Behavioral studies differentiate the rodent dorsal striatum (DS) into lateral and medial regions; however, anatomical evidence suggests that it is a unified structure. To understand striatal dynamics and basal ganglia functions, it is essential to clarify the circuitry that supports this behavioral-based segregation. Here, we show that the mouse DS is made of two non-overlapping functional circuits divided by a boundary. Combining in vivo optopatch-clamp and extracellular recordings of spontaneous and evoked sensory activity, we demonstrate different coupling of lateral and medial striatum to the cortex together with an independent integration of the spontaneous activity, due to particular corticostriatal connectivity and local attributes of each region. Additionally, we show differences in slow and fast oscillations and in the electrophysiological properties between striatonigral and striatopallidal neurons. In summary, these results demonstrate that the rodent DS is segregated in two neuronal circuits, in homology with the caudate and putamen nuclei of primates. The basal ganglia are a group of subcortical nuclei involved in a diversity of functions including motor control, learning, decision making, and reward . The strCortex and thalamus project to the striatum, forming organized glutamatergic synapses along its mediolateral axis, and defining multiple striatal subregions . This coIn addition to the differences in cortical or thalamic axonal innervation, there are dissimilarities in the composition of the striatal microcircuits. The distribution of parvalbumin (PV) and cholinergic interneurons (ChIs) along the mediolateral axis of the DS is not homogeneous . FurtherThe activity of every neural circuit is limited by anatomical and functional constrains which will restrict the repertoire of spontaneous and evoked activity patterns, defining the functional connectivity of the brain . In thisThe slow wave oscillation (SWO) is characterized by periods of high spontaneous activity (Up states) intermingled with silent periods (Down states) at the frequency of ~ 1 Hz, which is originated in the cortex  and propBecause MSNs recorded in vivo are known to fire scarcely , we perfIn conclusion, consistent with previous behavioral studies, our results demonstrate that DS is divided in two functional circuits, separated by a sharp boundary, each of them with specific properties that are essential to understand the striatum and basal ganglia functions.We obtained in vivo whole-cell patch-clamp recordings from 223 neurons located in the DS (n = 197) and several cortical areas (n = 26). All of them displayed SWO with prominent Up and Down states  and medial  regions of the DS in anesthetized mice. The membrane potential of the Down states was stable during the recordings with no differences between striatal regions . MSNs exhibited a rich subthreshold activity during Up states; however, they displayed a very low rate of spontaneous action potentials with similar frequencies along DS . Input resistance was higher for DLS-MSNs . These changes in resistance could impact in the neuronal properties of synaptic integration, setting the gain and timing for their synaptic inputs. Thus, the integration of the spontaneous activity on DLS- and DMS-MSNs could be, at least partially, modulated by the cellular differences which underlie their intrinsic electrophysiological properties.We first studied the properties of the SWO recorded in whole-cell from dorsal striatal MSNs. In order to obtain a quantitative description of the activity of the MSNs in this brain state, we computed 13 different features to characterize the magnitude  and shape  of the Up states , from whOnce we obtained a statistical prove of the differences in the SWO between DLS and DMS, we asked whether these differences were sufficient to define DLS- and DMS-MSNs as two different populations. More specifically, we asked which parameters, or combination of them, could be used to distinguish between DLS- and DMS SWO more accurately. To do so, we searched for those features of the Up states whose combination maximized the accuracy of classifying MSNs corresponding to DLS or DMS circuits. Using a supervised machine-learning technique, named Support Vector Machine (SVM)  with a lBecause multiple MSNs were recorded from the same animals . Following this divergence in beta band, we tested whether high-frequency oscillation contained enough information to classify DLS- and DMS-MSNs. To that end, we trained an SVM with a linear kernel with the amplitude and position of the peak energy inside the Up state of each band. We obtained a classification accuracy of 70.61 ± 0.89% indicating that the high frequencies do not discriminate between DS regions as clearly as Up state properties. Moreover, the addition of the high-frequency properties as extra features to our previous classifier did not improve the level of discrimination obtained by the SWO features  , a hypotThus, we conclude that all recorded MSNs in DS belong to the previously described DMS and DLS functional circuits and they are separated by a sharp functional boundary.MSNs in the DS integrate bilateral and multisensory information . This prAnatomically, it has been described that several visual areas project to the DS. For example, V1 projections are located in the most medial regions of the DS, while medial and lateral anterior visual cortical areas extend their projections to more lateral striatal territories , includiTo understand the main source of differences between the integration of the spontaneous activity in the DLS- and DMS-MSNs, we started by analyzing the first ranked feature: number of peaks during Up states . It is kWe performed in vivo whole-cell recordings in Layer V of FrA , M1 (primary motor cortex), S1 , and V1  . We founThe main features for the classification of DLS- and DMS-MSNs  were relIn summary, our results show that the main differences between DLS- and DMS-MSNs spontaneous activity, selected by our classifier, are consistent with the number and the temporal distribution of the voltage fluctuations of the membrane potential during the Up states, suggesting that they are modulated by excitatory inputs from specific cortical areas and the striatal microcircuits interactions, mostly inhibitory.The SWO has a cortical origin, from where it propagates to the striatum activating MSNs  and inteNext, we used the IMF carrying the slow component of the SWO . ConsistThese results predicted that, following the known propagation of the SWO across the cortex, with a predominant rostro-caudal direction , a sequeFinally, in order to test our prediction, we performed two simultaneous whole-cell patch-clamp recordings in two identified MSNs, located in the DLS and DMS . A repreIn conclusion, our results regarding the propagation of the cortical Up states to DS, confirm a stronger functional coupling of FrA to DLS and V1 to DMS. This generates a sequential activation of the DS, in which in most of the cases, DLS precedes DMS by tens of milliseconds.In a final step to deconstruct DS circuitry, we studied the activity of MSNs corresponding to the direct or indirect pathway. To understand corticostriatal dynamics it is essential to apprehend how both pathways integrate the upstream cortical activity. To answer our question, we analyzed the whole-cell recoding of the DS that were optogenetically identified as direct and indirect MSNs through the optopatcher  allowed Finally, we studied whether the differences in the electrophysiological properties between DLS- and DMS-MSNs, described before , were deIn summary, our results show that DLS- and DMS-MSNs cannot be subdivided by the direct and indirect pathways. However, their differences in resistance and integration of the spontaneous activity demonstrate that direct and indirect MSNs have distinct properties in the DLS and DMS.Circuits are limited but not defined by their anatomy; different axonal projections, cell types and synaptic dynamics delimit their spontaneous and evoked activity patterns. In this study, we have described two functional circuits in the anatomically homogenous DS, covering the medial and lateral regions and separated by a sharp functional boundary. We discriminated DLS and DMS circuits based on different properties of their spontaneous activity recorded in MSNs by means of the in vivo whole-cell patch-clamp technique. The use of an anesthetized preparation was convenient for a number of reasons; first, it granted the mechanical stability that allowed the recording of our dataset that includes whole-cell patch clamp recordings in multiple cortical and striatal areas, specially the double patch-clamp recordings in the striatum. In addition, during anesthesia, as well as sleep and resting awake periods, the brain activity is in the SWO regime . This stTo analyze the oscillatory activity, we used NA-MEMD algorithm  togetherIn humans, the dynamics of the SWO have demonstrated to be highly reproducible within and across subjects, providing information about the general state of the cerebral cortex and suggesting its applicability as a method to unravel functional circuits . The anaWe have shown that DS is divided in two different circuits, demonstrating the presence of a sharp functional transition from one circuit to another in the central region of the DS. This sharp transition is unexpected given the anatomical organization of corticostriatal projections, due to the profusion of axon terminals which provide overlapping inputs from multiple cortical areas to several regions of the DS . Hence, A and GABAB receptor activation  mouse line was crossed with the Channelrhodopsin (ChR2)-YFP reporter mouse line  to induce expression of ChR2 in indirect MSNs and was used to perform optogenetic differentiation of direct (dMSN) and indirect (iMSNs) MSNs , while pAdult mice of both sexes , between 12 and 44 weeks of age were used to perform the experiments . AnestheWhole-cell recordings were obtained in the DS from DLS, DMS, DCS between 2013 and 2647 μm depth. The number of striatal neurons was 126: DLS = 77, from which direct MSNs = 49, indirect MSNs = 34; DMS n = 91, from which direct MSNs = 65, indirect MSNs = 32; DCS n = 17, from which dDCS = 10, iDCS = 7. Cortical neurons (n = 26) were recorded from layer V in FrA, M1, S1 and V1 at a depth of 675–926 µm. In detail, the number of recorded neurons was: Frontal association cortex , primary motor cortex , primary somatosensory cortex , and primary visual cortex (n = 6). Additional paired simultaneous whole-cell recordings of MSNs (n = 6 pairs) were obtained from DLS and DMS between 2039 and 2348 μm of depth, by means of two micromanipulators . All of them in a perpendicular penetration angle of ~ 30°, except for DCS cells, in which the angle was ~20°. The exposed brain was continuously covered with 0.9% NaCl to prevent drying. Signals were amplified using MultiClamp 700B amplifier (Molecular Devices) and digitized at 20 kHz with a CED acquisition board and Spike two software (Cambridge Electronic Design).Borosilicate patch pipettes , were pulled with a Flaming/Brown micropipette puller P-1000 (Sutter Instruments) and had an initial resistance of 6–12 MΩ. Pipettes were back-filled with intracellular solution containing: 125 mM K-gluconate, 10 mM KCl, 10 mM Na-Phosphocreatine, 10 mM HEPES, 4 mM ATP-Mg and 0.3 mM GTP-Na. pH and osmolality were adjusted to ~7.4 and ~280 mOsm/L respectively. Biocytin  was then added to the intracellular solution to perform cell reconstruction after the experiment. To perform the analysis, 100 s of spontaneous activity  were used from the recording. Input resistance  was measNeurons were identified according to their recorded electrical properties and following morphological staining, according to the aspiny dendrites in the case of the ChI and FS interneurons. FS interneurons displayed narrow action potentials, relatively depolarized resting membrane potential, high discharge rate of action potentials, and no apparent inward rectification. Cholinergic interneurons were characterized by their depolarized membrane potential, voltage sag response to current step injections and spontaneous tonic discharge. From the whole population of recorded neurons, 2 of them were identify as FS, one in the DLS and other in the DMS, 3 ChI were recorded in the DMS. These five striatal interneurons were excluded from the data set. Neurons having a deviation by more than 10 mV from their initial resting membrane potential were excluded from analysis. Only MSNs and cortical neurons were included in the data set.Extracellular recordings were obtained using unipolar tungsten electrodes with impedance of 1 to 2 MΩ. The electrodes were placed in infragranular layers (~1000 µM depth from the pia) of two cortical regions in each experiment, from FrA, M1, S1, V1 cortex with an angle between 15° and 25°. Recordings were amplified using a differential AC Amplifier model 1700 (A-M Systems) and digitized at 20 KHz with CED and Spike-2, simultaneously with the whole-cell recording.Visual stimuli were delivered by a white LED positioned 50 mm from the contralateral eye. Duration was 15 ms and it was triggered every 5 s (0.2 Hz) during whole-cell in 17 MSNs recorded in the DCS coordinate and in 10 MSNs recorded in the DMS coordinate. The eye was covered with artificial eye drops  in order to prevent drying.In order to identify ‘on line’ the specific type of MSNs belonging to the direct (dMSNs) and indirect (iMSNs) pathways, the optopatcher was used . One or At the end of each experiment, mice were sacrificed with a lethal dose of sodium pentobarbital and perfused with a solution containing 4% paraformaldehyde in 0.1 M phosphate buffer . Brains were extracted and stored in PBS solution until the cutting. Before cutting, brains were transferred into PBS containing 30% sucrose for at least 48 hr. Coronal slices (25 μm thick) containing the entire striatum from the recorded side  plus 0.5 standard deviations. Then, we merged the parts of fragmented Up states, detected as an interval shorter than 250 ms between the transition of a prospective Up state to a prospective Down state to the transition from a prospective Down state to a prospective Up state. Finally, detected Up states shorter than 200 ms where discarded.When studying propagation of Up states, we considered two Up states from two different cortical regions, or a cortical LFP recording and an MSN to be part of the same SWO, thus propagating from one region to the other, when their onsets were closer in time than 500 ms.findpeaks Matlab function on the smoothed (200 ms smooth) Up state with a minimum peak height of 0.3 standard deviations and a minimum distance between peaks of 160 ms. We also computed the length of the Up state (13), the mean amplitude of the Up state, computed as the difference in membrane potential average value between Up and Down states (5) and the speed of the transition from the Down to the Up state (8), and from the Up to the Down state (9) as well as the Slope transition ratio, computed as the speed of transition from Down to Up states divided by the one from Up to Down states (10). In order to measure the slope speeds, we used a vector consisting in −100 to +200 ms  relative to the transition either to the Up or the Down state. Then, we smoothed (100 ms smooth) the selected region and computed its derivative. We delimited the slope computing the region over a threshold calculated as the mean plus 0.3 standard deviations of the derivative. Once we had delimited the transition, we fitted a lineal function to that region of the recording to compute the slope speed.In order to describe the properties of the SWO of the recorded MSNs, we computed 13 parameters . First, Intracellular synaptic currents were extracted using sharp deflections of the membrane voltage using a modification of the method presented here . In brieA inhibition ~ −70 mV. The average membrane potential during the Down states of the recorded MSNs was −69.2 ± 6.48 mV for DLS-MSNs and −69.31 ± 7.68 mV for DMS-MSNs. Around these voltages, the membrane fluctuations are depolarizing and excitatory. However, during the Up states , both depolarized and hyperpolarized events can be detected , in which the reversal potential for excitation is ~ −5 mV and GABAdetected . As prevdetected , this meWe classified MSNs from DLS and DMS using a Support Vector Machine (SVM)  with a lIn order to determine which features were more relevant for the classification of DLS- and DMS-MSNs, we used Recursive Feature Elimination. This approach organizes the features relative to their importance to the classification by recursively pruning the least relevant features starting from the initial set.Support Vector Machines (SVMs) are supervised classifiers that predict the category of a new entry based on their previous training history and are not suitable to predict new categories in novel data. In order to study the spontaneous activity of DCS-MSNs, we examined its distribution in the classification space, relative to the classification boundary instead of directly using the output of a classifier. This approach provides information, not only about whether the new data (DCS-MSNs) creates a new cluster in the classification space, but also about where is it placed along the classification axis used to discriminate between DLS- and DMS-MSNs. Hence, it provides a more informative approach to the distribution of DCS-MSNs relative to DLS- and DMS-MSNs than a direct multi-category classification .To calculate the distribution of the data along the classification boundary, we first obtained the coefficients and intersection values of the SVM used to classify the MSNs belonging to DLS and DMS. Once we obtained these parameters, we used them to project all data points to this hypervector and computed the probability density function of the new distributions . Now, thhttp://www.commsp.ee.ic.ac.uk/mandic/research/emd.htm). Standard stopping criterion was described elsewhere  algorithm  togetherlsewhere . At lastlsewhere . Once weWe computed the frequency of the SWO, and theta (6–10 Hz), beta (10–20 Hz) and gamma (20–80 Hz) bands as the instantaneous frequency using the Hilbert transform . For a gAll experimental comparisons were tested using the Wilcoxon Rank-sum test except stated otherwise. Directionality of the SWO in the cortex and in the striatum was tested using Wilcoxon signed rank test. Phase locking of different frequency bands was tested using Rayleigh test. When required, alpha values for multiple comparisons were corrected using Holm-Bonferroni correction. Error bars presented in the graphs represent the standard deviation unless stated otherwise. In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.Acceptance summary:Using multiple in vivo patch-clamp recordings coupled to local field potentials recordings in anesthetized animals, Alegre-Cortes et al. have explored and compared the properties of Dorso-medial striatum and Dorso-Lateral Striatum circuits. The technical approaches to explore the question are particularly impressive as double in vivo patch-clamp recordings in striatum have never been performed before. It allowed comparing the two striatal circuits and/or cortical neuronal responses simultaneously. The study brings new insights on the distinction between functional areas within the striatum using slow wave oscillations recordings and visual stimulations. The results are interesting and convincing, and the methodology appropriate to draw the conclusions presented here. The revised manuscript shows large improvements in linking different technical aspects of the study and the detailed explanations increase the clarity of the conclusions. The main conclusion (and novelty) is that the difference between the two striatal regions is not solely based on their cortical inputs but also on different properties of MSNs belonging to each region. Together these new findings change the current view of striatal circuits.Decision letter after peer review:eLife. Your article has been reviewed by two peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Laura Colgin as the Senior Editor. The following individual involved in review of your submission has agreed to reveal their identity: John Reynolds (Reviewer #2).Thank you for submitting your article "Medium spiny neurons activity reveals the discrete segregation of mouse dorsal striatum" for consideration by The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.eLife papers without additional data, even if they feel that they would make the manuscript stronger. Thus the revisions requested below only address clarity and presentation.We would like to draw your attention to changes in our revision policy that we have made in response to COVID-19 (https://elifesciences.org/articles/57162). Specifically, we are asking editors to accept without delay manuscripts, like yours, that they judge can stand as Summary:Using double in vivo patch-clamp recordings in striatum the authors have compared DMS and DLS striatal and/or cortical neuronal responses simultaneously. The study brings sheds new light on the existence of distinct functional areas within the striatum using slow wave oscillations recordings and visual stimulations. The main conclusion is that the difference between the two striatal regions are not solely based on their cortical inputs but also on different properties of MSNs belonging to each region.Revisions:1) The authors must provide the rationale for their choice of the parameters chosen to distinguish DMS from DLS neurons. First, if it is not sufficiently for expert reviewers, "average" readers will be at lost. Second, the whole paper relies on these parameters and careful explanations as to why and how is warranted.2) Double patch in vivo is "heroic" and while the reason for performing the study in anesthetized animals might be obvious to most, the Discussion must be expanded. In particular, slow wave oscillations are a specific regime within these networks and analyzing the networks in that state might be limiting. In this context, Mahon et al., 2006, showed a very different activity in the corticostriatal oscillations between SWO and awake animals. The results of the present study should be discussed in light of the different regimes existing in the corticostriatal pathways depending on the vigilance state of the animals.3) Concerning the propagation of SWO in the striatum, the Authors describe no difference in correlation with S1 inputs between DMS and DLS. How do they explain this? These experiments show nicely the sequence of activation in the corticostriatal loops. Nevertheless, they could argue against the conclusion of the study. If there is a difference in correlation between cortical areas projecting to only one striatal region (DMS or DLS) and no difference in correlation with S1, projecting to both, this would argue that the cortical inputs are the most important to drive DMS or DLS activity, and not the local circuits. This should be discussed.4) A more detailed methodological explanation of how the Noise-assisted multivariate empirical mode decomposition (NA-MEMD) was implemented to analyze membrane oscillations of single neurons as well as a graphical explanation as to how the potentials have been derived are warranted.Reviewer #1:Using multiple in vivo patch-clamp recordings coupled to LFP recordings in anesthetized animals, the study by Alegre-Cortes et al. explores the difference between DMS and DLS circuit properties. The technical approaches to explore the question are impressive as double in vivo patch-clamp recordings in striatum have never been performed before and allows to compare DMS and DLS striatal and/or cortical neuronal responses simultaneously. The study brings new insights on the distinction between functional areas within the striatum using slow wave oscillations recordings and visual stimulations. The main conclusion is that the difference between the two striatal regions are not solely based on their cortical inputs but also on different properties of MSNs belonging to each region.The results are interesting and convincing, and the methodology appropriate to draw the conclusions presented here. Nevertheless, attempts to link different aspects of the study and more detailed explanations would improve the clarity of the conclusions of the manuscript. Please see below some specific points.1) In its present form the manuscript presents a lot of different parameters to show differences in DMS and DLS. It would be important to link the different properties together to clearly highlight which components would be the most important to distinguish DMS or DLS and therefore draw a clearer conclusion about their different properties.For example, data regarding the electrophysiological properties of DMS- and DLS-MSNs would be better on the main figures and the authors should try to correlate these properties and the up states characteristics. It would be useful to explain better the rationale of the choice of these 3 specific parameters and try to include others in the overall interpretation.Another example, linking Figure 1 and Figure 4 would show relationship between different parameters of cortical oscillations and related ones in DMS and DLS. Figure 4: Why only focusing on the number of peaks during the up states while interesting differences have been highlighted in Figure1 on other features of the up states? The reviewer would recommend to include analysis of up state amplitude and slopes in this part of the manuscript.2) Concerning the propagation of SWO in the striatum, the Authors describe no difference in correlation with S1 inputs between DMS and DLS. How do they explain this? These experiments show nicely the sequence of activation in the corticostriatal loops. Nevertheless, they could argue against the conclusion of the study. If there is a difference in correlation between cortical areas projecting to only one striatal region (DMS or DLS) and no difference in correlation with S1, projecting to both, this would argue that the cortical inputs are the most important to drive DMS or DLS activity, and not the local circuits. This should be discussed.3) Related to this, sensory stimulation data did not support the conclusions of a clear segregation of DMS and DLS properties. The explanation for such a difference between sensory stimulation and SWO should be discussed in more details.The rationale for using visual (as opposed to whisker) stimulation is that it targets only the medial part of the striatum. This allowed the Authors to show a distinction between DMS and DCS, but, which was mainly due to different cortical input origin. Moreover, since the aim of the study is to highlight that differences within the striatum that are not due to different inputs, maybe using a common cortical input to activate both striatal networks would probably be suitable to explore if specific responses are only due to local properties. Whisker stimulation was excluded to test the distinction between DMS and DLS because S1 projects to both regions, but is it because the weight of S1 inputs are different on DMS and DLS and so it would be impossible to dissociate inputs from local circuits? If possible a common equivalent input could be used for the aim the study. These experiments would not be required for the present study but maybe the Discussion should include these points.4) Concerning the measure of the depolarizing/hyperpolarizing ratio (DH ratio), it is not clear how the authors conclude of striatal microcircuits contribution from the recordings in Figure 4 and how it can be isolated from cortical drive from the recordings? If not clearly stated, the reviewer is not sure that this analysis is necessary to support the author's conclusions.5) The method used to do the delineation of the DCS is not clear and should be better explained in the main text.6) Slow wave oscillations are a specific regime within these networks and even though informative, analyzing the networks in that state might be limiting. Mahon et al., 2006, they show a very different activity in the corticostriatal oscillations between SWO and awake animals. The results of the present study should be discussed in light of the different regimes existing in the corticostriatal pathways depending on the vigilance state of the animals.7) One last suggestion is that direct and indirect pathway data could be presented at the beginning with intrinsic properties and up states characterization of the MSNs in DMS and DLS to avoid coming back to intrinsic properties at the end of the manuscript.Reviewer #2:This interesting study presents a number of analyses of in vivo intracellular recordings of the membrane potential activity of MSNs in the striatum, which indicate that there are differences in electrophysiological properties in dorsomedial vs dorsolateral striatum. Furthermore there appears to be a discrete demarcation between neurons in each area with only a small overlap and no gradient. The authors then go on to record from afferent areas to determine if the patterns of activity in the MSNs are echoed by differences in neuronal activity in functional areas projecting to the striatum. Finally, they further demarcate the neurons in each area by whether they are dMSNs or iMSNs, and find that there were further differences of neurons in the lateral striatum only.There are a number of strengths to this paper, primarily by virtue of the very difficult experiments that these involve – of particular note is the heroic dual patching experiments which deserve special mention – there have been only a couple of dual intracellular in vivo studies before but in rats using sharp electrodes – obtaining low access resistance recordings from simultaneously recorded neurons using patch recording in mice is very notable. Overall the conclusions are robust and these raise the possibility of why these differences are present , which the authors speculate on but do not investigate.Although I can understand how temporal measures such a durations of states and trajectory of transitions can be quantified using the NA-MEMD algorithm, I cannot see how the actual average and SD of the up and down state membrane potentials have been extracted from the IMFs, when looking at those exemplified in Figure 2—figure supplement 2C and the SWO also shown in Figure 1C. Was there an additional step needed to calculate a probability density function from the distribution of the membrane potentials, as in Figure 5C? If not, because as the authors say it is the first time that the algorithm has been used to analyse membrane oscillations of single neurons, a much more detailed methodological graphical explanation as to how the potentials have been derived is warranted.The authors found "an Up state in FrA preceding an Up state in V1 in the 71% of times , data not shown" – this is itself very interesting and the data SHOULD be shown as further suggestive (correlative) evidence that the propagation of Up state timing from lateral to medial striatum is driven by frontal to visual cortex transition of slow wave.The observation of a difference between cellular properties and SWO measures in lateral and medial regions is just that, without any expose of the underlying reason. This paper could be much more enriched by some investigation of a mechanism that might explain these differences. In lieu of a full morphological comparison, one possibility the authors do raise that is quite achievable in a relatively short time is considering the effects of differences in dopamine innervation, either directly or indirectly via cholinergic neuronal innervation. Indeed there is known to be medial to lateral gradients of many neuromodulatory components including dopamine receptors which might contribute to the regional differences. The authors should consider undertaking a series of comparison experiments under blockade of D1 and D2 antagonists  and see if this normalises the observed differences between neurons. This is relatively more straightforward than trying to normalise FSI innervation and in my opinion is much more likely to provide at least a partial explanation. Revisions:1) The authors must provide the rationale for their choice of the parameters chosen to distinguish DMS from DLS neurons. First, if it is not sufficiently for expert reviewers, "average" readers will be at lost. Second, the whole paper relies on these parameters and careful explanations as to why and how is warranted.We have now included more detailed and careful explanations of the 13 features of the SWO in the Materials and methods section. In addition, an explanation about the 3 selected features by the Recursive Feature Elimination analysis has been included, as the ones that their combination better discriminate between de DLS- and DMS-MSNs , in the Result section.2) Double patch in vivo is "heroic" and while the reason for performing the study in anesthetized animals might be obvious to most, the Discussion must be expanded. In particular, slow wave oscillations are a specific regime within these networks and analyzing the networks in that state might be limiting. In this context, Mahon et al., 2006, showed a very different activity in the corticostriatal oscillations between SWO and awake animals. The results of the present study should be discussed in light of the different regimes existing in the corticostriatal pathways depending on the vigilance state of the animals.We have discussed this important idea in the context of Mahon et al., 2006 (Discussion). We also answer to reviewer 1 in detail below.3) Concerning the propagation of SWO in the striatum, the Authors describe no difference in correlation with S1 inputs between DMS and DLS. How do they explain this? These experiments show nicely the sequence of activation in the corticostriatal loops. Nevertheless, they could argue against the conclusion of the study. If there is a difference in correlation between cortical areas projecting to only one striatal region (DMS or DLS) and no difference in correlation with S1, projecting to both, this would argue that the cortical inputs are the most important to drive DMS or DLS activity, and not the local circuits. This should be discussed.This important point is now extensively discussed in the text and also please find our extended response to reviewer #1 (see below).4) A more detailed methodological explanation of how the Noise-assisted multivariate empirical mode decomposition (NA-MEMD) was implemented to analyze membrane oscillations of single neurons as well as a graphical explanation as to how the potentials have been derived are warranted.We have clarified the analysis performed during this paper, as sometimes it was ambiguous when the raw traces or an IMF (the output of the NA-MEMD) was being used. It is now clearer when the NA-MEMD is used to analyse membrane oscillations in the Figures 2 and 5. The presence of an IMF in the Figure 1 was particularly confusing. Therefore, we have removed the IMF from panel 1C and now we have added the IMFs to Figure 5A-B, which illustrates better when is used.Both the Up state properties in Figure 1 and hyperpolarizing and depolarizing potentials in Figure 4 were computed using the raw membrane potential and not the NA-MEMD, as detailed in the answer to Dr Reynolds. This new version of the manuscript states more clearly when the NA-MEMD is applied.Reviewer #1:Using multiple in vivo patch-clamp recordings coupled to LFP recordings in anesthetized animals, the study by Alegre-Cortes et al. explores the difference between DMS and DLS circuit properties. The technical approaches to explore the question are impressive as double in vivo patch-clamp recordings in striatum have never been performed before and allows to compare DMS and DLS striatal and/or cortical neuronal responses simultaneously. The study brings new insights on the distinction between functional areas within the striatum using slow wave oscillations recordings and visual stimulations. The main conclusion is that the difference between the two striatal regions are not solely based on their cortical inputs but also on different properties of MSNs belonging to each region.The results are interesting and convincing, and the methodology appropriate to draw the conclusions presented here. Nevertheless, attempts to link different aspects of the study and more detailed explanations would improve the clarity of the conclusions of the manuscript. Please see below some specific points.1) In its present form the manuscript presents a lot of different parameters to show differences in DMS and DLS. It would be important to link the different properties together to clearly highlight which components would be the most important to distinguish DMS or DLS and therefore draw a clearer conclusion about their different properties.We thank reviewer #1 for their supportive comments.We started the study with a statistical description of the magnitude and shape of the Up states, using 13 different parameters, now extensively explained in the new version . The characterization of the Up state features, related with its amplitude, duration and transition slopes have been previously used . From the 13 Up state features used, 11 of them were statistically different between DLS and DMS . Then, we investigated for the combination of features that together were more powerful to distinguish between DLS- and DMS-MSNs. To do so, we recursively pruned the features using a Support Vector Machine; this implies that, starting from the whole set of parameters, they were recursively eliminated depending on their relevance for the classification of DLS- and DMS-MSNs. As a result, we obtained a sorting of the features depending on their significance for the separation of DLS- and DMS-MSNs. The final result consisted in a combination of three parameters, number of peaks (feature nº 11), amplitude (feature nº 5) and Up to Down transition slope (feature nº 9), which when combined let us classify MSNs .We have amended the Results section “The spontaneous activity from DLS- and DMS-MSNs have different attributes” in order to clarify the logic behind the analysis.Figure 1 displays the most relevant information underlying the division of DLS and DMS. This is why we decided to place it at the beginning of the manuscript.For example, data regarding the electrophysiological properties of DMS- and DLS-MSNs would be better on the main figures and the authors should try to correlate these properties and the up states characteristics. It would be useful to explain better the rationale of the choice of these 3 specific parameters and try to include others in the overall interpretation.We thank reviewer #1 for this suggestion. We have clarified the logic and technique behind the selection of the three specific parameters.As we answered in the previous question, the rationale of the choice of the 3 specific features is based on their power to classify between MSNs and is determined by the Recursive Feature Elimination analysis.Our results support that, while it is clear that the differences between membrane electrophysiological properties of DLS and DMS are relevant for our understanding of the DS and may have a small contribution in the difference of spontaneous activity of each region, the main agents modulating the SWO in DLS and DMS are the cortical excitatory inputs and the local striatal interactions (which includes different gradients of neuromodulators and interneurons). Thus, we do not argue for a direct and measurable correlation between any membrane property and Up state feature.In an effort to clarify this question, we computed the correlation of the membrane properties and the Up state features of the MSNs. Nevertheless, no comparison had an absolute correlation value over 0.3 , suggesting that there is not a lineal relationship between these two groups of properties (this result is now added in the text). For instance, based on the electrophysiological properties, it is expected that higher input resistance will contribute to generate higher amplitudes when neurons are integrating synaptic inputs, but it was not the case. Up states amplitudes were higher in the DMS-MSNs with smaller values of input resistance than DLS-MSNs .Thus, after carefully discussing the suggestion of reviewer #1, we think that the actual distribution of figures and tables is the best way to explain our results.Another example, linking Figure 1 and Figure 4 would show relationship between different parameters of cortical oscillations and related ones in DMS and DLS. Figure 4: Why only focusing on the number of peaks during the up states while interesting differences have been highlighted in Figure1 on other features of the up states? The reviewer would recommend to include analysis of up state amplitude and slopes in this part of the manuscript.We have modified the Results section “DLS- and DMS-MSNs segregation is explained by their membrane potential transitions” to clarify the question of the reviewer. This section and Figure 4 were done with the aim to understand the main features of the Up states selected by the Recursive Features Elimination analysis classifying MSNs . We have also extended the Discussion section “DLS and DMS are two different functional circuits in mouse” in order to clarify this question.Figure 4D-F describes the differences in slopes. We have made an effort to improve the visualization of the DH ratio and its relation with the slopes, underlying changes between DLS and DMS-MSNs; this analysis suggests that the balance of excitatory and inhibitory inputs onto the MSNs is different during the transitions between states, suggesting a different interaction between the long range excitatory inputs and the local inhibitory ones. We have also added a new panel showing the grand average of the upward and downward states transitions slopes .The third relevant feature to classify MSNs was the Up state amplitude . In order to explain the Up state amplitude disparity, our initial approach was to calculate the MSNs input resistance, because higher input resistance should increase the amplitude of the synaptic inputs. However, against this hypothesis, amplitude was higher in DMS-MSNs. The number of direct excitatory inputs that single DMS-MSNs receives from cortex could be a candidate underlying this unexpected result. It was described that the medial region of the dorsal striatum is innervated by the highest degree of cortical input heterogeneity . Thus, it could be possible that single DMS-MSNs receive larger number of cortical inputs which promote bigger Up states amplitudes, even with smaller input resistance.2) Concerning the propagation of SWO in the striatum, the Authors describe no difference in correlation with S1 inputs between DMS and DLS. How do they explain this? These experiments show nicely the sequence of activation in the corticostriatal loops. Nevertheless, they could argue against the conclusion of the study. If there is a difference in correlation between cortical areas projecting to only one striatal region (DMS or DLS) and no difference in correlation with S1, projecting to both, this would argue that the cortical inputs are the most important to drive DMS or DLS activity, and not the local circuits. This should be discussed.We thank the reviewer #1 to help us to clarify this important question. The general conclusion extracted from our results claims that the entire distribution of corticostriatal projections together with the local microcircuit properties are both necessary for the functional division of the DLS and DMS. Also, it is important to remember that the striatum is basically a GABAergic network that requires excitatory inputs acting as activity driver.The approach used in Figure 5 aims to understand the global correlation of the striatal SWO with respect to different cortical areas, which is different from the modulation of activity once the MSNs are in the active Up state (inside the up state). In order to understand the propagation of the cortical SWO to the striatum we used the IMF that carried this wave (~1Hz), this method is now illustrated in Figure 5A-B. Note how this function retains the Up-Down dynamics but excludes the fast modulation of the membrane voltage. Thus, the cross-correlation analysis showed in Figure 5 gave us information about which regions of the cortex were the ones “triggering” the Up states to DLS and DMS.Following the indications of reviewer #1, we have extended the Discussion of the paper to address this concerns, adding a new reference .3) Related to this, sensory stimulation data did not support the conclusions of a clear segregation of DMS and DLS properties. The explanation for such a difference between sensory stimulation and SWO should be discussed in more details.The rationale for using visual (as opposed to whisker) stimulation is that it targets only the medial part of the striatum. This allowed the Authors to show a distinction between DMS and DCS, but, which was mainly due to different cortical input origin. Moreover, since the aim of the study is to highlight that differences within the striatum that are not due to different inputs, maybe using a common cortical input to activate both striatal networks would probably be suitable to explore if specific responses are only due to local properties. Whisker stimulation was excluded to test the distinction between DMS and DLS because S1 projects to both regions, but is it because the weight of S1 inputs are different on DMS and DLS and so it would be impossible to dissociate inputs from local circuits? If possible a common equivalent input could be used for the aim the study. These experiments would not be required for the present study but maybe the Discussion should include these points.We thank reviewer #1 for this input. We have extended the Discussion section entitled “DLS and DMS are not defined by their sensory responses”, to include it.DMS-MSNs, as well as DLS-MSNs respond to whisker (tactile) stimulation with the same onset delay, most probably because both regions share the axonal projections from S1. This corticostriatal innervation is characterized by higher axonal density in the lateral pole of the DS and lower density in its medial pole . Moreover, previous studies described a gradient of S1 axons invading both regions , with progressive loss of axons from lateral to medial territories and without any anatomical border limiting this cortical innervation. Therefore, it is expected that this gradual decrease of projections from lateral to medial striatum will be reflected in progressive changes on the spontaneous SWO of MSNs as well, impeding the localization of a functional border between DLS and DMS.On the other hand, we showed that visual responses display very different properties in DCS and DMS coordinates , including their onset delays. Despite these significant differences, they were not suitable to discriminate the border between DLS and DMS. This is something that occurs with a large number of results showed in our study; for instance, DLS- and DMS-MSNs differ in their intrinsic electrophysiological properties, direct and indirect pathways properties, sensory responses and highfrequency oscillatory activity. Even these results are essential to describe MSNs properties in both striatal regions, most of them do not contain enough information to discriminate between DLS and DMS circuits, based on our classifier. In contrast, we found that the combination of few Up states features of the SWO are enough to identify between striatal regions with ~90% of accuracy. All of these reinforce the idea of the SWO as a reliable method to study functional connectivity.4) Concerning the measure of the depolarizing/hyperpolarizing ratio (DH ratio), it is not clear how the authors conclude of striatal microcircuits contribution from the recordings in Figure 4 and how it can be isolated from cortical drive from the recordings? If not clearly stated, the reviewer is not sure that this analysis is necessary to support the author's conclusions.Our DH ratio provides information to characterize the Up states transition slopes, one of the Up state features selected to identify between striatal regions. In particular, the divergence in the DH ratio between DLS and DMS-MSNs implies that the balance between depolarizing and hyperpolarizing inputs is different during these transitions. We found that DMS-MSNs have larger ratio of depolarizing events during upward transition and larger proportion of hyperpolarizing events during downward transition with respect to the DLS-MSNs . These results supports our suggestion of the role of striatal interneurons as one of the possible causes underlying the functional segregation of the dorsal striatum. In this direction, it has been shown that the upward and downward transitions slopes of cortical neurons are governing by GABA inhibition .Please, note that this question is also discussed answering reviewer 2.We have now modified the text relative to the DH ratio, in the Materials and methods, Results and Discussion sections, to clarify them.5) The method used to do the delineation of the DCS is not clear and should be better explained in the main text.We thank the reviewer for this assessment. We have extended the section “DLS and DMS are two non-overlapping functional circuits in mouse” in order to clarify and provide further context to the study of the possible boundary between circuits in the DCS coordinates.In brief, we used the SVM that had been used previously to classify these two populations to study the distribution of DCS-MSN in the parameter space relative to the populations of DLS- and DMS-MSNs. The trained SVM contains a hypervector that maximises the separation between the two populations; in other words, it let us project our data into a new axis which has been optimised to distinguish between DLS and DMS MSNs. In this space, DLS- and DMS-MSNs have a known distribution and because of these, we could introduce a new pool of MSNs (recorded in DCS) and compare it with the previous ones. This let understand whether the DCS-MSNs created a continuum between the DLS- and DMS-MSNs populations along the decision axis or whether this separation remained after the addition of the new pool of MSNs, depending on the distribution of the population of DCS-MSNs along this axis.6) Slow wave oscillations are a specific regime within these networks and even though informative, analyzing the networks in that state might be limiting. Mahon et al., 2006, they show a very different activity in the corticostriatal oscillations between SWO and awake animals. The results of the present study should be discussed in light of the different regimes existing in the corticostriatal pathways depending on the vigilance state of the animals.We have extended the Discussion regarding the validity of our study when compared to awake states in the context of Mahon et al., 2006.First, the use of the SWO state let us benefit from a well-known brain state, on which cortically generated slow oscillations will propagate along the cortex and to the striatum, with well-defined spatial-temporal properties. This scenario is ideal to understand cortico-striatal communication, as we can study the propagation of each wave to the striatum as it travels through the cortex. In addition, it let us understand how these oscillations are integrated simultaneously in DLS and DMS thanks to the double patch-clamp recordings. These particular experiments would not have been possible without the mechanical stability that results from an anaesthetised preparation.Second, we have discussed the work by Mahon et al., 2006. The authors described that, similar to what it happens in mice or human cortex, MSNs change from a highly synchronous SWO state during anaesthesia/sleep to an asynchronous state during awake states. Previous works  have shown that the functional constrains that will shape the activity of a common circuits are shared between spontaneous and evoked states. Therefore, our results discussing the differences in the striatal organization provide insights about the functional organization of the striatum during different brain and behavioural states.Related to this, previous works have already demonstrated that the study of the SWO is useful to understand human and mice neurological disorders . The SWO has been considered as the default activity pattern of the cortex and the cortex is the major source of inputs to the striatum. One of the main functions of the slow-wave sleep is to preserve the proper connectivity within the circuits  and has been extensible related with memory consolidation , thus from this point of view, it makes sense to extract information of the circuits based on the analysis of the SWO. In fact, the SWO was previously suggested as a model to study functional connectivity between brain circuits .7) One last suggestion is that direct and indirect pathway data could be presented at the beginning with intrinsic properties and up states characterization of the MSNs in DMS and DLS to avoid coming back to intrinsic properties at the end of the manuscript.We thank reviewer #1 for this suggestion. We have considered the reordering of the narrative following this indication. While the suggested order will unify the discussion about the intrinsic properties between regions and MSN types, we think that the current organization puts the focus on the main point of the paper, which is the differentiation between DLS and DMS. Therefore, we think it is important to maintain the present order, going from the general question to the specific aspects, including subpopulation of neurons as direct or indirect MSNs. But, if the reviewer #1 still considers that this is an essential point to understand the study, we will reorganize our manuscript.Reviewer #2:This interesting study presents a number of analyses of in vivo intracellular recordings of the membrane potential activity of MSNs in the striatum, which indicate that there are differences in electrophysiological properties in dorsomedial vs dorsolateral striatum. Furthermore, there appears to be a discrete demarcation between neurons in each area with only a small overlap and no gradient. The authors then go on to record from afferent areas to determine if the patterns of activity in the MSNs are echoed by differences in neuronal activity in functional areas projecting to the striatum. Finally, they further demarcate the neurons in each area by whether they are dMSNs or iMSNs, and find that there were further differences of neurons in the lateral striatum only.Although I can understand how temporal measures such a durations of states and trajectory of transitions can be quantified using the NA-MEMD algorithm, I cannot see how the actual average and SD of the up and down state membrane potentials have been extracted from the IMFs, when looking at those exemplified in Figure 2—figure supplement 2C and the SWO also shown in Figure 1C. Was there an additional step needed to calculate a probability density function from the distribution of the membrane potentials, as in Figure 5C? If not, because as the authors say it is the first time that the algorithm has been used to analyse membrane oscillations of single neurons, a much more detailed methodological graphical explanation as to how the potentials have been derived is warranted.We thank Dr Reynolds for his supportive comments. We have clarified the use of the NA-MEMD in the new manuscript, as it was sometimes unclear whether a certain analysis had been done using the original raw trace or a certain IMF. The results shown in Figures 1,4 and 7 were computed using the original traces, and the Up states were isolated using a standard moving threshold, as described in the Materials and methods section.The authors found "an Up state in FrA preceding an Up state in V1 in the 71% of times , data not shown" – this is itself very interesting and the data SHOULD be shown as further suggestive (correlative) evidence that the propagation of Up state timing from lateral to medial striatum is driven by frontal to visual cortex transition of slow wave.We thank Dr Reynolds for this suggestion. We have included a new panel (panel F) in Figure 6 to show this data.The observation of a difference between cellular properties and SWO measures in lateral and medial regions is just that, without any expose of the underlying reason. This paper could be much more enriched by some investigation of a mechanism that might explain these differences. In lieu of a full morphological comparison, one possibility the authors do raise that is quite achievable in a relatively short time is considering the effects of differences in dopamine innervation, either directly or indirectly via cholinergic neuronal innervation. Indeed there is known to be medial to lateral gradients of many neuromodulatory components including dopamine receptors which might contribute to the regional differences. The authors should consider undertaking a series of comparison experiments under blockade of D1 and D2 antagonists  and see if this normalises the observed differences between neurons. This is relatively more straightforward than trying to normalise FSI innervation and in my opinion is much more likely to provide at least a partial explanation.We thank Dr Reynolds for his suggestion.We agree with the hypothesis that dopaminergic innervation could play a direct or indirect role on the functional discrimination of the striatum, together with other mechanisms such as the corticostriatal innervation and local inhibition. However, we consider that the impact of DA on the striatal SWO is beyond the scope of this study. Our classifier is created on the base of 197 striatal neurons recorded from the intact circuit in a heathy condition.Dopamine depletion induces changes in the striatal microcircuits but also in the cortical activity. Using in vivo patch-clamp recording in anesthetized mouse, the same technique used in our study, it was shown that the Up state frequency of single cortical neurons recorded in S1 decreases after 6-OHDA lesion . Hence, dopamine depletion will alter the corticostriatal transmission presynaptically, in the cerebral cortex, inducing unspecific changes in addition to the striatal ones, which can mask the mechanism underlying the striatal division. On the other hand, these experiments would only provide a partial explanation. Striatal neurons express different types of DA receptors, in MSNs and in different types of interneurons. In addition, DA receptors are expressed presynaptically at the glutamatergic terminals. Therefore, the absence of DA will not provide the precise mechanism underlying the possible changes on MSNs Up states.In the new version of the manuscript, we have emphasized the study of the properties helping to discern between DLS and DMS. Figure 4 describe the features selected by our classifier that are relevant to discriminate between striatal regions.Because it is known that corticostriatal axons are characterized by some degree of anatomical specificity, our first hypothesis was to explore whether cortical inputs are relevant to discriminate between the DLS and DMS. To answer this hypothesis, whole-cell recordings were performed in cortical neurons from FrA, M1, S1 and V1. We found that the number of peaks in the up states were clearly higher in M1 and S1 as well as in the DLS-MSNs. Thus, the structure of these complex fluctuations of the membrane potential of cortical Up states from M1 and S1 neurons are reproduced in the DLS-MSNs but not in the DMS-MSNs. This result, together with the previous anatomical description, strongly suggest that M1 and S1 inputs are sculpting the Up states of DLS-MSNs, in which the excitatory glutamatergic inputs are directly transmitted to MSNs, while the inhibitory ones must be mediated by one or several types of interneurons. Therefore, we conclude that cortical inputs are important for the functional division between the DLS and DMS.In addition to the result regarding the number of peaks, the information about the Up to Down states slopes between DLS- and DMS-MSNs rises the classification accuracy up to ~90%. To illustrate the divergence of upward and downward transition slopes we have included a new plot . It is known that GABA release modifies Up states slopes in cortical neurons. This effect is mediated by the activation of postsynaptic GABAA  and GABAB receptors  for Down to Up and Up to Down transition slopes respectively. Because MSNs display IPSPs during spontaneous Up states  and it has been described a different number of PV and activity of cholinergic interneurons along dorsal striatum, we discuss that the observed Up states slopes divergence between DLS- and DMS-MSNs can be mediated by differences on inhibition. In order to explore this possibility, we analysed the time course of the depolarized and hyperpolarized events during the Up states , and then we calculated its average ratio . The result shows a higher positive and negative ratio during the Upward and Downward transition slope respectively for DMS-MSNs. This result together with the previous descriptions of interneurons distribution along dorsal striatum suggest that local inhibition contribute to the division of the DLS and DMS circuits.


To illustrate the EC sensing potentiality of the GM, a high-sensitivity glucose detection was conducted on the graphene/Ni hybrid mesh with the longest edge length. At a detection potential of 0.6 V, the edge-rich graphene/Ni hybrid mesh sensor exhibited a wide linear response range from 10.0 μM to 2.5 mM with a limit of detection of 1.8 μM and a high sensitivity of 1118.9 μA∙mM−1∙cm−2. Our findings suggest that edge-rich GMs can be valuable platforms in various graphene applications such as graphene-based EC sensors with controlled and improved performance.The development of graphene structures with controlled edges is greatly desired for understanding heterogeneous electrochemical (EC) transfer and boosting EC applications of graphene-based electrodes. We herein report a facile, scalable, and robust method to produce graphene mesh (GM) electrodes with tailorable edge lengths. Specifically, the GMs were fabricated at 850 °C under a vacuum level of 0.6 Pa using catalytic nickel templates obtained based on a crack lithography. As the edge lengths of the GM electrodes increased from 5.48 to 24.04 m, their electron transfer rates linearly increased from 0.08 to 0.16 cm∙s Graphene possesses unique properties, including excellent transparency, high thermal and electrical conductivity, large specific surface area, and excellent electrochemical (EC) stability which make it one of the most attractive materials ,2,3,4. GOwing to their superior large periphery , graphene mesh (GM) architectures, comprising of interconnected graphene wires, are believed as potent edge-rich graphene electrodes for advanced EC applications. Several methods have been employed to fabricate GM, including electron beam lithography , nanoimp2 templates with various crack densities. Cyclic voltammetry (CV) was employed to assess EC performance of the fabricated GM and basal graphene samples. The results showed that the electron transfer rate of GM electrodes increased with increasing edge length and was remarkably greater than that of its basal graphene counterpart. This validates the important role of the graphene edge effects in controlling and enhancing EC performance of GM electrodes. To shed light on the possible EC applications of the edge-rich GM electrodes, a high-sensitive glucose detection was conducted on the graphene/Ni hybrid mesh with the longest edge length. The edge-rich graphene/Ni hybrid mesh sensor exhibited a wide linear response range from 10.0 μM to 2.5 mM with a high sensitivity of 1118.9 μA∙mM−1∙cm−2 and a limit of detection of 1.8 μM. Our findings suggest that edge-rich GM may be a promising candidate for boosting various graphene applications such as graphene-based EC sensors with controlled and enhanced EC reactivity.In this study, we present a strategy to fabricate scalable GM with controllable edge lengths and investigate their edge length-dependent EC reactivity. Specifically, different edge lengths of interconnected GM samples were obtained by exploiting self-cracking TiO2 suspension was prepared by successively mixing the solution of DI water (0.5 mL), ethanol (2 mL) , acetic acid (4 mL) , and titanium (IV) ethoxide (0.5 mL)  under continuous stirring (300 rpm). After stirring 30 min, different volumes  of erasable ink solution (PILOT P-WMRF8-G) were separately added and continuously stirred for 2–3 h. The density of TiO2 crack was controlled by amount of added ink. Next, the TiO2 suspension (100 µL) was drop-coated onto a 1.5 × 2 cm2 cleaned Si substrate with 300 nm thick of SiO2 layer atop. Note the SiO2 layer is not essential for the formation of TiO2 cracks described in below. After a few minutes, an interconnected self-cracked TiO2 template was obtained during the evaporation of the solvents in air at room temperature due to particle packing and volume shrinkage [2 template by a thermal evaporator at a vacuum level of 2 layer by acetone solution under ultrasonication for 3–5 min.The procedure for fabricating the Ni mesh template was adapted from our preceding work . First, hrinkage ,32. Subs3 (0.1 M/0.1 M) overnight. Finally, a gold contact and a SiO2 protection layers were successively deposited on top of the GM sample except for the center circular region (with 0.7 cm diameter) using a mask by thermal evaporation and E-beam evaporation methods, respectively.The graphene was grown on the Ni mesh template with a solid carbon precursor via a rapid thermal annealing approach as reported in previous works ,34,35,362, Ni mesh templates, planar graphene and GM samples were examined by optical microscopy , atomic force microscopy , and scanning electron microscopy . The crystallographic and layered structures of the GM samples were investigated by Raman spectroscopy  with the excited laser at the wavelength of 638 nm. The energy-dispersive X-ray spectroscopy (EDS) of the field emission scanning electron microscopy  was used to examine the elemental composition of the GM. X-ray photoelectron spectroscopy (XPS)  was used to analyze the surface chemical properties of the graphene samples.The morphologies and dimensions of the self-cracked TiO4NPF6)  and 1.0 mM ferrocene  in acetonitrile was used to determine the electron transfer rate. Glucose (D-(+) glucose, Sigma Aldrich, Missouri, USA) in 0.1 M NaOH was used to determine the EC sensing capability of the graphene/Ni hybrid mesh electrodes. It has been reported that the current of CVs increase with the pH value of the electrolyte [Electrochemical characterization was conducted inside a miniature reactor containing a working electrode (GM electrode), a Pt wire counter electrode, and an Ag/AgCl/KCl (3 M KCl) reference electrode see . All thectrolyte . Since tctrolyte . Prior t2 suspension on a SiO2/Si substrate to form a TiO2 crack template, (2) Ni deposition, (3) lift-off of the TiO2 layer, (4) growing graphene on the Ni mesh template, (5) Ni removal, (6) deposition of gold (Au) contact layer and (7) deposition of SiO2 protection layer  with different surface coverages (SCs) of cracks, respectively.Our fabrication procedure for GM electrode includes (1) drop coating of a TiOon layer . The gol of TiO2 ,32. Prev of TiO2 . Therefo2 template V-50, which clearly illustrates that cracks of the template are well connected to form large amount polygonal cells. A representative crack was characterized by AFM measurement. The width of the crack is around 1.75 μm, as shown in 2 template V-50 (assigned as NM V-50), and it clearly demonstrates that the Ni wires of the sample NM V-50 are highly interconnected to form a replica of the crack network on the TiO2 template V-50. The thickness of all Ni meshes used to synthesize graphene was chosen at 120 nm as indicated by the AFM profile of the NM V-50 shown in 2 templates  are displayed in 2 suspension for the preparation of the TiO2 template, which indicates that SC of Ni wires can be easily controlled by the volume of the added ink solution.2/Si substrate. As revealed from the SEM image  versus SC of graphene wires of each GM, and it clearly shows that ID/IG increases (from 0.31 to 0.88) with SC of graphene wires. It indicates that higher SC of graphene wire can yield more graphene edges.The graphene was directly grown on the Ni mesh template by a rapid thermal annealing of the solid carbon precursor (CA) at low vacuum. During the annealing procedure, dissociated carbon atoms diffused into the Ni template, and graphene precipitated onto the Ni surface during annealing and cooling ,34,35,36mage see a of the 704 cm−1 ,41. The 2 at 284.5 eV, the presence of carbon sp3 (defect peak) at 285.4 eV, and the presence of the C−O−C and O−C=O functional groups at 286.6 eV and 288.9 eV, respectively [2/sp3 of GM decreased from 7.01 to 4.5 for the GM V-5 and 4.07 for the GM V-50, indicating the presence of high edge in GM samples.XPS was used to analyze the surface chemical properties of graphene samples. The C 1s XPS spectra of graphene samples (displayed in ectively . Compare4NPF6 and 1.0 mM ferrocene with different scan rates are displayed in CV curves of graphene-based electrodes measured in acetonitrile solution containing 0.1 M Bupi is the peak current (A), A is the electroactive area of the electrode (cm2), n is the number of electrons participating in the reaction , D is the diffusion coefficient of ferrocene in 0.1 M Bu4NPF6 acetonitrile (2∙s−1 adapted from [C is the concentration of the probe molecule in the solution (mol∙cm−3), and −1). Since GM samples were directly grown on the Ni meshes, we assumed that the average width of each GM sample is close to that of Ni wires of each Ni mesh. We have measured the average widths of Ni wires of all five Ni meshes and found their average widths are very close see . Electroted from ,47), C ik) can be extracted from the scan rate dependence of the CV peak positions [p >E 200/n (mV), where n is the number of electrons per molecule (n = 1 for ferrocene redox mediator), the value of k can be calculated from following equation:V/s), R (= 8.314 J∙mol−1∙K−1) is the ideal gas constant, T is the room temperature (K), F is 96485.333 C∙mol−1, and RT/ and plotted as a function of the edge length in k value of the planar graphene scattered in ferrocene redox mediator (~0.056 cm∙s−1) is close to the results of CVD graphene reported in [k value increases with the edge length. The red line exhibits a linear fitting of k values exhibiting a linear increase of the electron transfer rates from 0.056 to 0.160 cm∙s−1 as the edge length of the GM electrodes increased from 0 to 24.04 m. This result confirms that increasing SC of the GM indeed promotes its EC reactivity because of the increase of graphene edge, where more ions from electrolyte are accumulated [The electron transfer rates  and their oxides are widely used as the metal catalysts due to their high sensitivity, good stability and low-cost ,52. Amon2 . To illuic sweep . With thic sweep . Figure xidation . The reaxidation ,53:(3)Ni2 through the Reaction (5) and that facilitated the reduction process of The increase of current at both anodic and cathodic peaks in the presence of glucose are similar to the results reported in . We prop−1∙cm−2 obtained by dividing the slope of the regression equation by the active area of the electrode [lectrode . The limlectrode  is out olectrode . The typ2 films. The electron transfer rates of the GM samples were evaluated and the result showed an increase in electron transfer rate with increasing graphene edge length. Moreover, we found all the GM electrodes exhibited considerably greater electron transfer rates than that of the basal graphene electrode. This indicates that the abundant graphene edge of the GM electrodes indeed is the main attribution for the enhancement of their EC performance. Furthermore, the sensor made of the graphene/Ni mesh V-50 exhibited a wide linear glucose response range from 10.0 μM to 2.5 mM with a limit of detection of 1.8 μM and a high sensitivity of 1118.9 μA∙mM−1∙cm−2. The edge-rich GM V-50 presented in this work shows a great promise for advancing various graphene applications such as graphene-based EC sensors with controlled performances.In conclusion, we have presented a facile and robust approach to fabricate large-scale and high-quality GM samples with controllable edge lengths by the combination of crack lithography and rapid thermal annealing techniques. The edge lengths of interconnected GM samples were controlled by the crack density of TiO


Transmission electron microscopy and environmental scanning electron microscopy are used as morphological characterization techniques. SnO2-decorated graphene displayed high sensitivity towards benzene, toluene, and xylene with the lowest tested concentrations of 2 ppm, 1.5 ppm, and 0.2 ppm, respectively. In addition, we found that, by employing these nanomaterials, the observed response could provide a unique double signal confirmation to identify the presence of benzene vapors for monitoring occupational exposure in the textiles, painting, and adhesives industries or in fuel stations.Benzene, toluene, and xylene, commonly known as BTX, are hazardous aromatic organic vapors with high toxicity towards living organisms. Many techniques are being developed to provide the community with portable, cost effective, and high performance BTX sensing devices in order to effectively monitor the quality of air. In this paper, we study the effect of decorating graphene with tin oxide (SnO Industrial and human developments have been steadily increasing the production and the emission towards the environment of toxic and hazardous gases. Among the various types of pollutants, aromatic volatile organic compound vapors like benzene, toluene, and xylenes are widely used in numerous fields, most notably by the petrochemical and textile industries. According to the World Health Organization (WHO), a long-duration exposure to such compounds, especially to benzene, leads to life-threatening diseases . As thosOptical based sensors, multisensor arrays, and chemoresistive sensors are among the most studied approaches for the detection of volatile organic compounds (VOCs) in general, as well as benzene, toluene, and xylene (BTX) in particular ,7,8. TheChemoresistive devices detect and identify gases by converting a gas volume fraction into an analog electrical resistance change, resulting from the charge transfer between the surface of the sensing film and the adsorbed gas molecules .Nanomaterials, such as metal oxides, carbon nanotubes along with graphene and its derivatives, are among the most studied materials for the development of chemoresistive gas sensors ,12,13.Graphene was discovered by Novoselov and Geim  and overTo overcome these, the decoration of graphene with metal or metal oxide nanoparticles or by grafting functional groups to its surface has been explored in order to improve the performance of graphene gas sensors .Moreover, other researchers worked on the improvement of gas sensors performance such as selectivity and sensitivity with the possibility of small scale integration ,25,26,27Chemoresistive gas sensors for detecting BTX aromatic volatile compounds  are challenging devices due to the relatively low chemical reactivity of BTX vapors . In this2/g. Commercially available nanopowders of tungsten oxide (WO3) and tin oxide (SnO2) were purchased from Sigma Aldrich . The carrier gas was nitrogen U (N2 purer than 99.995%) and was bought from Air Liquide . All these products were of analytical grade and used without further purification.The graphene used was purchased from Strem Chemicals Inc. , with reference no. 06-0235. This material consists of graphene nanoplatelets with an average thickness of 5–10 nanometers (3.35A between sheets) and sub-micron size (a few hundreds of nanometers). In addition, its surface area is 750 m2) and tungsten (WO3), were prepared (0.5 mg of SnO2 or WO3 nanoparticles were dispersed in 10 mL of ethanol). Subsequently, the suspension with the de-agglomerated graphene was placed on a hot plate while agitated vigorously at 50° and then a metal oxide NP suspension was added drop-by-drop. The stirring at 50° was kept up to half an hour after the last drop of the NP suspension was incorporated. This was done to favor the homogeneous attachment of the metal oxide nanoparticles onto the graphene surface. The loading with metal oxide NPs of graphene was adjusted to 5% in weight. In the final step, the dissolution of graphene decorated with metal oxide nanoparticles was deposited on the interdigitated gold electrodes  by the airbrushing technique at a temperature between 80 °C and 90 °C degrees . Then the suspension was ultrasonicated  for one hour in order to have a homogenous dispersion of the graphene platelets in the ethanol solution, avoiding the agglomeration of graphene. In the second step, other independent dispersions in ethanol with metal oxide nanoparticles, tin oxide  and an Environmental Scanning Electron Microscope (ESEM). The TEM morphology investigation was undertaken with a JEOL JEM 2100F  TEM, at 200 kV operating voltage. The Environmental Scanning Electron Microscope was realized by Carl Zeiss AG—ULTRA 55  ESEM.2/graphene, and WO3/graphene sensors were tested for the detection of BTX VOCs. The sensor chamber (35 cm3 in volume) can house up to six sensors at a time and measurements were performed on two replicate devices for the three different sensitive nanomaterials studied, thus ensuring that the differences in response recorded were consistent. The vapors were produced by a dilution bench consisting of a chemical solvent vaporization cell and two flowmeters (with a flow rate of 200 sccm). Sensor resistance was measured via an Agilent HP 34972A Multimeter  at a fixed operating frequency of 50 Hz. Due to the system configuration, the lowest achievable vapor concentrations were 2, 0.4, and 0.2 ppm for benzene, toluene, and xylene, respectively.The graphene, SnO0 and Rg are the resistance under the carrier gas and the aromatic VOC, respectively.The target vapors were injected in the sensor chamber using nitrogen as carrier gas. The response of the sensors was calculated according to the normalized resistance variation, presented in Equation (1).A transmission electron microscopy (TEM) analysis, performed in order to characterize the pristine graphene layers, is displayed in 2/g) for its decoration with metal oxide nanoparticles and interaction with the chemical environment. Additionally, graphene has a small content of oxygen functional groups (less than 8% wt.) derived from their synthesis. Nevertheless, these oxygen groups can easily interact with metal oxide nanoparticles, enhancing the decoration of graphene.3 and SnO2. 3 and SnO2 are observed. In 3 and SnO2 metal oxide nanoparticles on the graphene surface.Additionally, ESEM images were obtained for pristine and graphene decorated with WO2/graphene, and WO3/graphene were examined for aromatic vapors: benzene , toluene , and o-xylene . The vapor concentration was controlled by changing the mixing ratio of nitrogen and nitrogen balanced analyte vapor using mass flow controllers.The sensing performances of the pristine graphene, SnOThe vapors sensing performances at room temperature of the pristine graphene-based sensor towards the aromatic vapors benzene, toluene, and xylene are presented in The observed response of the graphene-based sensor shows an increase of the sensor resistance with the increase in the injected vapor concentration either for benzene, toluene, or xylene. A good repeatability was observed b,c at roA baseline drift was observed for the higher concentration tested. Due to the highly stable and reversible responses recorded for the lowest concentrations tested, this effect can be attributed to VOC molecules remaining adsorbed at the graphene surface at the end of a cleaning phase, preventing a complete baseline recovery. In fact, a short heating pulse of a few seconds at 70 °C is enough for completely desorbing all vapor molecules and fully regaining the baseline  than the one recorded with bare graphene. This implies that the limit of detection for VOCs in tungsten oxide-decorated graphene sensors is better  than in bare graphene sensors.The observed response of the WOobserved c at 250 2/graphene-based sensor operated at 250 °C towards the vapors of benzene, toluene, and xylene are presented in The responses of the SnO2/graphene-based sensor shows the same p-type behavior already observed in bare and tungsten oxide-decorated graphene. Sensor resistance increases for increasing concentrations of benzene. However, for toluene and xylene vapors, the sensor behaves as an n-type semiconductor, because its resistance clearly decreases for increasing concentrations of these species. Such transitions from p-type to n-type materials have been reported before in graphene nanomaterials and attributed to adsorbate induced charge transfer and charge carrier scattering effects [2/graphene-based sensors, while this effect is not seen for benzene (DM = 0), it is observed for toluene and o-xylene, which have increasing DM. For ethanol vapors, a molecule with even higher DM, this response inversion effect appears not only in SnO2/graphene but also in WO3/graphene operated at 300 °C . The lower detection limit was estimated in relation to the observed response noise of 0.3% at 250 °C. A good repeatability was observed  than in tungsten WO3/graphene and bare graphene sensors.The calibration curves presented in rved see c at 250 3/graphene, and SnO2/graphene sensors are presented in To calculate the sensitivities of the fabricated sensors to all the tested vapors, the calibration curves were fitted using a linear regression model. The sensitivity  for pristine graphene, WO2/graphene sensor for benzene, toluene, and xylene vapors. These differences can be related to the different dipolar moments of the aromatic VOCs tested and the different activation energy of the gas-sensitive materials. The higher sensitivity for tin oxide-decorated graphene could also be due to a better matching between the work function of graphene (near 4.6 eV) and that of tin oxide (near 4.5 eV) as compared to tungsten oxide (near 5 eV) [The best performance is observed for the SnOar 5 eV) ,52,53. Rar 5 eV) ,42,43,54It is worth mentioning that the loading level of metal oxide nanoparticles on graphene could be optimized further in order to possibly achieve an even higher sensitivity to aromatic VOCs. This would involve checking different loading levels in a narrow range near the 5% level used here. Loading levels below 1% do not significantly alter the properties of the bare material and loading levels higher than 10% result in the agglomeration of metal oxide nanoparticles, inhomogeneous distribution of these on the graphene surface, and loss of sensitivity.2/graphene, and WO3/graphene sensors toward aromatic vapors are presented in 2/graphene sensor towards toluene and xylene, roughly half a minute. These response times compare favorably to the reported results for graphene sensors [The response and recovery times of graphene, SnO sensors . For benIt is well known that the exposure to high concentrations of BTX vapors, commonly used as organic solvents in the industrial production of paints, lacquers, adhesives, and detergents may cause serious carcinogenic effects. The monitoring of the occupational exposure to these compounds is thus of great importance for the preservation of workers’ health. Currently, the eight-hour TVA level for benzene as indicated by OSHA is 1 ppm (the TVA levels for toluene and xylene are higher) . The met3/graphene sensors together with an n-type response from the SnO2/graphene sensor would be indicative of the presence of toluene or xylene. Finally, a response from the MHDA-Au-MWCNT sensor would indicate the presence of a non-aromatic VOC such as methanol, ethanol, or acetone.In a previous research , we show2) and tungsten oxide (WO3) nanoparticles decorating graphene on the performance of the resulting hybrid nanomaterials as chemiresistive sensors for detecting BTX vapors. The decoration of few layer graphene flakes resulted in high sensitivity towards benzene, toluene, and xylene vapors. The sensors could be suitable for the inexpensive monitoring occupational exposure to BTX. In a future research, the materials developed could be used for integrating a four-element sensor array on a single-chip MEMS configuration, and thus could provide a unique double signal confirmation response associated with the presence of highly toxic benzene vapors (as described in the discussion section). The fabrication of this single-chip sensor, the fine-tuning of the loading level of graphene with metal oxide nanoparticles to maximize the response to aromatic VOCs, and the study of the effect of ambient moisture will be the subject of our next research.In this paper, we studied the effect of tin oxide (SnO