Experimental approach. B, Representative brain section showing fiber placement for simultaneous imaging of dopamine release and astrocyte activity in the right DLS and left DMS (left), and mapping of fiber placements from 4 mice (right). C, Schematic of behavioral setup. D, Video frame of behavior with DeepLabCut (DLC)-labeled mouse paws, nose, and tail base. E, Calculated action value (Q-learning) and corresponding choices of the mice from a representative session. F, Right choice probability as a function of trial action value from the Q-learning model across 5 mice. G, Average photometry traces (mean ± SEM) showing the activity of dopamine (left) and astrocytes (right) recorded in the DMS (top) and DLS (bottom) during the task. Activity was averaged separately for ipsilateral and contralateral choice trials and normalized across time. Shaded regions indicate the initiation, turn, and choice phases of the trial. H, Dopamine release and astrocyte activity do not differentiate between rewarded and unrewarded outcomes. I, Dopamine and astrocyte signals during the task (representative trace, top), and cross-correlation of astrocyte and dopamine signals for DLS and DMS from 67 sessions with 5 mice (bottom). Box plots show the mean ± STD for the peak and the corresponding lags of cross-correlation during task performance. J, Astrocyte activity peaks in relation to the behavioral IDF. Top: A trace of the IDF plotted as a function of engagement levels. Middle: A recorded trace of astrocyte Ca ++ activity during task execution, with visual representation of initiation events. K, Ratio usage of kinematic B-SOiD clusters representing task-unrelated behaviors (light blue: immobility, light orange: grooming, light green: scratching) and task-related behaviors (light purple: initiation and choice ports). The blue boxes illustrate the comparison between engagement and disengagement periods based on IDF-defined thresholds (IDF >0.5, duration >15 s). The red boxes (DMS astrocyte Ca ++ peaks) and orange boxes (DLS astrocyte Ca ++ peaks) represent the ratio usage of clusters after versus before astrocyte peak signals. This comparison demonstrates that astrocyte peaks produce a similar distinction between task-related and task-unrelated clusters as the engagement criteria, supporting a link between astrocyte peak timing and behavioral state. L, Proportion of large astrocyte peaks (>99th percentile amplitude) observed during engaged and disengaged periods in both the DMS and DLS. The plot demonstrates that these large peaks are predominantly associated with disengaged periods. M, Time lag of large astrocytic peaks relative to the start of the next engagement period and the end of the previous disengagement period in both the DMS and DLS. The plot demonstrates that astrocyte peaks are closely coupled in time with the start of the next engagement period. N, Dopamine release signal and IDF in relation to astrocyte activity peaks. Dopamine peaks just before the astrocyte peak, followed by a marked elevation in the IDF, indicating that engagement peaks after the astrocyte peak. Data represent 67 sessions across 5 mice. O, Left. Confusion matrix showing the accuracy of the astrocyte signal (analyzed across the entire signal, not limited to peaks) in predicting task space parameters, including trial initiation, turn, rewarded outcome, unrewarded outcome, incorrect choice (error), and air puff. Right. Confusion matrix showing the accuracy of the astrocyte signal in predicting state transitions between engagement and disengagement periods. We next optogenetically stimulated astrocytes and specifically monitored the movement of the mice exploring the open field. Despite the strong influence of astrocytes on dopamine dynamics, optogenetic stimulation did not significantly affect general locomotor activity or specific motor behaviors voluntarily enacted by the mice. Movement trajectories, distances traveled, and time spent in different activity states—immobile, mobile, and running—remained consistent before, during, and after stimulation ( Fig. 2E-H ). Behavioral motif clustering (B-SOiD) and action-specific clustering (A-SOiD) indicated no significant changes in behaviors such as walking, sniffing, rearing, or grooming across stimulation periods ( Fig. 2I-K ). We interpreted these findings as suggesting that astrocytes might not perform an essential function in the enactment of motor behavior. Rather, their bidirectional interaction with dopaminergic neurons might indicate a more complex function in regulating cognitive or motivational processes by modulating dopamine signaling in the striatum. Building on these findings, we asked how this astrocyte-dopamine interaction influenced putative higher-order functions in the context of reinforcement-related decision-making. With a two-choice probabilistic switching T-maze task 29 ( Fig. 3A-F ), we asked whether astrocytes participate in the shaping of decision-making behavior by modulating dopaminergic circuits. We simultaneously monitored striatal astrocytic Ca ++ activity and dopamine release by combining fiber photometry with AAV-GFAP-GCaMP6f for astrocyte imaging and AAV-DA3h for dopamine release imaging. We imaged the DMS and DLS in separate hemispheres to avoid large damage with a single-hemisphere two-probe protocol. The mice were required to adapt their choices based on probabilistic reinforcement schedules, encouraging a “win-stay, lose-switch” strategy. We observed coordinated fluctuations of dopamine release and astrocytic Ca ++ activity in both the DMS and DLS, with similar activity for ipsilateral and contralateral choices. Astrocytic activity was very low during all phases of task performance compared to the dopamine release levels measured. Distinct patterns of activity occurred during the initiation, turn and choice phases of the task ( Fig. 3G ) Dopamine release levels were dynamically modulated, with release increasing during the runs, starting from initiation port exit and peaking after the turn into the choice arm. By contrast, astrocytic activity, though low in amplitude, followed an almost inverted dynamic pattern, peaking in the initiation port and reaching its lowest during the corridor run ( Fig. 3G ). Dopamine release varied between rewarded and unrewarded outcomes, but the levels were largely influenced by the timing of exit and duration in the choice port rather than reward-taking. Astrocytic activity was comparable across rewarded and unrewarded outcomes ( Fig. 3H ). We identified strong cross-correlations between the astrocytic Ca ++ activity and the dopamine release signals ( Fig. 3I ) that indicated a bidirectional relationship between them. Dopamine release consistently preceded astrocytic activation, with astrocytic activity subsequently followed by decreased dopamine. Cross-correlation analysis indicated that astrocytic Ca ++ responses followed dopamine peaks by about 2 s, whereas dopamine suppression was correlated with astrocytic activation 8 s after the astrocytic peaks. These findings align with those of our optogenetic experiments in suggesting a dynamic feedback loop between astrocytes and dopamine release in the striatum. The timing of these interactions reflects a coordinated process, wherein astrocytes both respond to and modulate dopamine levels over different time scales. This reciprocal communication could enable astrocytes to regulate the onset of engagement and disengagement. The proportion of large astrocytic peaks (>99th percentile amplitude) was significantly higher during disengaged periods in both the DMS and DLS ( Fig. 3J-M ), and they were closely aligned in time with the start of the next engagement period, as shown by the time lag between these peaks and behavioral transitions ( Fig. 3M ). These results suggested a strong association between the large astrocytic transients and disengagement. In fact, taking just the astrocyte peaks yielded an in-task versus out-of-task cluster usage pattern that closely mirrored the behavioral engagement-disengagement definition, reinforcing the link between astrocyte peak activity and behavioral state ( Fig. 3K ). We then examined the relative timing of the astrocyte Ca ++ peaks and dopamine release to determine whether these astrocyte surges were more related to disengagement or to re-engagement. We found that dopamine release peaked just before the astrocyte peaks, followed by a marked increase in the initiation density function (IDF) after the astrocyte peaks, marking the transition from a disengaged state to an engaged state ( Fig. 3N ). This pattern suggests that astrocyte surges precede shifts to engagement, indicating that these large astrocytic activations parallel or directly influence in the preparation of the mice to re-engage in the task. Deep learning analyses demonstrated that astrocyte surges were good predictors of behavioral state transitions but poor predictors of specific behavioral task parameters. Specifically, confusion matrix analyses demonstrated that the astrocytic signal was not a strong predictor of task parameters, such as trial initiation or choice outcomes ( Fig. 3O left). However, the astrocytic signal accurately predicted shifts between engaged and disengaged periods, indicating a strong link to behavioral state ( Fig. 3O right). Our findings establish astrocytes as active regulators of striatal dopamine signaling, particularly during behavioral state transitions. We observed a strong cross-correlation between dopamine release and astrocytic Ca ++ activity, with astrocytes not only responding to dopaminergic inputs but also modulating dopamine dynamics in a feedback loop. Large astrocytic transients occurred during disengagement, peaking just before re-engagement. This temporal relationship suggests a function in preparing the striatum for a change in behavioral state. Our findings further point to astrocytes as influential players in decision-making and behavioral flexibility. This astrocyte involvement in dopaminergic signaling could point toward modulation-based therapeutic strategies for disorders associated with dopamine-related circuits and behavioral engagement, including Parkinson’s disease and Alzheimer’s disease as were as autism-spectrum disorders. Methods Mouse lines and husbandry For the experiments, we used the DAT-Cre knock-in mouse line (Common Name: DAT IRES cre , Slc6a3tm1.1(cre)Bkmn ; RRID: IMSR_JAX:006660). This line was purchased from The Jackson Laboratory and maintained on a C57BL/6J background. In addition to the DAT-Cre line, we utilized wild-type C57BL/6J mice (RRID: IMSR_JAX:000664) also purchased from The Jackson Laboratory. All procedures were approved by the Committee on Animal Care at the Massachusetts Institute of Technology, which is AAALAC accredited. Both female and male experimental mice were housed under a standard 12/12-hour light/dark cycle with free access to food and water. For the T-maze probabilistic two-choice switching task, mice were single-housed and placed on water restriction during behavioral training and recordings (1.5-2.5 g hydrogel per day maintained at minimum 85% of their free-water body weight). Stereotactic injections and implantation General Procedure Mice were anesthetized with 2% isoflurane and positioned in a stereotaxic frame (Stoelting). Prior to the first incision, Buprenorphine (0.1 mg/kg) and the local analgesic Xylocaine/Lidocaine (4 mg/kg) were administered subcutaneously for pain relief. Body temperature was maintained at 36°C using a feedback-controlled heating pad. Viral injections were performed using a micropipette attached to a Quintessential Stereotaxic Injector (Stoelting). The pipette remained in place for 5 min post-injection to ensure proper viral diffusion before being slowly withdrawn. Post-surgical care included Maloxicam (5 mg/kg) administered immediately after the procedure, with a second dose given 18-24 hr later. Optogenetic Stimulation for Dopamine Release with Imaging of Astrocyte Ca ++ Activity and Dopamine Release In DAT-Cre mice, AAV-DIO-Chrimson (400 nl) was injected into the SNpc (AP: −3.08 mm, ML: ±1.25 mm, DV: −4.0 mm). AAV-hSyn-DA3h or AAV-GFAP-GCaMP6f (400 nl) were injected into the right DLS (AP: 1.1 mm, ML: 2.15 mm, DV: 1.95 mm) and DMS (AP: 1.1 mm, ML: 1.1 mm, DV: 2.2 mm). Optical fibers were implanted above the injection sites at angles of −10° for DMS and +10° for DLS. Optogenetic Stimulation for Astrocytes with Imaging of Astrocyte Ca ++ Activity and Dopamine Release For astrocytic stimulation, AAV-GFAP-Cre and AAV-DIO-Chrimson (total volume 400 nl) were injected into the right DLS (same coordinates as above) and DMS (same coordinates as above). AAV-hSyn-DA3h or AAV-GFAP-GCaMP6f (400 nl) were also injected into these sites. Optical fibers were implanted above the injection sites at angles of −10° for DMS and +10° for DLS. Simultaneous Imaging of Dopamine Release and Astrocyte Ca ++ Activity AAV-GFAP-GCaMP6f and AAV-DA3h (400 nl) were injected into the right DLS and left DMS (same coordinates as above). Optical fibers were implanted above the injection sites. Tissue preparation, immunolabeling, and microscopy Mice were anesthetized with Euthasol (pentobarbital sodium and phenytoin sodium, Virbac AH Inc.) and transcardially perfused with 0.9% saline, followed by 4% (wt/vol) paraformaldehyde in 0.1 M NaKPO 4 buffer (PB). Brains were dissected, post-fixed for 90 min, and stored in 20% (vol/vol) glycerin sinking solution overnight or longer. The brains were then sectioned transversely at 30 µm on a freezing microtome and stored in 0.1% sodium azide in 0.1 M PB until use. Free-floating sections were rinsed three times (2 min each) in 0.01 M NaKPO 4 buffer with sodium/potassium saline and 0.2% Triton X-100 (PBS-Tx), then blocked for 20 min using TSA Blocking Reagent (PerkinElmer). Sections were incubated overnight at 4°C on a shaker with primary antibodies suspended in TSA Blocking Reagent. Following primary incubation, sections were rinsed three times in PBS-Tx and incubated with Alexa Fluor secondary antibodies (Thermo Fisher Scientific) for 2 hr at room temperature. After secondary incubation, sections were rinsed three times in 0.1 M PB. Images were processed and analyzed using Fiji software. Background subtraction and contrast adjustments were performed for presentation purposes, and figures were prepared using Adobe Photoshop and Illustrator. Assessment of optical fiber placement was based on visible lesions from the fiber in the tissue. Mice with misplaced fibers or insufficient viral expression were excluded from the study. Photometry recordings Ca ++ activity was recorded using GCaMP6f and the red-shifted dopamine sensor DA3h with a two-color fiber photometry system (Doric Lenses). This setup enabled sequential fluorescence recording at a sampling rate of 10 Hz across three channels: a reference channel (400-410 nm), a green channel (460-490 nm), and a red channel (555-570 nm). Video recordings of mouse behavior were synchronized with photometry signals, tracking the xy-coordinates of five body parts (tip of the tail, base of the tail, center of the body, neck, and snout/head) using DeepLabCut software 30 . Task events, optogenetic stimulation, and both video and photometry frames were timestamped using Bonsai software, which interfaced with external hardware via an Arduino microcontroller running the Firmata-plus protocol ( https://github.com/firmata/protocol ). Photometry data preprocessing involved scaling the 400-410 nm control signal to the 460-490 nm (neuronal activity) or 555-570 nm (dopamine sensor) signals using linear least-squares regression, followed by subtraction to eliminate motion and autofluorescence artifacts. Baseline fluorescence was estimated using a least-squares regression line applied to the scaled control signal. The corrected activity signal was then normalized against the raw baseline, resulting in a ΔF/F trace that was adjusted for photobleaching, motion, and autofluorescence. Open field In the open field test, mice underwent overnight water restriction and freely explored a 30 x 30 cm arena for 15 min to assess locomotor activity. The arena’s transparent acrylic floor allowed for video capture from below using a high-resolution Oryx 10GigE camera (Flir) recording at 30 Hz. The video was synchronized with fiber photometry data, capturing neural activity, dopamine release, and behavioral patterns, enabling the correlation of locomotor behaviors with neural and dopamine dynamics. Movement was analyzed using DeepLabCut, with 1,000 frames manually labeled to identify key body parts (tip of the tail, base of the tail, center of the body, neck, and snout). Video coordinates in pixels were converted to centimeters using the arena’s dimensions. Optogenetic stimulation of Chrimson was delivered via a 593 nm laser. Dopamine neurons in the SNpc were stimulated for 4 s per trial, whereas astrocytes were stimulated for 10 s, with 10 trials and randomized inter-trial intervals of 1.5 min. T-maze probabilistic two-choice switching task We modified a version of the probabilistic two-choice switching task, adapted to a T-maze to spatially and temporally extend action selection, to facilitate the identification of action value encoding in neural signals. Water restricted mice were trained to choose between two options, with reinforcement outcomes that intermittently switched without warning. A 3-μl sucrose reward was delivered at the initiation port, while an air puff (5% of incorrect trials) served as negative reinforcement. This setup encouraged a “win-stay, lose-switch” strategy, requiring mice to adapt their choices based on changing reinforcement outcomes. One port had a 75% chance of providing a sucrose reward, and the other was non-reinforced. The reinforcement schedule switched randomly every 7-24 rewarded trials. Video data, processed with Bonsai software, enabled real-time detection of corridor end zone entries. Two Arduino microcontrollers automated the waterspouts and interfaced with Bonsai using the Firmata protocol. High-resolution video frames from a FLIR-Oryx 10GigE camera, recorded at 30 frames/s, were synchronized with the photometry setup, which recorded neural activity and dopamine release at a 10-Hz sampling rate across three channels: reference (400-410 nm), green (460-490 nm), and red (555-570 nm). Behavior and action value were analyzed using a Q-learning model for each animal, assessing adjustments in decision-making strategies in response to dynamic changes in reinforcement. Action value estimation using Q-learning Behavior and action values were analyzed using a Q-learning model for each mouse individually, assessing adjustments in decision-making strategies in response to dynamic changes in reinforcement. We used the Q-learning model to calculate the action values for different choices made during the task because it allowed us to update the estimated value of each option based on reinforcement outcomes. We used the following update rule for action values: where Qp(t): Current action value of choosing port p at time t, : Learning rate, which controls how quickly the values are updated, R(t): Outcome received at time t (1 for a reward, 0 for an omission, W airpuff : Penalty associated with receiving an airpuff, and A(t): Binary indicator of whether an airpuff trial occurred. We then used a logistic function to calculate the probability of choosing the right port: where β : Parameter controlling the slope of the function and the explore-exploit trade-off, and b: Static bias toward one side. We first estimated the parameters, W airpuff , and b for each mouse individually by minimizing the negative log-likelihood: The parameters were optimized using the downhill simplex algorithm implemented through the scipy.optimize.fmin function in Python. However, we observed a significant number of outliers in the W airpuff values, likely due to the limited number of trials in each session. To address this, we recalculated the mean value of W airpuff across sessions and used it as a default value, then refitted only the parameters β and b using adjusted mean of W airpuff . Signal analysis Signal data were processed using custom Python scripts. Five key events were identified for analysis: Trial Start, Initiation Port Exit, Turn, Choice Port Entry, and Choice Port Exit. For each event, median inter-event intervals were calculated from successive pairs of events. These intervals were then used to define symmetric time windows, extending from half the median interval before each event to half the median interval after. By combining these windows, we created a comprehensive visualization of all trials, allowing for variations in inter-event intervals and enabling a detailed examination of the temporal dynamics across different trial phases. Cross-correlation For each trial, astrocyte Ca ++ signal was compared with dopamine release signal using the MATLAB crosscorr function. Cross-correlation coefficients were calculated across time lags ranging from −10 s to +10 s. The coefficients were then plotted against the lag values, where positive lags indicate the first signal leads the second, and negative lags indicate the second signal leads the first. Kinematic analysis Kinematic data were derived from body-part coordination annotations provided by DeepLabCut. Movement speed was determined using the body center coordinates. The body twist angle was defined as the angle formed between vectors from the “tail base to body center” and “body center to neck.” Similarly, head twist angle was calculated between the vectors from the “body center to neck” and “neck to snout.” Kinematic data from all mice were aggregated to visualize the overall distribution density of each variable. Angular velocity histograms were created with 0.2 degree/s bins spanning 0 to 20 degrees/s (100 bins total) and smoothed over 5 bins for clarity. Movement speed histograms used 1 frame/s bins ranging from 0 to 1000 frames/s (1000 bins total), smoothed over 50 bins. The histograms were normalized by converting them into distribution densities, ensuring the area under each curve was 1. The average angular velocities for head and body twists and movement speeds were calculated for each mouse, with data segmented by motif. Comparisons between stimulation-off and stimulation-on periods were conducted using two-tailed t-tests to examine the effects of light stimulation on movement kinetics. Box plots were used for visualization, supplemented by swarm plots showing the ratio of kinematic variables during stimulation-on to stimulation-off periods. Initiation density function (IDF) To examine the temporal relationship between astrocyte Ca ++ activity, dopamine signaling, and behavioral task initiations, we computed the IDF, inspired by the spike density function. The IDF quantifies the density of task initiation events, reflecting engagement levels over time. Binary initiation events were convolved with a Hanning window spanning 20 s (600 data points, sampled at 30 Hz) to generate a continuous representation of initiation density. Higher IDF values indicate periods of denser initiation activity, representing higher engagement, within the time window. Astrocyte Ca ++ activity peak detection Astrocyte Ca ++ activity was assessed using the fluorescence signal following z-score normalization. Peaks in astrocyte activity were defined as values exceeding the 99th percentile within each session, isolating prominent activity events. For each detected astrocyte peak, a ±10- s window (300 data points on each side) was extracted to capture pre-peak and post-peak activity. Data alignment and averaging For each astrocyte peak, the corresponding IDF and dopamine Ca ++ activity (z-scored fluorescence) were extracted within the ±10-s window. The IDF was normalized to its maximum value within each session. Session-specific IDF and dopamine activity traces were averaged across all astrocyte peaks, and these session-level averages were further combined across 67 sessions from 5 mice to generate group-level averages for IDF, astrocytic, and dopaminergic activity. Behavioral motif analysis Behavioral motifs were identified using B-SOiD, a tool for clustering behaviors into short movement motifs. Videos were preprocessed with DeepLabCut, which annotated six body parts: snout, tail base, left front paw, right front paw, left hind paw, and right hind paw. The average motif usage frequency (in frames/s) was calculated for both stimulation-on and stimulation-off periods. The stimulation-on period was defined as the 8-s duration of optical stimulation, repeated ten times per session, whereas the stimulation-off period was defined as the 8-s period preceding each stimulation start. Changes in motif usage between conditions were analyzed using two-tailed t-tests, and the motif usage ratio between stimulation-off and stimulation-on periods was computed. Box plots were used for visualization, indicating dataset quartiles with whiskers to show the distribution, excluding outliers as per the 1.5 interquartile range rule. Motifs that were not observed during specific sessions were omitted for those mice, resulting in plots that may not represent all subjects. For the ratio of engagement and disengagement, for each animal, the number of kinematic clusters observed in each behavioral state—engagement and disengagement—was used to calculate the ratio of cluster usage by dividing the count of each specific cluster during engagement by the count during disengagement. Intervals representing engagement or disengagement were determined based on the following condition: an interval where the IDF value exceeded 0.5 for more than 15 s. Each dot in the resulting plot represents the average cluster usage ratio in each animal’s dataset. For the ratio of cluster usage based on astrocyte peaks, each signal (DMS and DLS astrocyte Ca ++ ) was used to identify peaks with an amplitude threshold of 0.99. We used the 10 s before and after each peak to calculate the ratio of cluster usage, reflecting changes in cluster behavior around peak astrocytic activity. To train the deep-learning model, we used 2-s signal windows extracted around both task events (e.g., choice outcomes) and state transitions (e.g., engagement to disengagement) from DMS and DLS signals within a range of [−30 to 30]. Data for both analyses were shaped similarly, with a 3:1 split between training and validation sets. We trained the model using the Tsai package (version 0.3.9) for time series data (Oguiza, 2023, https://github.com/timeseriesAI/tsai ), applying the TSClassifier with the InceptionTimePlus architecture. Balanced accuracy results were obtained for predictions related to both task events and state transitions. References 1. ↵ Pascual , O. et al. Astrocytic purinergic signaling coordinates synaptic networks . Science 310 , 113 – 116 ( 2005 ). OpenUrl Abstract / FREE Full Text 2. Jo , S. et al. GABA from reactive astrocytes impairs memory in mouse models of Alzheimer’s disease . Nat. Med . 20 , 886 – 896 ( 2014 ). OpenUrl CrossRef PubMed 3. Lezmy , J. et al. Astrocyte Ca-evoked ATP release regulates myelinated axon excitability and conduction speed . Science 374 , eabh2858 ( 2021 ). OpenUrl CrossRef PubMed 4. Caldwell , A. L. M. et al. Aberrant astrocyte protein secretion contributes to altered neuronal development in multiple models of neurodevelopmental disorders . Nat. Neurosci . 25 , 1163 – 1178 ( 2022 ). OpenUrl CrossRef PubMed 5. Nedergaard , M. , Ransom , B. & Goldman , S. A . New roles for astrocytes: redefining the functional architecture of the brain . Trends Neurosci . 26 , 523 – 530 ( 2003 ). OpenUrl CrossRef PubMed Web of Science 6. Sobolczyk , M. & Boczek , T . Astrocytic calcium and cAMP in neurodegenerative diseases . Front. Cell. Neurosci . 16 , 889939 ( 2022 ). OpenUrl CrossRef PubMed 7. Pittolo , S. et al. Dopamine activates astrocytes in prefrontal cortex via α1-adrenergic receptors . Cell Rep . 40 , 111426 ( 2022 ). OpenUrl CrossRef PubMed 8. Turk , A. Z. , Marchoubeh , M. L. , Fritsch , I. , Maguire , G. A. & SheikhBahaei , S. Dopamine, vocalization, and astrocytes . Brain Lang . 219 , 104970 ( 2021 ). OpenUrl CrossRef PubMed 9. Fischer , T. , Scheffler , P. & Lohr , C . Dopamine-induced calcium signaling in olfactory bulb astrocytes . Sci. Rep . 10 , 631 ( 2020 ). OpenUrl CrossRef PubMed 10. Li , Y. et al. Activation of astrocytes in hippocampus decreases fear memory through adenosine A receptors . eLife 9 , ( 2020 ). 11. Lines , J. , Martin , E. D. , Kofuji , P. , Aguilar , J. & Araque , A . Astrocytes modulate sensory-evoked neuronal network activity . Nat. Commun . 11 , 3689 ( 2020 ). OpenUrl CrossRef PubMed 12. Montoya , A. et al. Dopamine receptor D3 signalling in astrocytes promotes neuroinflammation . J. Neuroinflammation 16 , 258 ( 2019 ). OpenUrl CrossRef PubMed 13. Jennings , A. et al. Dopamine elevates and lowers astroglial Ca through distinct pathways depending on local synaptic circuitry . Glia 65 , 447 – 459 ( 2017 ). OpenUrl CrossRef PubMed 14. Filosa , J. A. , Bonev , A. D. & Nelson , M. T . Calcium dynamics in cortical astrocytes and arterioles during neurovascular coupling . Circ. Res . 95 , e73 – 81 ( 2004 ). OpenUrl Abstract / FREE Full Text 15. Parpura , V. & Haydon , P. G . Physiological astrocytic calcium levels stimulate glutamate release to modulate adjacent neurons . Proc. Natl. Acad. Sci. U. S. A . 97 , 8629 – 8634 ( 2000 ). OpenUrl Abstract / FREE Full Text 16. Stogsdill , J. A. , Harwell , C. C. & Goldman , S. A . Astrocytes as master modulators of neural networks: Synaptic functions and disease-associated dysfunction of astrocytes . Ann. N. Y. Acad. Sci . ( 2023 ). 17. Perea G , Navarrete M , Araque A . Tripartite synapses: astrocytes process and control synaptic information . Trends Neurosci . ( 2009 ). 18. ↵ Goenaga J , Araque A , Kofuji P , Herrera Moro Chao D. Calcium signaling in astrocytes and gliotransmitter release . Front Synaptic Neurosci . ( 2023 ). 19. ↵ Lefton KB , Wu Y , Yen A , Okuda T , Zhang Y , Dai Y , Walsh S , Manno R , Dougherty JD , Samineni VK , Simpson PC , Papouin T . Norepinephrine signals through astrocytes to modulate synapses . bioRxiv [Preprint ]. ( 2024 ) 20. Wahis J , Akkaya C , Kirunda AM , Mak A , Zeise K , Verhaert J , Gasparyan H , Hovhannisyan S , Holt MG . The astrocyte α1A-adrenoreceptor is a key component of the neuromodulatory system in mouse visual cortex . Glia . ( 2024 ). 21. ↵ Reitman ME , Tse V , Mi X , Willoughby DD , Peinado A , Aivazidis A , Myagmar BE , Simpson PC , Bayraktar OA , Yu G , Poskanzer KE . Norepinephrine links astrocytic activity to regulation of cortical state . Nat Neurosci . ( 2023 ). 22. ↵ Oda S , Funato H . D1- and D2-type dopamine receptors are immunolocalized in pial and layer I astrocytes in the rat cerebral cortex . Front Neuroanat . ( 2023 ). 23. Brito V , Beyer C , Küppers E . BDNF-dependent stimulation of dopamine D5 receptor expression in developing striatal astrocytes involves PI3-kinase signaling . Glia . ( 2004 ) 24. Miyazaki I , Asanuma M , Diaz-Corrales FJ , Miyoshi K , Ogawa N . Direct evidence for expression of dopamine receptors in astrocytes from basal ganglia . Brain Res . ( 2004 ). 25. ↵ Corkrum M , Covelo A , Lines J , Bellocchio L , Pisansky M , Loke K , Quintana R , Rothwell PE , Lujan R , Marsicano G , Martin ED , Thomas MJ , Kofuji P , Araque A . Dopamine-evoked synaptic regulation in the nucleus accumbens requires astrocyte activity . Neuron . ( 2020 ). 26. ↵ Kang S , Hong SI , Lee J , Peyton L , Baker M , Choi S , Kim H , Chang SY , Choi DS . Activation of astrocytes in the dorsomedial striatum facilitates transition from habitual to goal-directed reward-seeking behavior . Biol Psychiatry . ( 2020 ). 27. ↵ Hong SI , Kang S , Baker M , Choi DS . Astrocyte-neuron interaction in the dorsal striatum-pallidal circuits and alcohol-seeking behaviors . Neuropharmacology . ( 2021 ). 28. ↵ Thrane AS , Rangroo Thrane V , Zeppenfeld D , Lou N , Xu Q , Nagelhus EA , Nedergaard M . General anesthesia selectively disrupts astrocyte calcium signaling in the awake mouse cortex . Proc Natl Acad Sci U S A . ( 2012 ). 29. ↵ Lazaridis I , Crittenden JR , Ahn G , Hirokane K , Wickersham IR , Yoshida T , Mahar A , Skara V , Loftus JH , Parvataneni K , Meletis K , Ting JT , Hueske E , Matsushima A , Graybiel AM . Striosomes control dopamine via dual pathways paralleling canonical basal ganglia circuits . Curr Biol . ( 2024 ). 30. ↵ Mathis A , Mamidanna P , Cury KM , Abe T , Murthy VN , Mathis MW , Bethge M . DeepLabCut: markerless pose estimation of user-defined body parts with deep learning . Nat Neurosci . ( 2018 ). View the discussion thread. Back to top Previous Next Posted December 02, 2024. Download PDF Email Thank you for your interest in spreading the word about bioRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. You are going to email the following Striatal Astrocytes Influence Dopamine Dynamics and Behavioral State Transitions Message Subject (Your Name) has forwarded a page to you from bioRxiv Message Body (Your Name) thought you would like to see this page from the bioRxiv website. 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