Our brain is continuously challenged by daily experiences. Thus, how to avoid systematic erasing of previously encoded memories? While it has been proposed that a dual-learning system with ‘slow’ learning in the cortex and ‘fast’ learning in the hippocampus could protect previous knowledge from interference, this has never been observed in the living organism. Here, we report that increasing plasticity via the viral-induced overexpression of RGS14414 in the prelimbic cortex leads to better one-trial memory, but that this comes at the price of increased interference in semantic-like memory. Indeed, electrophysiological recordings showed that this manipulation also resulted in shorter NonREM-sleep bouts, smaller delta-waves and decreased neuronal firing rates. In contrast, hippocampal-cortical interactions in form of theta coherence during wake and REM-sleep as well as oscillatory coupling during NonREM-sleep were enhanced. Thus, we provide the first experimental evidence for the long-standing and unproven fundamental idea that high thresholds for plasticity in the cortex protect preexisting memories and modulating these thresholds affects both memory encoding and consolidation mechanisms. Since patient H.M.  we know To test if naturally restricted plasticity in the neocortex protects from memory interference, we artificially increased plasticity in the prelimbic cortex via the overexpression of an established plasticity-enhancer called regulator of G protein signaling 14 of 414 amino acids (RGS14414) . The oveThe Object Space task is based on the natural tendency of rodents to explore more novel objects in more novel locations. Most object-exploration tasks use this tendency to test if animals remember the previous trial, in contrast the Object Space task consists of multiple trials – for rats five training trials – with conditions differing in the underlying statistical structure of possible object-locations in these trials. The key condition testing for the behavioral expression of semantic-like memory (Overlapping) has one location that always contains an object, while the second object can be in one of three other locations. Critically, the final training and test trial 24 hr later have the same spatial configuration, thus the animal will only show preferred exploration of the less often presented location, if they created a cumulative memory over multiple trials. The main control condition (Stable) has the same configuration for the five training trials and one new location at test. Thus, this simple-memory, control condition can be solved by either remembering the final trial or multiple trials.Here, we adapted the Object Space task to be able to contrast how later experiences interfere with the memory of the training trials. For this, the effect of the 24 hr trial, which now functions as interference trial violating previous trained location-rules, on a newly added test trial 48 hr later  was investigated. We show that increased cortical plasticity leads to better one-trial memory performance in a simple, one-trial object location task. However, we observed that such enhanced fast learning is associated with impaired semantic-like, cumulative memories tested in the Object Space Task. In alignment with these findings, pharmacological experiments confirmed that these results were an outcome of local changes in the prelimbic cortex. Next, we assessed the learning rate by devising a computational model and this model revealed that an increased learning rate in the intervention group augmented the influence of recent in contrast to remote memories on behavior. Finally, electrophysiological experiments showed that increased plasticity leads to (1) less NonREM sleep and smaller delta waves, (2) more neurons with slower firing rates, (3) increased hippocampal-cortical connectivity measured in theta-coherence, delta-spindle-ripple coupling and increased granger causality during ripple events and (4) off-target changes in hippocampal ripples.Prelimbic plasticity was increased by the overexpression of RGS14414  and initAt the test trial conducted after interference, RGS14-overexpressing animals exhibited higher discrimination indices in the Stable condition , p<0.05 To show that these effects were a result of the interference, we conducted three control experiments. Firstly, we performed a behavioral control, in which animals did not experience the interference trial. Secondly, we performed a pharmacological control, in which animals did experience the interference, but any subsequent plasticity-related changes were inhibited in the cortex via the infusion of anisomycin, a protein synthesis inhibitor. In these experiments RGS14-overexpressing animals showed discrimination indices comparable to controls emphasizing the determinant role of the interference trial in producing opposite outcomes. Thirdly, we added an additional control, in which animals did not receive pretraining on the Object Space Task conditions, but instead were only exposed to an object configuration for one-trial. When tested 48 hr later, increased cortical plasticity led to an enhanced one-shot memory performance , p<0.05.Together, the behavioral and pharmacological results show that increasing cortical plasticity with RGS14-overexpression caused larger interference effects in a semantic-like memory task. Increased interference in this case was due to better memory for one-trial experiences and local changes in the prelimbic cortex. Thus, our experimental results verify for the first time the hypothesis that lower cortical plasticity is critical to protect previous knowledge from interference effects.To characterize the build-up of a memory trace and its expression in the Object Space Task, we previously developed  a computThus, the modeling results show that increasing cortical plasticity with RGS14-overexpression caused larger interference effects in a semantic-like memory task due to a higher learning rate.Sleep is supposedly the price the brain pays for plasticity . The ideTo test this, after viral-injection rats were implanted with hyperdrives containing 16 tetrodes targeting the hippocampus and prelimbic cortex . We recoNonREM sleep is dominated by alternations of on and off-periods of neural activity that can be detected in the LFP signal as delta-waves. We detected delta-waves in the cortex and hippocampus, surprisingly in both groups the hippocampus presented with a higher rate of delta-oscillations  lets us determine more accurately the length of the respective on and off periods underlying the delta wave. RGS14-overexpressing animals presented with longer off periods than controls (p<0.01). However, known homeostatic changes, where off-period durations decrease over longer sleep periods, remained the same in both groups .In sum, in controls we could confirm the proposition that learning and therefore plastic changes lead to a homeostatic response with increases in delta wave activity. However, we show that artificially increasing plasticity in the prelimbic cortex does not lead to a simple enhancement of this effect. Instead, we observed in RGS14-overexpressing animals that cortical delta-waves become as small as hippocampal delta-waves, off-periods become longer and less NonREM sleep is obtained.Next, we focused on neuronal firing of individual neurons in the prelimbic cortex. We first determined the firing rate of each neuron during task performance and noticed that RGS14-overexpressing animals showed more neurons with low firing rates  differed between conditions and treatments. In controls Overlapping showed the highest activity but all OS conditions differed from home cage , this was not the case for RGS14.Next, we examined the fraction of ripples occurring alone or coupled to delta or spindles. In controls, more ripples occurred alone in both the Stable and Home Cage in contrast to Overlapping and Random (p<0.01). However, Overlapping and Random had more ripples following delta waves . In contrast, in RGS14 Stable and Home Cage had as many ripples following delta as the other conditions, significantly more than the same conditions in controls (p<0.001).In sum, in controls both Overlapping and Random, conditions with moving locations in the different training trials, had more ripple-modulated neurons and more ripples followed delta-waves than in Stable (simple memory) or Home Cage. In contrast, in RGS14 the same effects were already present in Stable and Home Cage, emphasizing that with increased cortical plasticity all experiences lead to signatures of sleep-related consolidation.By increasing plasticity in the prelimbic cortex via the overexpression of RGS14 and combining this with behavioral, pharmacological, computational, and electrophysiological approaches, we tested the long-standing but unproven idea of a slow- and fast-learner in the brain. Our data strongly suggests that restricted cortical plasticity is needed to protect memories from interference, the first experimental confirmation of Marr and McClellands ideas that have fundamentally shaped our concepts of memories. Increasing plasticity enhanced one-trial memory, increased memory interference in our semantic-like memory paradigm and learning-rate in our modeling approach. Signatures of cross-brain connectivity – hippocampal-cortical theta coherence, NonREM oscillation coupling, granger causality analysis of ripple events – were all increased after our plasticity manipulation. However, experience-dependent increases, that were present in controls, were lacking in RGS14 animals, because they were already seen in the home cage condition. Increased plasticity in the prelimbic cortex also changed hippocampal ripple oscillations, arguing for a top-down control of the cortex on this oscillation. In controls, we observed that complex learning (Overlapping and Random conditions) but not home cage or simple learning (Stable) led to increased neural response to ripples as well as more ripples that followed delta-waves. In RGS14, these effects were present in all conditions, emphasising that after increasing plasticity even insignificant experiences induce consolidation markers and are retained. Finally, increasing plasticity led to a decrease in firing-rates and smaller delta waves in the cortex, in both cases effects that made the cortical signal more hippocampal-like.The Object Space Task, applied here, allows to separate different types of learning. First off, the simple memory condition Stable, which has the different objects placed at the same two locations throughout training and at test one new location is used, is similar to classic reference memory such as the watermaze . Since tBy contrasting these different conditions, we can extrapolate which effects are driven by which type of learning or experience. Changes induced by all OS conditions in comparison to Home Cage, should represent more general experience-dependent effects enabling simple memory consolidation or homeostasis   and deltOur hypothesis was that increasing cortical plasticity leads to “overlearning”. Instead of distinguishing salient information and selectively consolidating, all experiences would be treated equal, consolidated and retained on the long-term in the cortex. Therefore, signatures of learning should already be present in control conditions such as Home Cage. We confirmed this hypothesis, both experience and complex-memory dependent learning effects observed in controls were already present in Home Cage in RGS14  to long-term memory (24 hr) . As in tThese previous experiments imply that the main effects of RGS14 on memory are the result of increased cellular plasticity and especially increased BDNF at encoding but also during consolidation. In the current results RGS14 overexpression had multiple effects in the brain, thus we cannot conclude if effects on behavior were directly due to the increase in cellular plasticity in the prelimbic cortex or indirectly induced by changing, for example, sleep-oscillations implicated in memory consolidation. This also is the case for the anisomycin treatment, which inhibits cellular consolidation via inhibition of protein synthesis. We did not record neural activity during the treatment; therefore, we cannot exclude that effects other than the inhibition of protein synthesis could negatively impacted consolidation after the interference trial and have contributed to the results. However, the main aim of the treatment was to impact the consolidation and thus retention of the interference memory, which was achieved.Of note, with the current approach, we cannot disentangle if we see increased interference effects in RGS14 since they remember the interference-trial better than the original training and thus their behavior is more driven by the interference experience. Or if the effect is caused by the interference memory overwriting and thus eliminating the original training memory. Furthermore, the Granger analysis of directionality should be interpreted with caution since Granger can lead to spurious results especially in oscillating networks .To conclude, we could show that increasing plasticity in the prelimbic cortex enhances the ability to retain one trial information but this negatively impacts abstracted, cumulative memory representations and the ability to distinguish learning experiences, confirming long-standing but unproven memory system theories. Furthermore, changes in cortical plasticity affect neuronal firing rates, hippocampal-cortical connectivity, cortical delta waves and hippocampal ripples.https://www.genzellab.com/). Next, all animals underwent viral-injection surgery (see below), half the animals received RGS14414-lentivirus while the other had a vehicle (empty) lentivirus. In addition, 32 animals also received pharmacological canula’s targeting prelimbic region during this surgery . These and the other 32 behavioral animals had a 2-week surgery recovery and then went on to do the habituation as well as training in the Object Space Task . The eight electrophysiology animals  received a second surgery 3 weeks after the first one, for hyperdrive implantation. During 2–3 week surgery recovery, tetrodes were slowly lowered to target area before the animals also had habituation and training in the Object Space Task.A total of 72 rats were used in this experiment: 64 in the behavioral experiment and 8 in the electrophysiological recordings. In each case animals were first extensively handled for multiple days (at least 3) until they did not flinch when the experimenter touched them , and 8 in the electrophysiological recordings . The behavioral experiments and electrophysiological recordings were performed during the light period (between 9:00-18:00).Three-month-old male Lister Hooded rats weighing between 300 and 350 g at the experiment start  were used in this study. Rats were pair-housed in conventional eurostandard type IV cages  in a temperature-controlled (20+2 °C) room following a 12 hr light/dark cycle with water and food provided ad libitum. After lentiviral surgical intervention, animals were single-housed for two days and paired with their cage mates after recovery in rat individually ventilated cages . Animals were maintained in their IVC in a barrier room for 14 days before downscaling them to conventional housing conditions. After hyperdrive implantation, rats were single-housed in until the end of the experiment. A total of 72 rats were used in this experiment: 64 in the behavioral experiment  and conducted according to the Experiments on Animals Act .414- and vehicle-lentivirus solutions (1.72x107 CFU/ml and 2.75x106 CFU/ml respectively) were prepared and provided by Dr. Zafaruddin Khan at the University of Malaga   was cloned into the commercial vector p-LVX DsRed Monomer-C1  using DNA recombinant technology. Then, both non-replicant RGS14414- and vehicle-lentivirus (empty vector) were prepared and titered using the Lenti-XTM  according to the manufacturer’s instructions.The RGS14, Spain) ; . BriefThe animal’s procedures related to the non-replicant lentiviral solution were approved and carried out in compliance with institutional regulation.A customized lightweight tetrode micro-drive was manufactured to implant 10 and 6 movable tetrodes in the prelimbic cortex and hippocampus (HPC), respectively . Two sep2 and maintained at 1.5–2% isoflurane +1 l/min O2. A 0.8 mm diameter craniotomy was drilled above the target area in each hemisphere. The DV dura mater coordinate was measured before performing the durotomy.Lentiviral solutions were infused in the prelimbic cortex using stereotaxic surgery under biosafety level 2 conditions. The coordinates of the prelimbic cortex injection site were +3.2 mm AP,+/-0.8 mm ML from Bregma, and –2.5 mm DV from dura mater, according to The Rat Brain Atlas from Paxinos and Watson A 30 G dental carpule connected to a 10 μl Hamilton and an infusion pump  was slowly inserted into the brain target area (0.2 mm/min). A total volume of 2 μl of the lentiviral solution was infused at 200 nl/min. After 5 min of diffusion, the needle was removed, and the incision was sutured.For the anisomycin infusions, 32 animals were implanted with a 26 G bilateral guide cannula  above prelimbic cortex  was applied to protect the corneas during the intervention. At the start and end of the surgery, 2 ml of 0.9% NaCl physiological serum was administered subcutaneously. As analgesia, animals were administered 0.07 mg/ml carprofen in their water bottles two days before and three days after surgery. Immediately before surgery, 5 mg/kg carprofen was sc injected. In addition, a mix of 4 mg/kg lidocaine and 1 mg/kg bupivacaine in a 0.9% NaCl physiological serum was administered sc locally in the head.After the viral injection, animals were housed individually in rat IVC cages for 14 days. Their weights and status were monitored daily for the correct recovery of animals. Then, rats were pair housed with their previous cagemate and moved to conventional housing.Twenty-one days after viral infusion, a second stereotaxic surgery took place for tetrode micro-drive implantation in 8 animals. The procedure was similar as described above. In this intervention additionally, a prophylactic 10 mg/kg sc injection of Baytril antibiotic was administered at the beginning of the surgery. Two craniotomies  were drilled above the target areas on the right hemisphere. The coordinates for the upper left corner of each craniotomy were: AP +4.5 mm and ML –0.5 (prelimbic cortex) and AP –3.8 mm and ML –2 mm (HPC) from Bregma . A grounAnisomycin (ANI) powder  was solved in 1 M HCl in 0.9% NaCl physiological serum, and the pH was adjusted with 10 μl of 5 M NaOH. Aliquots were prepared and stored at –20 °C until the moment of use. Immediately after the 24 hr test in the object space task, animals were infused with 3 µl/hemisphere of a 25.6 µg/µl ANI solution or the solvent as control at 300 nl/min. An infusion pump and two 10 μl Hamilton syringes connected through a PE10 tube  to a customized 30 G bilateral internal cannula with a 2 mm projection  was used for the infusion. The internal cannula was carefully removed after 3 min of diffusion time, and the dummy and cap were placed back. All the animals from Experiment 2 received the infusion of both ANI and vehicle across different weeks for each experimental paradigm.414 protein is observed (The Object Space Task (OST) is a newly developed behavioral paradigm to study simple and semantic-like memories in rodents . The tasobserved ; . BriefThe OST consists of two phases: a training phase of five training trials in which animals are exposed to two identical objects  for 5 min ; and a interference/test phase consisting of a single 10 min' trial performed 24 hr and 72 hr after training. In the stable condition, both object locations were fixed during the training trials, and one object location was moved during the interference and tests sessions. In the overlapping condition, one object location was fixed, and the other one moved across training trials. In the interference and test sessions, the same object-location pattern from the last training trial was repeated.The open field was a wooden square 75x75 x 60 cm. For the task, but not for the habituation, we placed 2D proximal cues on the open field walls and 3D distal cues above the open field. The cues were changed in each experimental session. In addition, the open field base colors changed across task sessions . The objects used vary in material , size, and colors. For electrophysiological recordings, not plastic objects were used to prevent static interference. All object bases were attached to 10 cm x 10 cm metal plates. Circular magnets were installed in the corners underneath the open field floor to prevent object movements during exploration. The open field and object surfaces were cleaned with 40% ethanol between trials to avoid odor biases.online using the homemade software 'Scorer32'. The experimenter was blinded for treatment and experimental conditions at the moment of scoring. Object locations and experimental conditions were counterbalanced across treatments, individuals, and sessions.Each trial was recorded using a camera above the open field. The object exploration time was manually scored 414-treated animal, which performed the identical condition sequences with the same object-location patterns. Object locations and experimental conditions were counterbalanced across cohorts. Electrophysiological recordings took place during trials and rest periods . Therefore, two brown wooden sleep boxes (40x75 x 60 cm height) with bedding material were placed next to the open field. Animals had been accustomed to the sleep box for at least 3 hr in each open-field habituation session.For electrophysiological recordings, we run four cohorts of 2 animals each. Each cohort included one vehicle- and one RGS14Rats involved in the electrophysiological recordings also performed two experimental control conditions: homecage and random. The random condition was carried out as described previously , so therIn vivo freely moving extracellular recordings were executed during the OST and the resting periods. One session per experimental condition  was carried out per animal. The local field potential (LFP) and single-unit activity detected by the 64 channels were amplified, filtered, and digitized through two 32 channels chip amplifier headstages (InstanTechnology) connected through the Intan cables and a commutator into the Open Ephys acquisition box. The signal was visualized using the open-source Open Ephys GUI (sample rate 30 kHz). In addition, the headstage contains an accelerometer to record the movement of the animals.After all the recording sessions, the tetrode-implanted animals received brain electrolytic lesions 48 hr before the transcardial perfusion to identify the electrode tips placement. Thus, a current of 8 µA for 10 s was applied in two wires per tetrode using the stimulator with the animal under isoflurane inhaled anesthesia.3 in PBS. Once brains sank (after 2–3 days approx), they were frozen in dry ice and stored at –80 °C. Finally, 30 or 50 µm coronal sections of target areas were obtained using the cryostat , collected in 48-well plates containing 0.02% (w/v) NaN3 PBS and stored at 4 °C.After data collection, animals had overdoses with 150 mg/kg sodium pentobarbital ip. Rats were transcardially perfused first with 80 ml of 0.1 M phosphate-buffered saline pH 7.4 (PBS) and then with 250 ml of 4% (w/v) paraformaldehyde in 0.1 M phosphate-buffered pH 7.4 (PFA). After brain extraction, it was immersed in PFA overnight at 4 °C. Then, the brains were rinsed in PBS 3 times for 10 min and cryoprotected by deepening in 20 ml of 30% (w/v) sucrose, 0.02% (w/v) NaN414 was checked by free-floating fluorescence immunohistochemistry. First, the target sections were selected, rinsed in PBS, and incubated overnight at 4 °C with the rabbit polyclonal anti-RGS14 antibody . Then, the Alexa fluor 488-conjugated goat anti-rabbit IgG  at room temperature for 2.5 hr. Some drops of water-soluble mounting medium containing DAPI  were applied for 5 min before placing the coverslip. Leica fluorescense microscope (Leica DM IRE2) and camera were used to observe and photograph the samples.The overexpression of rgs14Coronal sections were AP sequentially mounted on gelatin-coated slides and incubated at 37 °C overnight. Slices were hydrated first in 0.1 M PBS pH 7.4 and then in Milli Q water for 20 min each. Next, brain sections were stained in 0.7 %(w/v) acetate cresyl violet for 20 min and dehydrated in an increasing ethanol gradient . Lastly, the tissue was immersed in xylene for 15 min, and the coverslip was placed using some DePeX mounting medium drops. Cannula placement, infusion traces, or/and tetrode lesions were observed and photographed under a light field microscope (Leica DM IRE2) and a camera.The total exploration time was calculated as the sum of the time spent exploring both object locations. The discrimination index (DI) was computed by subtracting the familiar object location exploration time to the novel object location and dividing it by the total exploration time. For Overlapping it is moved location – stable location, for Stable it is location-to-be-moved-at-test – stable location and for random which is assigned as moved and stable is random. A DI >0 means a preference for the new object location and consequently memory from the previous episode. A DI = 0 shows no preference for either the new object location or the fixed one. DI <0 means a preference for the stable object location.https://github.com/MehdiKhamassi/ObjectSpaceExplorationModel  means that the model will need numerous repetitions of the same observation  to properly memorize it. In contrast, a high learning rate α (i.e. close to 1) means that the model quickly memorizes new observations at the expense of old observations which are more quickly forgotten. As a consequence, with a low learning rate the exploratory behavior generated by the model will mostly reflect remote memories but not recent ones (semantic-like memory). Conversely, with a high learning rate, exploratory behavior in the model will mostly reflect recent memories but not remote ones (episodic-like memory).β>0) will result in neophilic behavior: the model spends more time exploring objects associated with high uncertainty (i.e. novelty or constantly changing location); a high negative inverse temperature (β<0) will result in neophobic behavior: the model spends more time exploring objects with low uncertainty (stable/familiar objects).Finally, an inverse temperature β close to zero means that the model does not strongly translate memories into object preferences for exploration, thus showing little object preference. In contrast, a high inverse temperature will mean that the model’s exploratory behavior is strongly driven by differences in relative uncertainty between place-object associations. A high positive inverse temperature  was selected per animal. Using a 10 s sliding window, an experienced researcher created the hypnogram and 1 s epoch vector indicating brain states. The absences of movement in the accelerometer discriminate between wakefulness and sleep. During sleep periods, a dominant theta frequency in the dorsal hippocampus in the absence of spindles and delta waves indicated REM sleep. NonREM sleep was classified when slow oscillations were detected in the prelimbic cortex. The intermediate phase was defined as short transitional periods between NonREM and REM that show an increase in frequency in the prelimbic cortex and frequency similar to theta in the dorsal hippocampus. Microarousals were defined later on as periods of wakefulness <15 s within a sleep period.Different states  were The total sleep time (TST), total wakefulness time, and total time for different sleep states  were computed per session on MATLAB. The average across sessions was calculated per rat, and the mean and SEM were computed per treatment. Additionally, the % of TST of NonREM time, REM time, and intermediate time were calculated for the 3 hr recording post-training.https://github.com/genzellab/sleep_architecture  for the Object Space  and home cage conditions.http://fmatoolbox.sourceforge.net were modified and used to detect the start, peak and end of spindles and delta waves respectively. The optimal threshold was found for each animal by visually inspecting the detections and modifying the default parameters of the functions when needed. The results were saved as timestamps with respect to the concatenated NonREM signal in seconds. They were then used to find the timestamps with respect to the recorded signal. This process was repeated for pre and post trial sleep periods in study days pertaining to all animals in both treatment groups. The same method was applied for the detection of hippocampal delta waves.The downsampled prelimbic cortex channel (1 kHz) was loaded into the Matlab workspace and using a third-order Butterworth filter the signal was filtered to 9–20 Hz for detecting spindles and to 1–6 Hz for detecting delta waves. The NonREM bouts were then extracted from the filtered signal and concatenated. The functions FindSpindles and FindDeltaWaves from the Freely Moving Animal (FMA) toolbox The downsampled channels (1 kHz) of the hippocampal pyramidal layer were loaded into the Matlab workspace and the NonREM bouts were extracted. Using a third-order Butterworth bandpass filter, the epochs of HPC signal were filtered to a frequency range of 100–300 Hz. A custom MATLAB function was used for detecting the start, peak and end of the ripples by thresholding voltage peaks which lasted a minimum duration of 30ms above the threshold. The start and end of the ripple were determined as half the value of the selected threshold. Values equivalent to 5 times the standard deviation of concatenated NonREM bouts were computed individually for presleep and all post trials in a study day. The average of these values was calculated to find a single detection threshold per study day. An offset of 5 microV was added to the threshold to reduce false positives. This was repeated for all study days pertaining to all animals in both treatment groups.The traces of each event detected  were extracted using the start and end timestamps obtained from the detectors. The traces of the events were filtered in their corresponding detection frequency band. Characteristics such as the amplitude and mean frequency were calculated for these filtered events using built-in and custom MATLAB functions. Namely, the amplitude of the events was calculated by computing the envelope of the filtered trace using a Hilbert transform. The absolute value of the result was taken and its maximum was found. The mean frequency of the filtered traces was computed using the meanfreq function of MATLAB.The sequences between ripples, spindles and delta waves were counted in various combinations to study cortico-hippocampal coupling during NonREM sleep as done by The co-occurrence between ripples and spindles was computed by comparing the start and end timestamps of both events. To consider co-occurrence between a ripple and a spindle, either one of the following conditions had to be fulfilled. (1) A ripple had to start and end within the duration of the spindle. (2) One of the events had to start or end within the duration of the other. Given that more than one ripple can co-occur with the same spindle, we counted separately spindles co-occurring with spindles and spindles co-occurring with ripples.The downsampled prelimbic cortex signal was filtered in the 0.5–4 Hz range using a third-order Butterworth bandpass filter and its Hilbert transform was computed to find the phase angle of slow oscillations in a range from 0° to 360°. The peaks of ripples and spindles were then used to find the corresponding slow oscillation phase. This same signal was later used to find the phase during spikes timestamps of cortical neurons.A two-second-long window centered on each ripple peak was extracted from the hippocampus and the prelimbic cortex channels respectively. All ripples across animals and conditions were combined per treatment and their amplitude was computed by finding the maximum of their envelope computed with a Hilbert transform. The median ripple amplitude was calculated and the corresponding two-second-long windows of the 2000 ripples which amplitude was the closest to the median amplitude were included in the following analysis. A notch filter at 50 Hz was applied to the ripple-centered windows using the ft_preprocessing function from the Matlab-based Fieldtrip toolbox https://github.com/genzellab/rgs14, (swh:1:rev:c1b7c33cc10ac404a0d816a8e1ddeb3220edab58).To determine the predictive power between brain regions during ripples, the time-frequency Spectral Granger Causality was computed for each directionality . A windoAn important step of our analysis was identifying the cortical neurons and their spiking activity. The sleep and trial recordings of each tetrode of the prelimbic cortex were extracted and concatenated chronologically to allow tracking the individual neuronal activity across the whole study day. The tetrode recordings were preprocessed by applying a bandpass filter between 300 and 3000 Hz. For each tetrode, multiple spike sorting algorithms were run using the SpikeInterface python-based framework Sampled at 30 kHz, the prelimbic cortex tetrode channels were loaded and then filtered using a third-order Butterworth bandpass filter with a frequency range of 300–600 Hz. Neurons labeled as ‘good’ were used for further analysis. A preliminary waveform per neuron was defined with an 82-sample window with 40 samples before and 41 samples after the spike timestamp (ST). For each neuron, STs were randomly permuted and a total of 2000 were selected. The permutation was done in order to avoid bias when selecting waveforms. These 2000 waveforms were averaged to obtain the mean spike waveform per neuron. Since tetrodes were used to record neuronal activity during the task and sleep, there were 4 average waveforms to choose from. The one with the highest peak amplitude was chosen. For each neuron, all STs were stored. Further, each neuron was assigned a unique ID. This process was performed for all neurons of all rats.The average neuron waveforms calculated from rats 1, 2, 6, and 9 were categorized as ‘Vehicle’ and stored in a 139 (t1) ×82 matrix, with 139 being the total number of putative neurons and 82 being the number of samples as mentioned before. Similarly, ‘RGS14’ data from rats 3, 4, 7, 8 was stored as a 353 (t2) ×82 matrix. Variables t1 and t2 were the total counts of neurons for the respective treatments. The bz_CellClassification.m script from Buzsaki lab’s GitHub was used to compute the trough-to-peak delay time and the spike width for the spike waveform of each putative neuron. The function ClusterPointsBoundaryOutBW.m of the same repository was modified to incorporate visualization of interneuron and pyramidal data for both treatments. Neurons were then visualized in a 2D plane with x and y axes as trough-to-peak delay and spike width respectively. The set of coordinates of all putative neurons were fed into a Gaussian Mixture Model (GMM) with two components in order to find the centroids of the clusters of pyramidal cells and interneurons. The cluster which contained spikes with high values of spike width and trough-to-peak delay was labeled as the pyramidal neurons cluster, while the remaining one was labeled as the interneurons cluster. For both clusters, a threshold of mean +/- 2 SD with respect to their centroids was used to filter out the outliers. Next, the firing rates of the remaining neurons were calculated as is described in the following paragraph and those with extreme firing rate values were reinspected in the Phy interface by another experimenter to potentially discard remaining false positives. After this procedure there were a total of 101 pyramidal neurons for the RGS14 treatment and 57 for Vehicle. The total number of interneurons were 18 for RGS14 and 7 for Vehicle. In the following analyses, only pyramidal neurons were used given the low number of interneurons detected.The spikes of each neuron were grouped by the sleep stage during which they fired. The total number of spikes of a neuron during a specific sleep stage were determined by counting all the spikes occurring during the sleep stage in question across pre-trial sleep, post-trial sleep and trials periods of a single day. The cumulative amount of time spent in a specific sleep stage during the day was determined similarly. Using these two values, the firing rate of a neuron during a sleep stage was computed by dividing the total number of spikes during the sleep stage by the cumulative amount of time spent in seconds in the sleep stage. The firing rate during each sleep and wake stage was calculated for all neurons. The firing rates of neurons during the ‘Wake’ stage in the vehicle control condition were divided into five quantiles based on their magnitudes , as shown in The spiking activity of neurons was extracted for each NonREM bout of every pre and post-trial during a study day. The spike timestamps of the multiple neurons firing during a NonREM bout were combined to generate a single spike train of multiunit activity (MUA). A binary array with the length of the NonREM bout and a sampling rate of 30 kHz was created, where 1 meant a spike occurred during that timestamp and 0 meant absence of spike. The start and end of OFF periods were detected by calculating the inter-spike-interval (ISI) between spikes and finding values above 50ms. The remaining periods were considered for ON period detection. Only ON periods with a duration between 50 and 4000ms were kept. A minimum number of 10 spikes per ON period was required to include it in the analysis. Durations of all ON and OFF periods found during a pre or post-trial were computed and their mean value was stored. This was repeated for all study days in which pyramidal neurons were detected. Only ON and OFF periods of study days including a minimum of 7 neurons were included.Using the spike timestamps during NonREM sleep, the cortical pyramidal neuron response to hippocampal ripples was computed in a 2 s window defined around the peak of each ripple. For each ripple the activity of all pyramidal neurons detected during that day was extracted. The spikes timestamps in the 2 s window were normalized to vary from –1 to 1 s, where 0 was the ripple peak. After concatenating the normalized timestamps across all ripple-centered windows per neuron, they were binned in 10ms bins and the number of spikes was determined for each bin. Hence, for each neuron, a [1x200] column vector was obtained. This vector was then z-normalized. The final z-scored vector was found by averaging the z-scored vector over all neurons. The data was smoothed twice using the MATLAB smooth function. To visualize the activity of prelimbic cortex pyramidal neurons around the ripple, the firing activity of a neuron was determined from a 50ms window around the peak of the ripple (–20 to +30ms) by quantifying the number of spikes. After compiling the firing activity around the ripple peak for each neuron the values were sorted in an ascending order and was visualized using the MATLAB imagesc function.Figures were generated in GraphPad Prism (truncated violin plots with high smoothing). This important study reveals that slow plasticity in the neocortex is essential to prevent memory interference. The method of artificially increasing plasticity in the prefrontal cortex of rats during learning and its effect on sleep physiology, when memories are believed to be reprocessed, is solid. The work will be of interest to neuroscientists interested in learning and memory. public reviews designed to be posted alongside the preprint for the benefit of readers; (ii) feedback on the manuscript for the authors, including requests for revisions, shown below. We also include an acceptance summary that explains what the editors found interesting or important about the work.Our editorial process produces two outputs: (i) Decision letter after peer review:eLife. Your article has been reviewed by 3 peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Laura Colgin as the Senior Editor. The following individuals involved in the review of your submission have agreed to reveal their identity: Usman Farooq (Reviewer #2); Marcos Frank (Reviewer #3).Thank you for submitting your article "Learning Fast and Slow: Increased cortical plasticity leads to memory interference and enhanced hippocampal-cortical interactions" for consideration by The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.Essential revisions:In their study, Navarro Lobato et al. explored the relationship between plasticity and memory in the hippocampo-cortical network. To this end, virally-expressed RGS was injected into the medial prefrontal cortex of rats, and animals were trained on behavioral assays to test different types of memories, namely semantic and episodic. The study reports that enhanced plasticity in the prefrontal cortex led to the interference of recent memories, providing evidence that too much plasticity – and consequently fast learning – was detrimental to the acquisition of semantic-like memories in the cortex. This increased plasticity was also associated with changes in sleep physiological patterns and hippocampal-cortical coupling.While the reviewers agree that the study is of potential interest, they raised several major concerns, which are summarized below. They have agreed that the below concerns must be addressed to warrant the publication of the study.1. It is important to clarify the behavioral results by providing a more detailed description of the task parameters . Furthermore, the lack of discrimination in control animals is surprising and seems at odds with previously published results. Last, whether increased plasticity leads to increased interference and/or enhanced one-shot is unclear.2. The study should provide more details on RGS over-expression, the exact site of injection, possible resulting anatomical changes , and justification that RGS does not lead to a ceiling effect in some task parameters (and not directly improve plasticity).3. Electrophysiological results do not compare overlapping and stable conditions (while it seems these data were collected), these results should be presented or, at least, this choice of the presentation should be discussed.4. The study should provide evidence that the reported decrease in firing rates does not depend on differences in behavior.5. The relationship between plasticity and firing rates should be toned down, especially since the discussion is based on hippocampal data, for which the physiology may be different as in the cortex.6. The study should provide a better characterization of changes during sleep, especially UP/DOWN state duration, REM sleep duration, and firing rates, as well as a comparison of PFC and hippocampus.7. If possible, the study should include some physiological characterization of anisomycin-treated animals.8. Potential alternative interpretations should be discussed, for example, the consequence of RGS over-expression on sleep architecture and physiology, which would itself change learning (not just a change in plasticity).Reviewer #1 (Recommendations for the authors):1) This is a difficult paper to read, as the authors present a mixture of results and discussion while describing figures in the text, with relatively little space to present the results. The presentation can be significantly improved to enhance readability by devoting the Results section to primarily describe results, and moving the interpretation/ speculation to the Discussion section as much as possible. In addition, it will be valuable to describe in detail the Object-Space task with a test trial after 24 hours as per the original experimental design , before moving on to the interference condition. Finally, dividing multiple-graph panels into labelled sub-panels that are referenced in the Legend  will be useful.2) For the behavioral results in Figure 1C, the Overlapping condition is supposed to show slowly increasing DI corresponding to accumulated memory as per the text/ legend. Statistics need to be presented here, and it instead looks like there is an increase in DI for the Overlapping condition only on the 5th training trial. Further, the interference trial after 24 hours and the test trial after 48 hours should correspond to the test trial in the original design, where an increase in DI is expected, but none seems to be observed here. As noted in the Public review, the authors need to discuss this in detail.3) Relatedly, please explain the colors and shades used in Figure 1D in detail, or change the color scheme. The dark gray alluded to here (Experiment 2 Control) looks like violet.4) Line 196-198: "Furthermore, because it is the faster-firing neurons that dominate upstate spiking activity and therefore δ amplitude, the slowing of firing rates in the more plastic neurons is most likely the cause of the smaller δ waves seen in these animals". Can a reference be provided for the first part of the statement?Also, reference 14 relates to hippocampal slow-firing neurons (as noted above), so any conclusions regarding slow-firing cortical neurons being more plastic need to be justified.5) For physiological differences, is there any reason why data is not presented comparing the Stable and Overlapping OST condition since this is the central point of the manuscript? As per Methods on pg. 500-506, recordings were obtained separately for each condition.6) The Granger analysis of directionality should be interpreted with caution since Granger can lead to spurious results , especially in oscillating networks.7) In Figures S6 and S7, none of the axes labels and axes tick labels are visible – they are too small.8) In Figure 4, there is no scale bar for the color plots. In Figure 4C, please explain the acronyms B, Pre, Post.Reviewer #2 (Recommendations for the authors):– Check the whole manuscript for grammar e.g., these theories 'provides'… they 'remains' – Describe the object space task in more detail  in the main manuscript as it is crucial to the manuscript– Which statistical tests were used to test for the hypothesis? Please provide details alongside each test – How is this a semantic-like task? Please describe/explain in the manuscript.– As a host of electrophysiological changes have been observed, a cartoon model might help show how they all fit together to support the hypothesis of the authors– Are hippocampal firing rates different or only prelimbic?– Why were the electrophysiological differences of the object space task not compared with the random condition but the home cage condition instead? To determine the neural signature of increased plasticity due to the task in the RGS14414 group wouldn't that comparison be best?Reviewer #3 (Recommendations for the authors):This is a very interesting and thoroughly performed study that addresses very important questions in the field. The authors use a viral-vector-based strategy to over-express a construct that increases dendritic arborization, and spine formation (in the cortex) and is known to boost certain forms of memory. This is interpreted as a means of increasing cortical plasticity. Then, through a combination of behavioral tasks, electrophysiology, and sleep analyses, they report numerous changes in cortical and hippocampal activity. There are a few areas of improvement that would greatly strengthen the study.1. Can the authors address the mechanism governing the effect of RGS? It seems that (based on other studies) transfected brain areas show a general increase in dendritic arborization, spine increase, etc., but it's not clear how this translates to 'experience-dependent' plasticity per se. It seems like non-specific effects, akin to what happens when neurotrophins are injected into V1. The result of the latter is not always normal, or like what happens with naturally occurring plasticity. This may be occurring in this experiment as well, as RGS appears to generally increase hippocampal-cortical connectivity in a manner different than what is driven by the experience. Also, considering that a Lenti virus was used, which can lead to random genome integration, some discussion would be helpful.2. Is there any histology showing the localization of the RGS transfection?3. It seems the best control for the effects of RGS is to use a compound that targets the same pathways. For example, Navarro-Lobato recently used the knockdown of 14-3-3ζ gene for this purpose (JNS 2021). I ask because anisomycin probably involves other pathways .4. What happened to firing rates during REM sleep? Cortical neurons change their activity in REM sleep, possibly more so after 'plasticity'. These analyses would be very helpful.5. Can the authors present data on the duration of NREM up and down states? I ask because this parameter has been measured previously (Vyazovskiy) in the context of sleep need and plasticity.6. Is it possible that the lack of experience-dependent changes in the RGS animals (in some parameters) is due to a ceiling or masking effect? That RGS saturates the capacity for further change?7. It would be helpful to indicate in the figure legends for the supplementary figures when the data are from RGS or controls.8. What happened to EEGs, FRP's, etc., in the anisomycin-treated rats? This would help one determine if the cortex was abnormal in some way. Essential revisions:In their study, Navarro Lobato et al. explored the relationship between plasticity and memory in the hippocampo-cortical network. To this end, virally-expressed RGS was injected into the medial prefrontal cortex of rats, and animals were trained on behavioral assays to test different types of memories, namely semantic and episodic. The study reports that enhanced plasticity in the prefrontal cortex led to the interference of recent memories, providing evidence that too much plasticity – and consequently fast learning – was detrimental to the acquisition of semantic-like memories in the cortex. This increased plasticity was also associated with changes in sleep physiological patterns and hippocampal-cortical coupling.While the reviewers agree that the study is of potential interest, they raised several major concerns, which are summarized below. They have agreed that the below concerns must be addressed to warrant the publication of the study.1. It is important to clarify the behavioral results by providing a more detailed description of the task parameters . Furthermore, the lack of discrimination in control animals is surprising and seems at odds with previously published results. Last, whether increased plasticity leads to increased interference and/or enhanced one-shot is unclear.We apologize that not all things were clear, we seem to have oversimplified some explanations. In the previous publication with the same task, a 24h test trial was used (not 72h after training as here). Actually, the interference trial in this project is behaviourally the same as the test trial in the previous publication. The reason we call it interference and not test, is simply because our main interest in the current project was how this trial affects the 72h test trial. However, we can analyze the behaviour in that trial to “replicate” our previous findings. This data was already included in the supplement  but we now expanded the figure to include a bar graph version that is similar to the Plos Bio figure of the original report. Here you can see that in vehicles we replicate previous findings and they are above chance, further there is not difference between the groups (that could have influenced our results at 72h). We also now include examples of objects used in this figure see  and expa2. The study should provide more details on RGS over-expression, the exact site of injection, possible resulting anatomical changes , and justification that RGS does not lead to a ceiling effect in some task parameters (and not directly improve plasticity).The injection site was already included in the methods  and now we added a histology overview. We do not have data on anatomical changes but now include a paragraph in the discussion citing previous data and findings. We expanded the discussion on how RGS on one hand shows “overlearning” but on the other hand more complex interactions of some learning mechanisms. Further, we include discussion on other parameters such as overall exploration time and how RGS14 does not change these.3. Electrophysiological results do not compare overlapping and stable conditions (while it seems these data were collected), these results should be presented or, at least, this choice of the presentation should be discussed.Our starting hypothesis was that RGS14 animals show “overlearning”, so every experience is treated as a salient learning experience. Therefore, we expected the biggest difference between treatments for the home cage condition (HC) and thus focusses on the HC vs OS analysis. Since as predicted this analysis provided the main effects and a large volume of results, we did not delve in more depth in the conditions.However, in the meantime in a different project with the same task  we have explored the condition effect in more detail and found a specific effect of the percentage of ripples that occur after a δ wave in the Overlapping training condition. Since the reviewers pointed out this shortcoming, we now applied the same analysis to the RGS data-set and are happy to report that we find the same effect (included in the revision). We further now split the ripple-response analysis for condition and can also report differences there. Therefore, we would like to thank the reviewers for encouraging us to continue with the analysis of the data in more detail, considering new findings in our other projects. We have added a new figure and expand on this in the discussion.4. The study should provide evidence that the reported decrease in firing rates does not depend on differences in behavior.Firing rate were shown for different wake and sleep states  in the supplement. We now added firing rate group analysis for wake in the sleep box (no specific behaviour), NREM and REM . All analysis confirms the initial finding from task wake, therefore differences in firing-rates are not due to differences in behaviour.5. The relationship between plasticity and firing rates should be toned down, especially since the discussion is based on hippocampal data, for which the physiology may be different as in the cortex.We adapted the discussion on firing rates and now include also more referencing to the original review proposing the idea that goes beyond hippocampus. We also now point out that the previously cited paper is focussed on the hippocampus. Overall, we expanded the discussion including also the new results in regards to hippocampal δ-waves and reported hippocampal-firing-rates (see answer to next comment).6. The study should provide a better characterization of changes during sleep, especially UP/DOWN state duration, REM sleep duration, and firing rates, as well as a comparison of PFC and hippocampus.We added REM bout duration  and are happy to report that there is a significant difference. We had originally not followed up on bout duration since there was no effect in overall duration. Regrettably we cannot add a PFC and HPC comparison for the firing rates, since we did not have sufficient tetrodes correctly placed in the hippocampus to record enough neurons to make this analysis reliable.However, we now expand the discussion on the hippocampal and cortical firing rates and now mention that the magnitude of change we see in RGS14 would mirror the firing rate changes we would expect for the hippocampus according to the literature.A HPC-PFC comparison we could add, was the analysis of δ-waves. Interestingly, in controls hippocampal δ waves are smaller than cortical ones. Further, the decrease in amplitude we already reported for RGS14, cortical δ-waves, corresponds to the normal size of hippocampal δ-waves . Thus, in RGS14 cortical and hippocampal firing-rates and δ-amplitudes are the same, which is not the case for controls. This is further evidence that with our manipulation we turned our “slow learning” cortex into a plastic, hippocampal-like structure.Finally, we also added the on/off period analysis. Interestingly, in RGS14 off periods are longer, fitting to the SHY hypothesis. Further, we can replicate the observed time course of on/off durations reported previously .7. If possible, the study should include some physiological characterization of anisomycin-treated animals.Regrettably, it was not possible to include electrophysiology in the anysomycin treated animals. We currently do not have implants that combine tetrodes with canula. We now include in the discussion a comment in that regard.8. Potential alternative interpretations should be discussed, for example, the consequence of RGS over-expression on sleep architecture and physiology, which would itself change learning (not just a change in plasticity).We now included this in the discussion, mentioning that we cannot disentangle if our results are due to direct changes in cellular plasticity or via changes in e.g. oscillatory coupling or sleep architecture.Reviewer #1 (Recommendations for the authors):1) This is a difficult paper to read, as the authors present a mixture of results and discussion while describing figures in the text, with relatively little space to present the results. The presentation can be significantly improved to enhance readability by devoting the Results section to primarily describe results, and moving the interpretation/ speculation to the Discussion section as much as possible. In addition, it will be valuable to describe in detail the Object-Space task with a test trial after 24 hours as per the original experimental design , before moving on to the interference condition. Finally, dividing multiple-graph panels into labelled sub-panels that are referenced in the Legend  will be useful.We now expanded the OS task description (introduction/discussion), include labelled sub-panels and further moved interpretation/speculation out of results to improve readability. We still kept those sections that explain why we decided to do a certain analysis to help the reader understand our approach. Finally, we significantly expanded the discussion, hopefully improving clarity and including more structure.2) For the behavioral results in Figure 1C, the Overlapping condition is supposed to show slowly increasing DI corresponding to accumulated memory as per the text/ legend. Statistics need to be presented here, and it instead looks like there is an increase in DI for the Overlapping condition only on the 5th training trial. Further, the interference trial after 24 hours and the test trial after 48 hours should correspond to the test trial in the original design, where an increase in DI is expected, but none seems to be observed here. As noted in the Public review, the authors need to discuss this in detail.As mentioned above, we now clarified that the 24h interference is similar to 24h test of past experiments and that we replicate effects. The same is for training DI that over many projects tends to show the same curve (most publications to come soon), where in trial 2-4 there is only a slight increase and the main increase in in trial 5 when the newest location is shown . We now included stats  and in 3) Relatedly, please explain the colors and shades used in Figure 1D in detail, or change the color scheme. The dark gray alluded to here (Experiment 2 Control) looks like violet.We now moved the color scheme description higher up in the legend, directly after mentioning the groups of 1D. It may have one some screens the impression of violet but it is programmed as dark grey.4) Line 196-198: "Furthermore, because it is the faster-firing neurons that dominate upstate spiking activity and therefore δ amplitude, the slowing of firing rates in the more plastic neurons is most likely the cause of the smaller δ waves seen in these animals". Can a reference be provided for the first part of the statement?We added to which results this was referring to .Also, reference 14 relates to hippocampal slow-firing neurons (as noted above), so any conclusions regarding slow-firing cortical neurons being more plastic need to be justified.We expanded this section including a new citation about cortical neurons.5) For physiological differences, is there any reason why data is not presented comparing the Stable and Overlapping OST condition since this is the central point of the manuscript? As per Methods on pg. 500-506, recordings were obtained separately for each condition.This data and analysis has now been added as mentioned in the main comments.6) The Granger analysis of directionality should be interpreted with caution since Granger can lead to spurious results , especially in oscillating networks.We added this statement and citation to the discussion7) In Figures S6 and S7, none of the axes labels and axes tick labels are visible – they are too small.We remade these figures with larger fonts etc.8) In Figure 4, there is no scale bar for the color plots. In Figure 4C, please explain the acronyms B, Pre, Post.-120ms after ripple)We added color scale bars. The acronyms were explained in the legend: for each types the response at baseline (200-120ms before ripple), pre-ripple (120ms-40ms before ripple), during ripple (40ms before – 40ms after ripple peak), and post-ripple (40msReviewer #2 (Recommendations for the authors):– Check the whole manuscript for grammar e.g., these theories 'provides'… they 'remains' We performed a language check on the manuscript and hope to have caught all mistakes.– Describe the object space task in more detail  in the main manuscript as it is crucial to the manuscriptWe expanded the description.– Which statistical tests were used to test for the hypothesis? Please provide details alongside each test These are provided in the figure legends  to facilitate readability of the main text.– How is this a semantic-like task? Please describe/explain in the manuscript.We now added this to the introduction and discussion.– As a host of electrophysiological changes have been observed, a cartoon model might help show how they all fit together to support the hypothesis of the authorsWe added a cartoon model  and also now expanded the discussion with subheadings. We hope that the new subdivision of effects also helps focus the article. We would like to thank the reviewer for this suggestion, since the model made us aware which of our results from the supplement should rather be included in the main figures. We moved the ripple-response analysis per firing-rate group to Figure 4.– Are hippocampal firing rates different or only prelimbic?Regrettably, we did not manage to place enough tetrodes in the hippocampus to make such an analysis feasible. However, as shown in the main comments, this effect was previously reported by others. Our manipulation made the cortex have similar firing-rates as usually seen in the hippocampus.– Why were the electrophysiological differences of the object space task not compared with the random condition but the home cage condition instead? To determine the neural signature of increased plasticity due to the task in the RGS14414 group wouldn't that comparison be best?As can be seen in the new analysis, random actually behaves like overlapping. It makes sense since in both cases objects are moving and potentially a statistical pattern can be detected. This is also seen in similar learning rates for both conditions in the computational model in the original Plos Biology article. Stable is less of a learning incentive, since less novelty is present during the task. But they are still experiencing something and are still learning a simple task. We now expanded this in the discussion with an explanation of the different possible contrasts with these conditions.Reviewer #3 (Recommendations for the authors):This is a very interesting and thoroughly performed study that addresses very important questions in the field. The authors use a viral-vector-based strategy to over-express a construct that increases dendritic arborization, and spine formation (in the cortex) and is known to boost certain forms of memory. This is interpreted as a means of increasing cortical plasticity. Then, through a combination of behavioral tasks, electrophysiology, and sleep analyses, they report numerous changes in cortical and hippocampal activity. There are a few areas of improvement that would greatly strengthen the study.1. Can the authors address the mechanism governing the effect of RGS? It seems that (based on other studies) transfected brain areas show a general increase in dendritic arborization, spine increase, etc., but it's not clear how this translates to 'experience-dependent' plasticity per se. It seems like non-specific effects, akin to what happens when neurotrophins are injected into V1. The result of the latter is not always normal, or like what happens with naturally occurring plasticity. This may be occurring in this experiment as well, as RGS appears to generally increase hippocampal-cortical connectivity in a manner different than what is driven by the experience. Also, considering that a Lenti virus was used, which can lead to random genome integration, some discussion would be helpful.Yes! This is exactly the point we are trying to make. Naturally occurring plasticity would be selective for experience and using general “plasticity enhancers” would break down the natural regulation and processes. We tried to emphasize this more in the discussion by showing where RGS seems to be learning in “overdrive” and where effects break down due to the sledge-hammer approach (dark purple in the new comic summary).2. Is there any histology showing the localization of the RGS transfection?We now included an overview picture with injection site in the supplement  to complement the transfection picture in Figure 1. There is a lot of background in the immune signal, which is why positive transfection can only be confirmed in zoomed in pictures.3. It seems the best control for the effects of RGS is to use a compound that targets the same pathways. For example, Navarro-Lobato recently used the knockdown of 14-3-3ζ gene for this purpose (JNS 2021). I ask because anisomycin probably involves other pathways .With the manipulation we aimed at generally inhibiting consolidation of the interference trial and not subtly disentangle how RGS14 works. We use RGS14 simply as a tool to manipulate plasticity but are not specifically interested how it does this . This is why we chose to use a “consolidation inhibitor” as is used most often in the memory field, anisomycin.4. What happened to firing rates during REM sleep? Cortical neurons change their activity in REM sleep, possibly more so after 'plasticity'. These analyses would be very helpful.This was added to the supplement, the same effects were seen. Already previously in the supplement we presented how each neuron group modulated their firing rate over the different states.5. Can the authors present data on the duration of NREM up and down states? I ask because this parameter has been measured previously (Vyazovskiy) in the context of sleep need and plasticity.We would like to thank the reviewer for this suggestion. We added the analysis and as SHY predicts we see longer Off-periods in RGS14 and we can replicate the time-course finding where off-periods become shorter over long sleep periods. We added the results to Figure 2 (see above).6. Is it possible that the lack of experience-dependent changes in the RGS animals (in some parameters) is due to a ceiling or masking effect? That RGS saturates the capacity for further change?We now added to the discussion that we cannot disentangle some of the effects. On one side the cellular consolidation vs sleep consolidation but then also increase interference memory vs actually overwriting the old memory . Overall, behaviour in the task was similar (exploration times etc) as was previously reported. We now include this also in the discussion.7. It would be helpful to indicate in the figure legends for the supplementary figures when the data are from RGS or controls.We added this where it was missing and apologize for the oversight.8. What happened to EEGs, FRP's, etc., in the anisomycin-treated rats? This would help one determine if the cortex was abnormal in some way.Regrettably we do not have combined anysomycin – electrophysiolgical data. We currently do not have an implant that can combine cannulas and recordings. However, we now add this to the discussion and also that our general aim was to inhibit consolidation.


When patients with acute ischemic stroke present with suspected large vessel occlusion in the catchment area of a primary stroke center (PSC), the benefit of direct transport to a comprehensive stroke center (CSC) has been suggested. Equipoise remains between transport strategies and the best transport strategy is not well established.We conducted a national investigator-driven, multicenter, randomized, assessor-blinded clinical trial. Patients eligible for intravenous thrombolysis (IVT) who were suspected for large vessel occlusion were randomized 1:1 to admission to the nearest PSC (prioritizing IVT) or direct CSC admission (prioritizing endovascular therapy). The primary outcome was functional improvement at day 90 for all patients with acute ischemic stroke, measured as shift towards a lower score on the modified Rankin Scale score.P=0.31). Onset to groin time for patients with large vessel occlusion was 35 minutes (P=0.007) shorter when patients were transported to a CSC first, whereas onset to needle (IVT) was 30 minutes (P=0.012) shorter when patients were transported to PSC first. IVT was administered in 67% of patients in the PSC group versus 78% in the CSC group and EVT was performed in 53% versus 63% of the patients, respectively.From September 2018 to May 2022, we enrolled 171 patients of whom 104 had acute ischemic stroke. The trial was halted before full recruitment. Baseline characteristics were well balanced. Primary analysis of shift in modified Rankin Scale  revealed an odds ratio for functional improvement at day 90 of 1.42 (95% CI, 0.72–2.82, This trial investigated the benefit of bypassing PSC. We included only IVT-eligible patients presenting <4 hours from onset and with suspected large vessel occlusion. Lack of power prevented the results from showing effect on functional outcome for patients going directly to CSC.https://www.clinicaltrials.gov; Unique identifier: NCT03542188.URL:  With a sensitivity ranging from 0.50 to 0.67 and a specificity ranging from 0.83 to 0.89, these scales generally perform equally well in finding patients with LVO, but a low accuracy among all tools challenges LVO detection.12 If patients are suspected for LVO and are ineligible for IVT, guidelines stipulate direct transport to a CSC. The rationale being, EVT remains a possible acute treatment option.13 The optimal transport strategy for patients with suspicion of LVO and eligibility for IVT remains uncertain. and no significant difference was found in functional outcomes between patients transported to PSC versus CSC in the recent RACECAT trial  that cluster-randomized patients in a 7-hour window.14 The advantage of a bypass transport strategy would be a reduced delay to EVT for patients with confirmed LVO, but the disadvantage would be delay to IVT for patients without LVO. A possible harm for patients with intracerebral hemorrhage (ICH) when subjected to longer transport has also lately been suggested by the RACECAT trialists.Currently, 2 reperfusion treatment modalities exist for patients with acute ischemic stroke (AIS): intravenous thrombolysis (IVT) and endovascular therapy (EVT) for patients with a large vessel occlusion (LVO). Both treatments are critically time dependent,15We aimed to investigate the benefits of bypassing the nearest PSC for patients with AIS with suspected LVO, considered eligible for IVT at the prehospital level by measuring functional outcome after 90 days on the modified Rankin Scale (mRS).TRIAGE-STROKE (Transport Strategy in Patients With Suspected Acute LVO) was conducted as an investigator-initiated, multicenter, randomized, assessor-blinded trial involving all IVT-eligible patients suspected of LVO in a PSC catchment area. The trial was performed in the Central and Northern regions of Denmark from year 2018 to 2022.16 and the protocol and statistical analysis plan are available with the Supplemental Material. The trial was approved by the regional Research Ethics Committee as an acute study. Consent was waived in the acute setting and subsequently obtained from patients or a relative and a trial guardian (supplementary). The study was conducted following good clinical practice E6 guidelines. A steering committee supervised the trial with support from a data monitoring committee. The committee was involved when results from RACECAT were presented and because design, geographic area, and LVO score differed from TRIAGE-STROKE they decided to continue the trial. TRIAGE-STROKE was halted as planned in May 2022 as described in Results.The trial methods have previously been published,The trial had no industry involvement but was funded by a nonprofit organization that did not participate in any part of the trial, data analysis, article preparation, or the decision to publish. The report conforms to the recommendations of the CONSORT  guidelines. Deidentified data are available from the corresponding author on reasonable request.17 score of ≥2, were living independently (prestroke mRS score 0–2), and their symptoms occurred while the patient was in a PSC catchment area (drivetime to PSC was shorter than drivetime to CSC). They had to be candidates for IVT , including being able to arrive at both the CSC and the PSC within 4 hours from stroke onset. Patients were excluded if the stroke occurred inside a hospital or if their life expectancy was below 1 year. Symptom onset was defined as witnessed time of onset or last known well. Decreased consciousness was not an exclusion criterion.Patients with acute focal neurological deficits were eligible for inclusion if they were ≥18 years of age, had a Prehospital Acute Stroke Severity (PASS)LVO was defined as occlusion of at least one of the following vessels: internal carotid artery, first segment of middle cerebral artery, proximal part of first division of middle cerebral artery, or basilar artery occlusion assessed initially by computed tomography angiography or magnetic resonance angiography, which was accessible at all centers.The target population was defined before trial commencement as all randomized patients with a final diagnosis of AIS (modified intention to treat) in both transportation arms. The final diagnosis of AIS was validated in all cases by a stroke neurologist.During the 4-year trial period, the referral 2 PSCs admitted 2491 stroke patients within the 4,5-hour window potentially eligible for screening for LVO symptoms. The shortest road distance between the PSCs and the CSC was 114 km (71 miles) from both PSCs. This corresponds to ≈1 hour of ground transportation in this setting .17 PASS was considered positive when at least 2 of the following 3 items were present: eye deviation, arm paresis, and consciousness/aphasia (patients unable to tell own age or current month). PASS was already in use for LVO detection when the trial was initiated. A previous study had shown a sensitivity of 56%, a specificity of 92%, and a positive predictive value of 40% to detect LVO.18Prehospital paramedics from 86 ambulance bases (128 ambulances) and 2 helicopter bases (2 helicopters) were trained in recognizing stroke and using the PASS stroke severity score.Paramedics consulted all patients with suspected LVO with the stroke neurologist at the CSC by telephone conference. Patients considered trial candidates were screened and randomized using the electronic data capture system, which was accessed via a website by the stroke neurologist. Randomization was performed in blocks of 4 and was stratified by age (<65/≥65 years), geographic region, and PASS score (2 versus 3). Patients were randomly assigned in a 1:1 ratio to either direct CSC admission or admission to the nearest PSC.pm–8 am) and on weekends.During the trial period, one of the PSCs was functionally a CSC during daytime. Patients from this center were included only in off-hours  to assess functional improvement for the target population .All randomized patients were evaluated with mRS on day 90 by trained research personnel blinded to transport strategy (supplementary).Secondary analyses were shifted for the following subgroups: all randomized patients, all patients with hemorrhages, all patients with stroke mimics, all patients with LVO, all ischemic patients with no LVO, and dichotomized analysis of mRS score of 0 to 2 for all patients with AIS. Excellent outcome (mRS score 0–1), and fair outcome (mRS score 0–3) were defined in post hoc analysis. Other secondary outcomes were time from onset, pick-up, and arrival at first hospital for all patients; and time to IVT (needle time) and EVT (groin puncture) for all treated patients with AIS, successful reperfusion defined as modified Treatment in Cerebral Infarction score of 2b–3, and length of CSC stay. Safety outcomes were severe dependency or death (mRS score of 5–6) in patients treated with EVT. Adverse events and severe adverse events were registered according to good clinical practice guidelines by project nurses during admission and at 90-day examination of the patient (supplementary). To measure prehospital and in-hospital delays, detailed time metrics were collected during the trial by emergency medical staff at ambulances and helicopters, and by stroke clinicians.For supplementary, we performed a not-planned subgroup analysis where effect was measured in the subgroups of age, sex, diabetes, hypertension, atrial fibrillation, PASS score, use of IVT and EVT, and presence of an LVO.We originally estimated that a sample size of 600 patients would provide 80% power to detect a 12% difference between the 2 trial target population groups.2 and t tests were used for the dichotomous end points. Secondary efficacy outcomes were not tested for multiple comparisons and the results cannot be used for hypothesis testing or inference.Baseline data for all randomized patients and patients in the target population group was obtained and inspected for imbalances, comparing medians, interquartile ranges (IQRs), and percentages, as appropriate. A multivariate analysis adjusting for age, sex, and prehospital stroke score was performed. The primary target population analysis used univariable ordinal logistic regression comparing the 2 transport arms. The primary effect measure was odds ratio (OR) with a 95% CI for a shift towards a better outcome on the mRS. The Brant and Wald tests were used to show that regression assumption holds true. For the secondary analyses, univariable logistic regression was used to estimate OR for the subgroups, and χwww.R-project.org).All analyses were performed using R software version 4.2.1 . The 2 PSCs had the same corresponding CSC covering an intermediate to rural density catchment area with 887 513 inhabitants  This resulted in poor recruitment Figure S. The 2 PFrom September 2018 to May 2022, 186 patients were assessed for eligibility, 174 underwent randomization, and 171 gave consent for trial participation; 87 were assigned to transport to the CSC; 84 to a PSC. In the included population, randomization was not respected in 2 patients (1%) because exclusion criteria were revealed after randomization, and another 2 patients (1%) had thrombectomy at the PSC offering daytime thrombectomy. No patients were lost to follow-up and all patients were analyzed after intention to treat principle  had AIS, 51 (30%) had ICH, and 16 (9%) had a stroke mimic. Stroke mimics included seizures (N=12), aorta dissection, cervical disc rupture, brain tumor, and hemiplegic migraine (1 each). In the target population with 104 patients with AIS, 71 (68%) had LVO and 33 (32%) had AIS without LVO.The demographic and clinical characteristics, including prestroke morbidity, stroke severity, and door-to-needle time, were similar in the 2 trial groups, both in the target population and the total randomized population Table . Among aThe common OR for improvement measured by mRS at day 90 for all patients with AIS was 1.42;  when transported directly to a CSC Figure . The adjTable S1; Grotta bars are shown in Figure S2).Secondary outcomes showed that for the 71 patients with LVO, OR was 1.41;  in favor of going to CSC first; for the 33 patients with AIS and no LVO, the OR was 1.43;  in favor of going to a CSC first. For the 51 patients with ICH, the common OR in favor of going to a CSC first was 0.94 . The percentages of patients with mRS scores 0 to 3 were 83% in the CSC group versus 64% in the PSC group, corresponding to a 19-percentage point absolute risk reduction. This leads to a number needed to transport to CSC of 5 (for patients with AIS) to make 1 more patient ambulatory; the absolute number needed to transport was 8, taking nonischemic diagnoses into account ; for mRS score 0 to 2, OR was 1.21 ; for mRS score 0 to 3, OR was 2.64 .Among the 33 (32%) patients with AIS secondarily transferred from a PSC to a CSC, 4 (13%) were recanalized when arriving at the CSC (2 spontaneously and 2 had IVT). Sixty patients underwent EVT, and reperfusion  was obtained in 91% of the PSC group compared with 93% of the patients with EVT in CSC group (Table S2).The rate of patients treated with IVT when going first to CSC was 78% versus 67% in the group going to PSC first. The rate of patients who underwent EVT was 63% in the first to CSC group and 53% in the PSC group. The differences were not significant (P<0.001). Time from departure from scene to PSC was 27 minutes (19–38); for direct transport to CSC, it was 58 minutes (43–68). Time from onset to needle was 114 minutes  for PSC patients and 144 minutes  for CSC patients (P=0.012). Time to groin puncture was 222 minutes  for PSC patients needing transfer and 187 minutes  for CSC patients (P=0.007). Hence, onset to groin time for EVT was 35 minutes (P=0.007) longer if patients were transported to a PSC first, whereas onset to needle (for IVT) was 30 minutes (P=0.012) longer if they were transported directly to a CSC.Times from onset to arrival at first hospital was 82 minutes  for patients going first to a PSC and 116 minutes  for patients going directly to a CSC .Time from arrival at a PSC to departure for EVT treatment (door-in-door-out time) was 67 minutes . When arriving at the CSC, delay to groin puncture was 16 minutes  for patients in the PSC group that were transferred to the CSC versus 61 minutes  for the patients going directly to the CSC. Except for the subgroup females the subgroup analysis showed nonsignificant positive point estimates in all subgroups when going first to CSC (Table S3). No imbalances were seen in adverse events (Table S4).Among the total randomized population, OR for receiving a better outcome (shift) was 1.01  when going to a CSC first. The OR of severe dependence or death (mRS score 5–6) at day 90 was 1.72  when transported first to a PSC for patients with LVO, and 3.85  for patients without LVO when going to PSC first. For patients with hemorrhage, the OR was 0.78  for severe dependency or death when going to a PSC first (P=0.3 when transported directly to a CSC).TRIAGE-STROKE randomized patients with clinical signs of LVO eligible for IVT and who had onset of symptoms while located in a PSC catchment area. The trial was carried out in a setting where transport time from the scene to PSC was 27 minutes, transport time from the scene to CSC was 58 minutes, and the transfer time between PSC and CSC was 55 minutes. The study was underpowered and did not have the strength to show an effect on functional outcome at day 90  in favor of going directly to a CSC, suggesting that transport directly to a CSC enhances the probability of becoming ambulatory at day 90. This was a post hoc analysis. We did not adjust for multiple comparisons and the result should be interpreted with caution. Conclusions cannot be made from this.The Grotta bars indicated benefit in the CSC group, why a post hoc analysis of mRS score of 0 to 3 was made. When dichotomizing the primary outcome for mRS score of 0 to 3 at day 90, the OR was 2.65 ; for direct transport to CSC, it was 58 minutes (43–68). This 31-minute time difference renders the results from this trial particularly interesting. Our findings do not challenge the current guidelines to bypass PSC if the increased transport time to CSC is 30 minutes or less. The very efficient workflows at the PSCs shown in the now 2 published randomized controlled trials raise the question if these guidelines also comply with settings where stroke networks operate as efficiently as in these trials.Transport strategy models to predict optimal transport strategies have investigated thresholds for differences in onset to treatment times, with purpose of achieving the best functional outcome.14 Nearly a third of the patients in this trial had unknown symptom onset and 76.8% arrived at the CSC within 4 hours. In TRIAGE-STROKE, the corresponding percentage was 94%. In RACECAT there were statistically more patients receiving IVT in the PSC arm (48% versus 60%) and statistically more patients who received EVT in the CSC arm (49% versus 40%). In TRIAGE-STROKE the treatment rates were generally higher, especially for IVT which was 78% for CSC-first patients versus 67% for PSC-first patients and EVT rates were 63% versus 53% respectively (both nonsignificantly different). Some studies have found that the chance of a good outcome decreases exponentially as a function of time in the first hours after LVO onset, and patients presenting early therefore benefit the most.22 The difference in designs regarding IVT eligibility between the 2 trials would potentially be a benefit for the patients with LVO going first to CSC in RACECAT. But this benefit is probably outweighed by the delay for IVT among both LVO patients and non-LVO patients who were delayed to the extent that IVT could be given. This is reflected in (low) IVT rates at CSC. The concept in TRIAGE was to investigate patients who were eligible for IVT at both the PSC and the CSC.One randomized controlled trial (RACECAT) has been published previously. The trial cluster-randomized 1401 patients to either PSC/telestroke center or a CSC and analyzed 949 patients with AIS. This trial was neutral with respect to shift in mRS .Table S5).TRIAGE-STROKE demonstrated efficient workflows in both arms. The door-in-door-out time at the PSC was 67 minutes and the door-to-groin time was 16 minutes in the PSC arm. Comparing time metrics to RACECAT, patients were delayed approximately half an hour to IVT in both trials when going directly to CSC (30 minutes in TRIAGE-STROKE versus 35 minutes in RACECAT), but delay to EVT was markedly longer in RACECAT (56 minutes versus 35 minutes in TRIAGE-STROKE) for patients going first to PSC. Hence, LVO patients in RACECAT going directly to the CSC were left untreated for a longer time. Time differences in the 2 trials mainly reflect different geographic characteristics  if patients were routed directly to CSC. In the present study, no difference in outcome was seen  for this patient group, however, mortality in patients with ICH in the CSC group was slightly higher compared with the PSC group. This was a nonsignificant difference, but the same trend was seen in RACECAT. This is an issue for further investigation.23The strengths of this study are the valid and individual randomization process, the detailed data collection, and accuracy in collection of prehospital time measures. Furthermore, the study was conducted within the 4.5-hour window of IVT (for patients eligible for IVT) and using a simple stroke severity score (PASS). This study confirmed previous findings that PASS is acceptable for LVO detection with a positive predictive value of 42% in this trial. The tool has been validated to have a PPV of 40 in a previous trial..24 Fourth, one of the PSCs was functionally a CSC in daytime and the 2 included patients treated with EVT at this PSC potentially contributed to contamination of results.This trial has several limitations. First, the planned number of included patients was not achieved as described in the preplanned power analysis and the results are underpowered. Second, generalizability beyond the local setting and population in which the trial was conducted may be limited although detailed time metrics were collected to enhance generalizability. The fact that only 2 out of the planned 6 PSCs were recruiting could introduce nonrandom imbalances in this setting. Third, we acknowledge that the treatment rates for IVT are non significantly higher in the group randomized to CSC first and this could reflect in-hospital challenges that are not an issue elsewhere. Guidelines at the centers are the same but a more conservative use of IVT at PSCs should be further investigated as annually reports have shown this tendencyWe suggest to collect more randomized data addressing this topic to sort out the best transport options for patients with suspected LVO.In this trial, where the difference in delay to both IVT and EVT was approximately half an hour, significant benefits in favor of going directly to a CSC were not demonstrated by functional improvement of mRS. Post hoc analysis revealed a statistically significant OR of staying ambulatory (mRS score of 0–3) in the direct-to-CSC group.The trial was funded by the Novo Nordic Foundation (NNF) 170C0029520. Dr Simonsen received grants from the NNF and Health Research Foundation of central Denmark region. Dr Johnsen received grants from Pfizer and NNF.Dr Fisher reports employment by Beth Israel Deaconess Medical Center and compensation from Lumosa for consultant services. Dr Blauenfeldt reports compensation from Bayer and Novo Nordisk AS for other services. Dr Johnsen reports compensation from TrygFonden, Phizer, and Bristol Meyers Squibb for consultant services. Dr Simonsen reports employment by Aarhus University Hospital. The other authors report no conflicts.TRIAGE-STROKE Investigators and CollaboratorsTrial Registration and OrganizationSupplemental ResultsFigures S1–S3Tables S1–S5


Difficult-to-treat rheumatoid arthritis (D2T RA) remains an area of unmet need in rheumatology. Despite the increasing availability of biologic and targeted synthetic disease-modifying anti-rheumatic drugs (DMARDs) choices to treat RA, a significant proportion of patients with D2T RA still fail to achieve low disease activity or remission [1]. Here we present the case of a patient with D2T RA refractory to multiple lines of conventional DMARDs (cDMARDs) and biological DMARDs (bDMARDs), who has been managed with a novel combination of rituximab and filgotinib, with no adverse side effects or increased infection risk frequency in his follow up period to date.A 51-year-old man was referred with a history of strongly seropositive RA which had failed multiple conventional treatments. He was diagnosed with seropositive RA in his teens and by the age of 23, had extensive deforming arthritis with fixed flexion in his knees for which he has had required arthroplasties. Previous cDMARDs include methotrexate, leflunomide, sulphasalazine and hydroxychloroquine, all of which were stopped due to side effects or inefficacy. He had primary failure to etanercept, adalimumab, tocilizumab, abatacept, and rituximab. He previously had good efficacy on the combination of infliximab and methotrexate but developed secondary failure after a decade. Golimumab and methotrexate worked well in combination but was stopped when methotrexate was discontinued due to a persistent macroscopic haematuria. Other comorbidities include type 1 diabetes, hypercholesterolaemia, hypothyroidism, and osteoporosis.On initial review he was on a combination of baracitinib and sulphasalazine. However, he had evidence of persistent high disease activity with a disease activity score (DAS-28) of 6.09 with 9 tender joints, 4 swollen joints, and VAS 7/10. Inflammatory markers were elevated with an ESR of 60 mm/hr and CRP of 16mg/L.He switched to upadacitinib, but this was discontinued due to headaches and lack of clinical benefit. He was re-trialled on infliximab with concurrent tacrolimus, but this was not effective and he had anti-infliximab antibodies of > 200ng/mL (0-10 ng/mL) on immunological testing. Combination rituximab (1 gram days 1 and 15) and cyclophosphamide (750mg days 3 and 17) was initiated next, which helped with his jaw synovitis, but not the remainder of his joints, and developed gastrointestinal side effects with the cyclophosphamide. Following MDT discussion, filgotinib was introduced and he has managed to reduce his prednisolone dose by 3mg to his current 5mg dose, with no significant deterioration in his joints and an improvement in his inflammatory markers.Our patient fulfils the EULAR definition of D2T RA with failure to multiple biologic and conventional DMARDs, persisting clinical and biochemical evidence of active disease and difficulties with tapering glucocorticoid treatment [2]. This has had a significant impact on his quality of life and activities of daily living. Insulin dependent diabetes adds to the complexities of his management, as though his diabetes is well controlled at baseline with the use of a closed loop insulin system, the recurrent need for steroids had a knock-on sequela on his blood glucose control, and further contributed to his osteoporosis and cardiovascular risk.B cells have been identified as key contributors in the development of RA [3]. The synovial membrane of RA patients is known to contain B cell derived plasma cells which produce RF, with RF positivity being associated with aggressive articular disease and increased extra-articular manifestations [4]. Following the promising outcomes demonstrated in a randomized, double blind, controlled study by Edwards et al [5], we attempted B cell depletion with rituximab in combination with cyclophosphamide. Unfortunately, the cyclophosphamide could not be continued as he developed gastrointestinal side effects and the combination only conferred marginal benefit. Blood tests done 6 months following rituximab did show a depletion in his B cell count from 294 to 0, and in his RF from 864 to 276.Given the infection risks with prolonged depletion of his peripheral blood B cell counts, filgotinib was introduced given its rapid onset of action and short half-life, with the benefits of JAKi over other classes of DMARDs in D2T RA patients being demonstrated by Ochi et al [6]. Reassuringly, there has been no adverse side effects or major infections in his follow up period to date.This case highlights the complexities of managing a patient with D2T RA. Biologics are now the standard of care in RA, but despite the wide range available, 15% of patients are refractory to treatment, and sustained remission is only achieved in less than 50% [7]. The use of concomitant dual biologic therapy to neutralize multiple inflammatory pathways appears to be a promising strategy, but there is a paucity of evidence on the effectiveness and safety of combining two bDMARDs.Previous studies have looked at combining rituximab with a TNFi [8], and a TNFi with abatacept or anakinra, but this was associated with more side effects and no clear advantage [9]. Another study has also shown benefit in adding an IL17 inhibitor to RA patients who have not responded to certolizumab [10]. Ours is the first case that we know of, where rituximab has been used in combination with a JAK inhibitor. We are hoping to approach his treatment strategy by depleting his B cells and combine this with targeting the JAK-STAT pathway to reduce any pro-inflammatory cytokine signaling. The short half-life of filgotinib would be an advantage should there be any adverse side effects or severe infections.Our patient has been strongly engaged in the journey of his RA treatment, but unfortunately, the extent of his previous drug treatments and comorbidities means he is usually excluded from entering any drug trials, and this would similarly be applied to most patients who fall into the D2T RA category. A patient centred treatment approach is especially crucial when undertaking riskier treatment strategies, with all treatment consultations requiring a careful discussion of the risks and benefits. This case emphasizes the need for further research on combination biologic therapies in D2T RA and would add to the growing body of evidence in this area.