Deferral rates are also higher in HPs with a lower activity compared to HPs who interview more donors . Furthermore, statistically differences are observed between the type of blood donation venue in terms of the global deferral rate , and specific reasons for deferral . Providing the HPs with feedback on these findings was an opportunity to draw their attention to some aspects of the selection process in order to improve it.Blood donor selection is a cornerstone for blood transfusion safety, designed to safeguard the health of both donors and recipients. In the Service du Sang, Belgian Red Cross, French and German-speaking part of Belgium (SFS), health professionals (HPs) are allowed to interview donors on their own after formal qualification. This qualification is afterward evaluated by means of two complementary quality indicators: monitoring of donor health questionnaires (DHQs) and analysis of donor deferral rate. The study aims to evaluate the degree to which both quality indicators may be useful and appropriate tools to evaluate the quality of blood donor selection. An analysis performed on 2016 data showed that noncompliance detected by means of DHQ monitoring seems to be more frequent in HPs who conduct a low number of interviews compared to all HPs as a group (5.67 vs. 3.23%;  Blood donor selection is a cornerstone for blood transfusion safety, designed to safeguard the health of both donors and recipients. Donor safety , 2 is taThe European Directive 2005/62/EC  and the potential risks of donation.Donor Health Questionnaire (DHQ): filled out by the donor alone (before the pre-donation interview) or with the HP (during the pre-donation interview).Donor interview: conducted by a qualified HP.Donor health assessment: at the end of the interview, the donor is declared eligible to give blood or deferred temporarily or permanently; this decision also takes into account physical and biological parameters, such as hemoglobin level, blood pressure, heart rate, and weight.The blood donor selection process usually encompasses four main steps :Pre-donBefore an HP is allowed to conduct donor interviews and selection alone, qualification must be acquired.via e-learning) focusing on blood donor selection criteria: a predefined grade must be achieved in order to continue the training,Theoretical training is provided  (see below).In the Service du Sang, Belgian Red Cross, French and German-speaking part of Belgium (SFS), the following qualification criteria are used and as recommended in terms of training objectives , communiEach step must be successfully completed before the next step is taken. When all steps are completed, the HP is qualified for donor selection.After this initial qualification, HPs are allowed to interview donors alone, but their qualification is not infinite. It is nevertheless difficult to evaluate the quality of this activity. As confidentiality is a prerequisite for the pre-donation interview, regular attendance by a third person (supervisor) is not recommended even with a view to evaluating the competency of each HP. Furthermore, the presence of a third person could influence the behavior of both the donor, who might hesitate to disclose all the relevant data  and A sample of DHQs filled in by donors and analyzed by the HPs during the pre-donation interview are monitored yearly for each HP in order to assess whether the HP in question collected the relevant data and used them properly.The donor deferral rate observed for each individual HP is compared to the overall rate for all HPs within the SFS in order to detect differences that might reflect different ways of selecting donors.Blood donor selection by qualified HPs can be evaluated in various ways. For example, the quality of the decision taken by a trainee  can be assessed by comparison with the decision taken by a senior physician, the latter being considered as the reference . In the Both quality indicators are used routinely by the SFS. The study aims to analyze their adequacy to evaluate the quality of blood donor selection and to possibly improve blood transfusion safety.Results of both indicators are provided to each individual HP to improve their blood donor selection, and, as a consequence, to improve blood transfusion safety.Evaluations performed on blood donor selections, which took place in 2016 in the SFS are analyzed and the results reported in the study that provides some examples of issues detected and thoughts about the further development of these tools.The study was conducted as an audit of blood donor medical selection analyzing two indicators: DHQs monitoring and blood donor deferral rate. This audit was submitted to the Erasme-ULB Ethics Committee , which determined that full review and approval was not required.® software, MAK systems, Paris, France) and the decision taken are checked for a series of DHQs, typically all the DHQs for one blood collection session for this HP.In the SFS, each HP ideally undergoes one annual monitoring by a senior HP (supervisor). The paper version of the DHQ filled in by the donor and checked by the HP with the donor during the face-to-face interview, the data entry in the data-base , reason for deferral (when the donor is deferred), and type of blood donation venue  were analyzed.Donor signatureHP signatureDonor’s “Yes” answers documentedAnswers to all questionsCoherent answer on DHQ and entry into data-baseDonor selection.In order to evaluate HPs’ compliance in donor selection, the following items are checked, according to the DHQ used by the SFS. These items are listed in a standardized questionnaire control form (QCF) used by supervisors in order to document the control:Data from all QCFs collected in 2016 were recorded in an Excel data-base and analyzed by descriptive statistics , including the identification of HPs, the blood donation venue, the category of donor (first-time vs. regular or repeat donor), and the six types of noncompliance listed above.Blood donor deferral rates were calculated globally and for each specific deferral category for the whole group of HPs, for HPs who conducted fewer than 500 interviews during the research period and for those who conducted 500 interviews or more. Deferral rates in the various blood donation venues were compared. The statistical analysis was performed by using the chi square test in order to evaluate the degree to which observed differences were statistically significant.During the study period, 209,617 pre-donation interviews were conducted by 125 HPs  were assessed with a total of 2,063 DHQs. More HPs may have been monitored but were not included in the study because the QCFs were not provided on time.Deferral rates were calculated and analyzed for all donations.This study reports data from 2,063 DHQ checks carried out within the SFS, i.e., 0.98% of all DHQs for 2016.Table Noncompliances for the various parameters checked by means of the QCF, as described above, are displayed in absolute numbers and in percentages of the total number of DHQs checked for all HPs as a group. The distribution according to median and P10, 75 and 90 shows great inter-individual variability.N = 47), i.e., a selection of HPs who performed donor selection most regularly.Table For the whole group of HPs Table , 5.67% oThe most frequent noncompliance issue was a missing answer, i.e., no box ticked by the donor and no comment provided by the HCP: 3.01% of the checked DHQs.The second most frequent noncompliance was missing HP signature (2.76%).The least frequently reported noncompliance was missing donor signature: 5 times out of 2,063, i.e., 0.24%.Overall, the quality of the decision  was considered as being wrong in 0.92% of the cases.The HP subgroup who interviewed most donors on an annual basis (more than 500) had lower percentages of noncompliance than the whole group for all parameters checked Table .A great variability was observed for the various criteria in both the whole group of HPs and in the group of HPs who interviewed more than 500 donors during the study period.A total of 13.09% of the 209,617 donors interviewed by the 125 HPs were deferred for blood donation Table .The deferral reason was recorded by the HPs in the data-base during the pre-donation interview for 85.12% of donors.Exposure to potential transfusion-transmitted infectious agents within the deferral period, due to surgery, endoscopy, tattoo, piercing, potentially contaminating accident, acupuncture, mesotherapy, or blood transfusion: 1.60% .Traveling in a region where transfusion-transmitted infectious agents are present, mainly malaria, Chagas disease, and West Nile Virus infection: 1.57%.Sexual risk behavior, i.e., new sexual partner within the deferral period, men having sex with men, or sexual partner infected with HIV, HBV, or HCV: 1.09%.The main reasons were:Other deferral reasons, such as current infection or serious medical condition were less frequent.Quite a number of donors (4.5%) were not allowed to give blood, but blood analyses were performed in order to check previous results. The main reason is probably low hemoglobin level, but this information is not available for analysis.Table In HPs who interview more donors (at least 500/year) overall deferral rates were lower and, more interestingly, variability tended to decrease, particularly for the global deferral rate (SD: 9.84% compared to 3.28%).Furthermore, statistically significant differences are observed between the type of blood donation venue, i.e., fixed or the various types of mobile sites Table . For insData for each individual HP are compared with those of the group as a whole and to those of HPs working in the same type of venue as the monitored HPs. This comparison allowed to identify differences that were discussed with the HP, an opportunity to assess their blood donor selection activity and, when needed, to provide additional training.The analysis of the data suggests that checking DHQs and analyzing blood donor deferral rate on an individual basis may be used as quality indicators of blood donor selection and as a basis for improvement of this activity.The completion of DHQs is a useful tool to provide HPs with adequate data to take the right decision. Using a DHQ reduces the risk of transmission of blood-borne infectious agents by transfusion. A study showed a significant reduction in Gabonese seropositive donors  who completed a DHQ , althougOne limitation of the DHQ is its understanding by all donors; its form has to be regularly analyzed and reviewed , 14, in Another limitation of the DHQ is the noncompliance on the part of some donors when it comes to responding properly and honestly to all questions. To improve donors satisfaction and probably to make donors more compliant when it comes to filling out the DHQ honestly, an abbreviated questionnaire may be an alternative at least for repeat and regular donors .A standardized DHQ is used by most blood services to collect relevant data in order to evaluate the donor’s eligibility to give blood. Each blood service uses its own standardized DHQ. Exceptions are the USA and Canada, where uniform DHQs have been used since 2005. In Germany, a national DHQ has been recently tested .An automated computer-assisted pre-donation interview could improve the collection of relevant data , 18 but These critical findings highlight the importance of analyzing DHQs as a means to evaluate the quality of the blood donor selection, confidentiality required by the donor interview conflicting with the attendance of a supervisor.In this study, monitoring DHQs by means of the QCF allowed to identify a number of issues which otherwise would have gone unnoticed and adapted feedback was provided to the HPs concerned.Some issues occured only once, as for any human activity: one HP for example, forgot to make sure that an answer was given to one question in the set of DHQs checked. This HP was then reminded of the importance of staying focused. If several questions remained unanswered, the HP received different feedback and was possibly monitored for a certain period until they improved.An issue such as a missing HP signature is major, but was mostly due to simple forgetfulness on the part of the HP who made an adequate donor selection. In fact, as HPs are asked to comment/sign off on all “yes” answers that might be a reason for deferral, tracking their comments made it possible to confirm whether the DHQ has been checked during donor selection. Missing signatures could, therefore, be noticed by the supervisor in most cases.The same type of noncompliance issues detected during the DHQ checks may have a very different impact on transfusion safety. The impact may be possible transmission of a TTI, a health consequence for the donor, or rather noncompliance with the legal framework which may have no immediate consequences for safety in a specific case, but should of course be avoided.Therefore, it is important that the supervisor who performs the checks have thorough knowledge of the consequences of all issues, and give appropriate feedback to the HP involved.A not documented “yes” answer or no answer to the question:When the question involves antecedents of allergy or asthma, the consequence is rather minor, whereas when it concerns contact with a person suffering from hepatitis or another infectious disease, this may have major consequences. In fact, the HP may have missed a reason for deferral or for requesting additional laboratory analyses, with potential transmission of a TTI.Discrepancies between DHQ completion and data entry:For instance, it is more important to enter the actual “yes” answer in the data-base for the question “Have you ever had a malaria attack?” in the case of a first-time donor than for “Have you ever been operated on since you were born?” Indeed, a positive answer to antecedents of malaria automatically generates an analysis request and possibly deferral of the donor. Therefore, if the appropriate answer is not entered, the opportunity for an analysis request and/or donor deferral will be missed.Donor selection:Missing deferral of a donor who is at risk of having been infected with a blood-borne agent, such as a donor who has a new sexual partner within the deferral period, can have a safety impact. On the other hand, accepting a donor whose hemoglobin level is 0.1 g/dL below the legal threshold for giving blood is wrong, but has no safety consequences for either the recipient or the donor.The supervisor adapts their evaluation of the HP when performing DHQ controls accordingly. The examples below illustrate this individualized approach:The least frequent noncompliance issue observed in this study, the missing donor signature, is a major one as without this confirmation from the donor, the blood bag cannot be used.Sometimes systematic or very frequent issues were detected in some or all of the HPs in a group, or in an individual HP. For the supervisor, this was an opportunity to remind the HPs of the rules to be followed in order to improve blood collection quality and safety. In some cases, the issue was a subject for a continued education session.full first 5 years. Therefore, if the donor has only lived in this region for 1 year, the answer entered was “no,” resulting in the malaria analysis request not being automatically generated on the first blood donation. Not testing for malaria could miss a reason for deferral and transmission of malaria. How to answer this question correctly was explained to the HP group twice by the supervisor, first by email when it became obvious that this was a recurrent error, and then included in the next continued education session. After these two steps, there was a marked improvement.The majority of HPs in a group understood the question involving a “donor born in a malaria region, who has lived in this region for the 5 first years of their life?” as living in a malaria region for the One HP did not check properly in which countries Chagas disease is present and forgot to record the relevant journey. As a result, the analysis request was not generated.Examples:The reminder to this HP to check whether a country is affected by Chagas disease had an obvious impact on their data entry.In some situations, incorrect documentation has no impact on blood quality or safety, but can give the wrong impression of noncompliance. This may induce the presence of findings during an audit or inspection.post hoc administrative check would identify the duration of the deferral as being too short and consider it to be a failure to comply with legal requirements. The HP was thus reminded to enter the actual start date and subsequent checks confirmed that this was being done correctly.For instance, one HP always entered the date of the interview as the start date for a temporary contra-indication  instead of the actual date; the end date, however, was correctly calculated. The donor was thus deferred for the correct duration and there was no safety issue whatsoever. However, a p < 0.001). This information is interesting and should be analyzed further in order to assess whether there is a threshold in the number of interviews to be conducted over a specific period.Noncompliance seems to be more frequent in HPs who conduct fewer interviews (5.67 vs. 3.23%; This finding is also of use for adapting the frequency of DHQ controls to the number of interviews performed, HPs with a high number of interviews needing less frequent controls than those with a low number of interviews.The variability between all of the HPs as a group and those who interviewed more than 500 donors during the study period encourages individual analysis of the data and the use of the analysis to set up specific strategies for each HP. Even if an HP is well-graded they can potentially improve their competency toward a better grade and continue to reduce the transfusion risk, even it will never be zero.Analysis of deferral rates is another efficient way of evaluating HP donor interviews and provides a different type of information than DHQ monitoring.Deferral affects the supply of blood components because a blood bag is not collected and because the return rate for a further donation is reduced , 20, in The comparison of individual deferral rates of an HP with the whole group allowed to identify discrepancies that were discussed with the monitored HP.An HP had a much higher overall deferral rate than the group as a whole. Discussion with the supervisor revealed that this HP was very anxious about making mistakes, and preferred to defer donors if there was any doubt whatsoever.An HP had a much lower deferral rate for new sexual partners than the group as a whole. It appeared that this HP did not feel comfortable asking the question on this topic and relied entirely on the answer provided by the donor on the DHQ. Nevertheless, it is a well-known fact that quite a number of donors misunderstand or ignore the question and answer “no,” when in fact they should answer “yes.” Therefore, asking the question orally is important. Detection of this deviation was an opportunity to discuss this particular topic with the HP and provide additional training.Examples:These two issues would not have been detected by means of DHQ monitoring; they illustrate the complementarity of the two blood donor selection evaluation tools used by the SFS.These great inter-individual differences emphasize the importance of providing each HP with personal data in order to give individual advice and plan specific training.It is important to take into account the various parameters, which may have an effect, such as the type of blood donation venue Table . For insNo hard data are available about the impact of the feedback provided by the supervisor to the HPs, but clear improvements were reported. An objective analysis of this impact may be valuable to validate the monitoring tools.ad hoc training and has been qualified accordingly. The quality of the selection process is difficult to assess, but is important in the context of blood transfusion safety.In the SFS as in most blood transfusion centers, donor selection is the result of a pre-donation interview performed confidentially by an HP who has had full post hoc control of the DHQs and analysis and comparison of deferral rates. Even if the number of DHQs checked was low compared to the total amount of donors selected, it made it possible to identify a number of mistakes made in donor selection, both at individual HP level as well as in a group of HPs. Deferral rates analyses also pinpointed the difficulties experienced by HPs in specific selection situations.Having a supervisor present during the interview introduces a bias in itself, as both the donor and the HP may act differently compared to a plain face-to-face interview. Therefore, two complementary methods have been developed in the SFS to assess the quality of the selection process, i.e., Providing the HPs with feedback on these findings is an opportunity to talk to them and draw their attention to some aspects of the selection process in order to improve it. In certain situations, the topic was included in a continued education session. Clear improvements were reported when further controls and/or deferral analyses were performed.A comparison of the results for HPs according to the number of interviews conducted on an annual basis showed that for both the control of DHQs and the deferral rates, HPs with a greater number of interviews made fewer mistakes.In conclusion, controls of DHQs and analyses of donor deferral rate seem to be useful tools to improve the quality of donor selection. It may be interesting to assess whether there is a threshold number of interviews, a HP should conduct per year in order to achieve optimal donor selection quality.PG collected data recorded by HPs and performed a preliminary analysis of these data in order to develop figures and tables. EN and PG enhanced a deeper analysis of the data, and EN exploited the data with a view to planning individual and overall actions. EN and PG wrote the article.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The reviewer PT and the handling editor declared their shared affiliation.


The striatum integrates excitatory inputs from the cortex and the thalamus to control diverse functions. Although the striatum is thought to consist of sensorimotor, associative and limbic domains, their precise demarcations and whether additional functional subdivisions exist remain unclear. How striatal inputs are differentially segregated into each domain is also poorly understood. This study presents a comprehensive map of the excitatory inputs to the mouse striatum. The input patterns reveal boundaries between the known striatal domains. The most posterior striatum likely represents the 4th functional subdivision, and the dorsomedial striatum integrates highly heterogeneous, multimodal inputs. The complete thalamo-cortico-striatal loop is also presented, which reveals that the thalamic subregions innervated by the basal ganglia preferentially interconnect with motor-related cortical areas. Optogenetic experiments show the subregion-specific heterogeneity in the synaptic properties of striatal inputs from both the cortex and the thalamus. This projectome will guide functional studies investigating diverse striatal functions.DOI:http://dx.doi.org/10.7554/eLife.19103.001 To fully understand how the brain works, we need to understand how different brain structures are organized and how information flows between these structures. For example, the cortex and thalamus communicate with another structure known as the basal ganglia, which is essential for controlling voluntary movement, emotions and reward behaviour in humans and other mammals. Information from the cortex and the thalamus enters the basal ganglia at an area called the striatum. This area is further divided into smaller functional regions known as domains that sort sensorimotor, emotion and executive information into the basal ganglia to control different types of behaviour. Three such domains have been identified in the striatum of mice. However, the boundaries between these domains are vague and it is not clear whether any other domains exist or if the domains can actually be divided into even smaller areas with more precise roles.Information entering the striatum from other parts of the brain can either stimulate activity in the striatum (known as an “excitatory input”) or alter existing excitatory inputs. Now, Hunnicutt et al. present the first comprehensive map of excitatory inputs into the striatum of mice. The experiments show that while many of the excitatory inputs flowing into the striatum from the cortex and thalamus are sorted into the three known domains, a unique combination of the excitatory inputs are sorted into a new domain instead. One of the original three domains of the striatum is known to relay information related to associative learning, for example, linking an emotion to a person or place. Hunnicutt et al. show that this domain has a more complex architecture than the other domains, being made up of many distinct areas. This complexity may help it to process the various types of information required to make such associations.The findings of Hunnicutt et al. provide a framework for understanding how the striatum works in healthy and diseased brains. Since faulty information processing in the striatum is a direct cause of Parkinson’s disease, Huntington’s disease and other neurological disorders in humans, this framework may aid the development of new treatments for these disorders.DOI:http://dx.doi.org/10.7554/eLife.19103.002 The basal ganglia play an essential role in movement control and action selection . Their pNeuronal circuits within large brain structures, such as the cortex and the thalamus, are organized around functional subregions. For example, the cortex contains many distinct functional areas, including the sensory subregions, which are defined by specific sensory inputs, and the motor subregions, which are defined by intracortical microstimulation . The thaAlthough the striatum lacks accepted domain demarcations, it is known to have stereotypic excitatory input patterns . For exaThe lack of systematic anatomical maps of corticostriatal and thalamostriatal inputs has stymied efforts to dissect the cortico-thalamo-striatal triangular circuits. For example, recent functional studies suggest that corticostriatal and thalamostriatal axons differ in their release probability and plasticity properties , but theHere, we provide a quantitative and comprehensive description of cortical and thalamic inputs to the mouse striatum. This is achieved by integrating an in-house comprehensive thalamic anterograde projection dataset  and a sehttp://connectivity.brain-map.org)   and idenmap.org) .10.7554/The localized dataset used in the current study includes a median of seven injections per subregion for 15 cortical subregions , were acIn contrast to the cortical subregions, certain thalamic nuclei are smaller than the typical size of a viral injection  and manyCorticostriatal projections are known to have two functionally distinct types of innervation patterns: a core projection field of densely packed terminals and a larger diffuse (i.e. sparse) projection field . To examFor each cortical subregion, a maximum projection density map throughout the striatum was determined by combining projection distributions from all injections within a given cortical subregion . Each prNext, to reveal whether information from different cortical subregions may interact in the striatum, we calculated pairwise projection convergence between cortical subregions for diffuse and dense projections . As expeTo localize the projection distribution for each thalamic injection, we developed a semi-automated image segmentation method to identify axonal projections in the striatum . SimilarWe further determined the thalamic nuclear origins of the thalamostriatal projections by overlaying confidence maps with the two widely used mouse atlases (the AMBA and the Paxinos Mouse Brain Atlas (PMBA) ). The coTo determine if and how different portions of the striatum exhibit heterogeneity in the excitatory inputs they receive, the dense and diffuse corticostriatal projections . These corticothalamic data were integrated into our analysis , ventromedial nucleus (VM), and Pf , which aTaking advantage of the extensive cortico-thalamo-basal ganglia circuit data described above, we examined whether the information flow is segregated with respect to the four major striatal subdivisions described in The above information allows us to further investigate the cortical and thalamic connections with respect to each striatal subdivision . The thaNext, the thalamic relationships with the striatal subdivisions in the cortico-thalamo-basal ganglia circuit were examined . The thaThe striatal subdivisions described here were defined by their excitatory input patterns, leading us to investigate the functional differences between individual cortical and thalamic inputs to the striatum. Guided by our comprehensive striatal input maps, we examined functional properties of inputs to the dorsomedial (DM) striatal subdivision . In addiRecent studies have identified functional differences between corticostriatal and thalamostriatal inputs with respect to their synaptic properties . HoweverBy using a paired-pulse ratio (PPR) experiment to examine the presynaptic release probability, we found that paired-pulse photostimulation of Thal1 axons resulted in facilitation of synaptic transmission onto MSNs, whereas Thal2 axons showed synaptic depression . ConsistTo our knowledge, the present study provides the first comprehensive excitatory input map of the mouse striatum. Given the broad roles of the striatum in action selection, motor execution, and reward, understanding how individual inputs precisely project to the striatum and how such inputs may interact with one another is a step forward in dissecting the circuit mechanisms underlying striatal function.An unbiased cluster analysis of the corticostriatal input patterns reveals that the striatum can be divided into four large subdivisions with clear boundaries . Three oAn orthogonal approach to spatially subdividing the striatum involves the separation of the patch and the matrix compartments via neurochemical markers . These sThe work presented here was achieved by integrating two large-scale viral-tracing datasets and vigorous data analyses. Recent technical advances have made it possible to systematically generate whole brain projection data at mesoscopic resolution . HoweverAlthough most current efforts at mesoscopic circuit mapping focus on illustrating the connections between two macroscopic brain regions , informaRegarding the corticostriatal inputs, the results of the present study are largely consistent with related literature in rat and primate, although several sources could potentially contribute to any discrepancy in isolated cases. First, the relative small size of mouse brain allows the systematic tracing coverage of all cortical subregions and >93% volume of the thalamus with individual injections of small (500–600 µm) sizes, and the imaging of the entire projections of every injection. The level of completeness has not been previously achieved in any mammalian species. The comprehensiveness of the datasets allows us to perform quantitative analyses that are difficult with a few example images. On the other hand, because of the relative small size of mouse brain, and the lack of anatomical landmarks for demarcating certain subregions, accurately assigning cortical subregions can be challenging . For coRecent work from Hintiryan and colleagues uses an anterograde tracing dataset from cortical injections to illustrate the corticostriatal circuits and demonstrate the usefulness of large scale mesoscopic projection mapping to study ‘circuitry-specific connectopathies’ . AlthougThe anatomical axonal projection map suggests, but does not guarantee, synaptic connections , especihttps://github.com/BJHunnicutt/anatomy.All animal experiments were conducted according to National Institutes of Health guidelines for animal research and were approved by the Institutional Animal Care and Use Committee (IACUC protocol number: IS00003542). All mice were housed in a vivarium with 12 hr light/dark cycle (lights on at six am). All calculations were performed in MATLAB (MathWorks). Related custom software is available at Github: Thalamic injection and imaging data were generated as described previously . In briehttp://connectivity.brain-map.org/) (Research Resource Identifier (RRID): SCR_008848)  . The dat_008848) . Both maSpecifically, at the time our analyses were performed, the AIBS Mouse Brain Connectivity Atlas contained 1029 cerebral cortex injections  which saFor hippocampal areas, while some injections included in this dataset had CA1 or CA3 as a primary target, only injections that at least partially covered the subiculum sent projections to the striatum (data not shown). For amygdalar areas, the primary volumes of the amygdala injections in this dataset are in the basolateral amygdalar nucleus (BLA), and basomedial amygdalar nucleus (BMA), but they also cover parts of the central nucleus of amygdala (CEA), posterior amygdalar nucleus (PA), medial amygdalar nucleus (MEA), and piriform-amygdalar area (PAA), areas which span both pallial and subpallial parts of the amygdaloid complex .The raw data processing methods used to generate the voxelized corticostriatal projection data and AIBS averaged template brain were described previously .3, 25 D-glucose, 2.5 KCl, 1 MgCl2, 2 CaCl2, and 1.25 NaH2PO4, pH 7.25–7.35, ~310 mOsm, and bubbled with 95% O2/5% CO2. The brain was removed and placed into ice-cold cutting solution containing (in mM): 110 choline chloride, 25 NaHCO3, 25 D-glucose, 11.5 sodium ascorbate, 7 MgCl2, 3 sodium pyruvate, 2.5 KCl, 1.25 NaH2PO4, and 0.5 CaCl2. 300-μm-thick coronal slices were vibratome sectioned . Slices were incubated in oxygenated ACSF for 45 min at 34°C, and then maintained in an oxygenated holding chamber at room temperature.Mice were injected at P14–16 with 10–20 nl of an AAV2/1 virus encoding ChR2-H134R-TdTomato (Addgene: 28017). Coronal brain slices were prepared 14 days later from mice anesthetized with an intraperitoneal injection of ketamine (13 mg/ml) /xylazine (1 mg/ml) (~0.01 ml/g body weight solution was injected) and perfused transcardially with ice cold ACSF containing (in mM): 127 NaCl, 25 NaHCOsCRACM) were recorded in voltage clamp  while blue light was stimulated the thalamic axons transfected with ChR2. Each map was repeated two to four times. The maps were averaged and a cell was counted as a positive responder if there was any excitatory postsynaptic current amplitude >6x the standard deviation of the baseline , where the anterior commissure (ac) separates from the rostral migratory stream. The border of the dorsal striatum was determined by the lateral ventricle (VL) and corpus callosum (cc). The ac was included in the striatum mask until it became medial of the VL. Posterior to the commissural part of ac, the ac formed the ventral border of the striatum. In posterior sections containing the globus pallidus extrenal segment (GPe) and the internal capsule, they were considered the medial border of the striatum.To facilitate comparison across experiments and datasets, each experimental striatum mask was aligned to the striatum of the AIBS average template brain . First, The full experimental striatum was scaled in the anterior-posterior (A-P) axis to fit the rotated average template brain based on the first and last section containing the corpus callosum crossing the midline. A linear scaling in the D-V axis was applied based on the average distance from the top of the striatum to the center of mass of the anterior commissure in the front several sections, and this scaling for sections posterior to the anterior commissure crossing the midline was based on the average distance from the top to the bottom of the striatum. Scaling in the M-L axis was determined by an average width of the dorsal striatum above the center of mass. The section images are then iteratively aligned to the rotated average template brain in the D-V axis using the anterior commissure for the first several sections, and the dorsal border of the striatum for posterior sections, and realigned in the M-L axis based on the center of mass of the top half of the striatum.After these alignments, the experimental brains were rotated in all axes to align with the original coordinates of the average template brain, and then subjected to one more round of iterative alignment in each axis as described above. Finally, after visual inspection, if manual adjustments to the alignment were necessary, they were fed back to a point just before the average template brain is rotated to mimic the aberrant sectioning angle of the experimental brain, and the process is repeated. The corresponding thalamic projection masks were aligned concurrently with the striatum masks. The final result is the alignment of each experimental brain to the average template brain . Figure Corticostriatal projections were identified in the AIBS images based on an AIBS custom image segmentation algorithm that identifies all fluorescent pixels and produced a full-resolution (0.35 µm/pixels) binary mask of positive pixels . The imahttp://www.brain-map.org/api/index.html) (Research Recourse Identifier (RRID): SCR_005984) which contained the volume and density of projections to all brain regions defined in the AMBA ontology. This data was utilized in the present study to identify the density of corticothalamic projection in specific thalamic nuclei  was used  striatal volumes that contained corticostriatal projections originating from cortical subregions , (2) strEach voxel was assigned a point in a 15-dimensional space corresponding to the density of projections from each cortical subregion . The optThe chord diagrams illustrating corticocortical connectivity  were genThe network relationship diagrams shown in For the cortico-thalamo-basal ganglia circuit, it is also worth noting that since the cortical subregions used to localize the thalamocortical projections may send corticostriatal projections to more than just the associated striatal subdivision, the thalamocortical data may over-represent the association with the striatal subdivision. However, this does not diminish the relationship seen between the thalamus and cortex for subdivision-specific networks in the circuit, since the thalamocortical inputs are going to a primary input to the striatal subdivision, but it may account for the excess of thalamocortical projections not associated with corresponding thalamostriatal projections in these networks . Further12 particles/ml)  . Injecti3, 11.1 D-glucose, 2.5 KCl, 1.2 MgCl2, 2.4 CaCl2, 1.2 NaH2PO4, ~305 mOsm, supplemented with 5 µM MK-801 and oxygenated with 95% O2/5% CO2. Slices were incubated in oxygenated KREBS buffer supplemented with 10 µM MK-801 for 30 min at 33°C and then maintained in a holding chamber at 22–24°C. Recordings were performed at 32–33°C with oxygenated KREBS buffer containing GABAA- and GABAB-receptor antagonists, nicotinic and muscarinic acetylcholine receptor antagonists, a metabotropic glutamate receptor five antagonist, and an NMDA receptor antagonist. Two experimenters (BCJ and WTB) using two electrophysiology rigs performed whole-cell recordings; experimenters’ initials below note differences between experimental setups. There was no difference in results between experimenters, therefore all data were pooled. Oxygenated KREBS was supplemented with : BCJ, GABAB-receptor antagonist CGP 52432 (10), GABAA-receptor antagonist SR 95531 (10), nicotinic acetylcholine receptor Mecamylamine (10), muscarinic acetylcholine receptor antagonist Scopolamine (10), metabotropic glutamate receptor five antagonist MPEP (0.3), NMDA receptor antagonist MK-801 (5); WTB, GABAB-receptor antagonist CGP 55845 (0.2), GABAA-receptor antagonist Picrotoxin , mecamylamine (1), muscarinic acetylcholine receptor antagonist atropine (0.1) and MPEP (0.3), pre-incubated in MK-801 (5).Coronal brain slices (300 µm) were prepared 14–21 days post-injection with ice cold KREBS buffer containing (in mM) 125 NaCl, 21.4 NaHCO2, 10 Hepes, 1 EGTA, 1.8 Na2ATP, 0.38 Na2GTP, 7.8 phosphocreatine; pH 7.35–7.40; 290 mOsm). Putative MSNs were identified by their morphology and stereotypic physiological properties. Evoked excitatory postsynaptic currents (EPSCs) were recorded in whole-cell voltage-clamp mode at −75 mV holding potential. Recordings for Borosilicate pipettes  were filled with potassium gluconate-based internal solution  1 ms duration light pulses. For paired-pulse stimulation, two consecutive pulses at an interval of 50 ms were given and repeated every 20–40 s for at least five times. Repetitive stimulation consisted of 10 pulses at 20 Hz and was repeated every 20–40 s for at least seven times. Putative MSN with evoked EPSC of ≤−100 pA were included.Ephus software (www.ephus.org) or using an Axopatch 200A amplifier (Molecular Devices) and AxoGraph X software sampled at 20 kHz and filtered online with a 5 kHz low-pass filter (WTB). Data analysis was performed in Matlab, R (http://cran.r-project.org), Igor Pro (Wavemetrics), Excel (Microsoft), Axograph X, Origin7 (OriginLab) and Prizm 6 (GraphPad). Rise- and decay time were calculated based on 10% to 90% of EPSC peak value. For decay time calculation, the presence of a slow current was taken into account. Slow currents of single evoked EPSCs were calculated as the change in mean current (10 ms episode) at 40 ms post-stimulus relative to 10 ms pre-stimulus or, in the case of repetitive stimulation, 10 ms prior to the tenth stimulus over 10 ms before the first stimulus, and normalized to EPSC peak value. Charge transfer was calculated per stimulus over a 50 ms episode starting from the stimulus onset and normalized to the charge transfer evoked by the first stimulus. For data analysis, numbers of observations represent recorded cells from (# cells / # mice): d/vACC, 34/6; V1, 26/4; Thal1, 25/5; Thal2, 32/5.Data were acquired at 10 kHz using an Multiclamp 700B (BCJ) with an online 2 kHz low-pass filter (Molecular Devices) and post-hoc Dunn’s test with Bonferonni correction for multiple testing  were injected with LumaFluor red or green beads (1:1 diluted in sterile PBS) in the dorsomedial (DMS) and posterior striatum (PS). Each animal received one injection in the DMS and two in the PS. Bead color – injection region combination was assigned randomly per animal. Injection coordinates were based on  In the interests of transparency, eLife includes the editorial decision letter and accompanying author responses. A lightly edited version of the letter sent to the authors after peer review is shown, indicating the most substantive concerns; minor comments are not usually included.eLife. Your article has been reviewed by 3 peer reviewers, and the evaluation has been overseen by David Van Essen as the Senior Editor and Reviewing Editor. The following individuals involved in review of your submission have agreed to reveal their identity: David M Lovinger (Reviewer #1).Thank you for submitting your article "A Comprehensive Excitatory Input Map of the Striatum Reveals Novel Functional Organization" for consideration by The reviewers have discussed the reviews with one another and the editor has drafted this decision to help you prepare a revised submission.The reviewers recognized many major strengths of this study. They also identified a number of issues that will entail substantive revisions.Essential revisions:1) Insert a new figure to show the thalamostriatal projection in more detail. This is clearly a key point in the paper, but it seems like a logical step is missing.2) Expand the first part of 3) Split the last part of 4) Address the substantial differences between the present results and those of older corticostriatal studies (including those in rat as well as primate).5) Examine the existing dataset to see if it supports or denies the Yeterian and Van Hoesen,  hypothesis that cortical areas strongly connected with each other converge in the striatum.6) Cite and discuss the recent paper by Hintiryan et al. . Many of the corticostriatal projection subdomains identified by these investigators show good overlap with the data in this manuscript, but there may be some areas of disagreement , and if so these should be discussed.Encouraged but not required:1) A small number of retrograde injections labeling select sub-regions of striatum in order to validate key claims about convergence of cortical and thalamic inputs.2) As suggested by Reviewer #1, show that the oEPSC results are robust.3) Consider including data on entorhinal projections to striatum. This is a highly interesting cortical area for a number of reasons, so it is not clear why it was not included.Reviewer #1:Hunnicutt and coworkers have developed a comprehensive map of the mouse cortico- and thalamostriatal projections , and the overlap of the connections. This was accomplished using a combination of existing databases and new experiments. The authors combined this anatomical connectivity work with detailed computer based analyses of injection volumes, averaging of striatal volumes, and effective subtraction of fasciculated fibers of passage. These approaches were supplemented with high quality optogenetic/electrophysiogical experiments showing the location and properties of intriguing selected cortical and thalamic inputs. Overall this work represents a tour-de-force of structural and functional connectivity analysis that will be valuable to everyone studying the cortico-basal ganglia-thalamic circuits and their functions. The mapping of overlapping cortical and thalamic inputs to striatum is especially valuable. There are a few areas where data presentation might be improved, and I strongly urge the authors to think about ways that input pathways and especially convergent pathways could be represented in schematic or 3d formats that would be easier for readers to understand. There are also a few questions about some aspects of the data and some small corrections that are needed.1) It would be helpful to have at least one figure or panel that included schematic drawings of a section of cortex, a section of striatum and a section of thalamus that would illustrate the convergence of inputs to the striatum, for at least one good example case. Ultimately, it would be great to have several such schematics, maybe posted online. A 3-dimensional plot of some sort would be even more useful, but admittedly difficult to put into the manuscript itself. All the necessary information is certainly present in the figures, it is just very hard to appreciate the overlap without some sort of direct visual representation.2) There did not seem to be any data on entorhinal projections to striatum. This is a highly interesting cortical area for a number of reasons, so it is not clear why it was not included.3) Why were the injections, tracing and recordings performed using such young animals? Is there reason to believe that no anatomical or physiological differences would be observed in adult brain?4) Regarding the oEPSCs, did the paired-pulse and frequency responses and the sustained current vary with light stimulus intensity, duration or the amplitude of the initial oEPSC? The data certainly suggest differences in synaptic properties, but care should be taken to be sure that these differences are not due to differences in the level of channel rhodopsin expression or activation in different projections .5) The authors need to cite and discuss the recent paper by Hintiryan et al. . Many of the corticostriatal projection subdomains identified by these investigators show good overlap with the data in this manuscript, but there may be some areas of disagreement , and if so these should be discussed.6) In the second paragraph of the subsection “Corticostriatal projectome data overview”, the authors note that the striatum does not receive olfactory information, and cite a previous publication and the AIBS site. However, it is not clear if they include the olfactory tubercle in their definition of the striatum. It might be worth revising this statement to indicate that olfactory information does not go to dorsal striatum and only weakly to ventral striatum/accumbens, but that the olfactory tubercle that has many striatal-like properties does receive this input, but will not be the subject of this study. On a related subject, Hintiryan et al. report that piriform cortex projects to ventral/ventromedial striatum, and the Allen Brain connectivity site shows a weak projection to parts of the nucleus accumbens, so perhaps statements regarding this cortical subregion should be revised.Reviewer #2:This paper by Mao et al. examines how cortical and thalamic inputs innervate and define functional regions in the striatum. The authors combine a meta-analysis of a cortex data set from the Allen Institute  with further analysis of a thalamus data set from their own lab . They first establish that different parts of cortex and thalamus make unique projections into the striatum. They then show how the thalamic nuclei associated with thalamostriatal projections relate to the corticostriatal projections. Similarly, they establish how these thalamic nuclei relate to striatal subdivisions defined by corticostriatal inputs. They then show how these thalamostriatal maps relate to their published thalamocortical maps, in an attempt to begin to establish the presence and extent of cortico-thalamo-striatal loops. Finally, they also perform some slice physiology experiments to confirm cortico- and thalamo-striatal connections.While I think there is a lot of interesting information in the paper, the presentation was often confusing, and more example data, analysis, and explanation are needed. It should be noted that a recent study also studied the cortico-striatal projectome in great detail . In my view, the current paper is complementary, as it focuses on thalamostriatal projections and larger circuits. I think it has the potential to be a valuable resource, but several major points first need to be addressed.1) The authors jump from mapping corticostriatal inputs in 2) 3) The first part of 4) The last part of 5) In 6) The authors should discuss in detail how their work relates to Hintiryan et al., 2016.Reviewer #3:This paper shows how far we have come technically. The future of connectivity studies looks a lot like this paper. It is exciting to see a comprehensive approach to a large and diverse projection like the corticostriatal pathway.However, the results make a stark contrast with the results from traditional methods of mapping the corticostriatal projection, and I have more confidence in the previous results. The results here indicate much less structure in the corticostriatal projection than expected, and it may be the projection is less structured in mice than in larger animals. Both the old and new methods (and primates and mice) have their strong and weak points. It is great to cover the entire cortex. In the previous generation of this kind of work it was never possible to do more than a fragment of the cortex in any one study. The positive side of the old primate work is that cortical regions were much better defined, and the projections were seen at higher resolution. Not only are functional regions larger in primates, but it was often possible to define regions functionally, rather than only by position on the cortex. This allowed injections to be placed far from functional boundaries, and even in functional subregions (e.g. hand area of somatosensory cortex). A lesson of those old papers was that cortical regions that are close to each other might be functionally disparate and have disparate projections in the striatum. That is, although they projected to the same general area of the striatum (e.g. the somatosensory cortex recipient region), injections in the hand and foot somatosensory cortex did not have convergent projections at the microscopic level, and so did not converge on single striatal neurons. My first concern about this study is that all of those principles learned from the primate work have been jettisoned, and a strictly spatial scheme is used. Because functional areas of the cortex in the mouse are so small, it is likely that most of these injections involve very different functional cortical regions with different projections in the striatum, and are being superimposed to make the projection look less specific than it is. The same could be said about the thalamic projections. Stated differently, if the cortex or thalamus contained an affine map of function, the lost resolution in the injections could be recovered by comparing nearby injections. If it is not, then this will not work. I think the previous work suggests that it is not.One method that has served well in the past when functional mapping was unavailable was to look at the projections in the thalamus. Often, the precise set of cortical areas involved in a tracer injection can be inferred from its thalamic projections, as these have such sharp boundaries and corticothalamic relations are so well known.One test of whether the results are showing us something really new or are just a blurred version of what we already know would be to find some new principle emerge, or to test an old one. I do not see any clear statement of a new principle. I think in this the authors may have missed an opportunity. One principle proposed by previous authors was that cortical areas that are strongly connected with each other converge in the striatum . It seems to me that the information required to test this idea may be already available in the authors' data set, just by including corticocortical projections.Declaring there to be a sensorimotor domain in the striatum is misleading. The somatosensory and motor systems are very closely connected and in the past many studies have shown that somatosensory and motor function may even overlap spatially in the cortex (of rodents). However, this does not extend to other sensory modalities. For example, visual cortex or its projections to the striatum do not go to the region called sensory by the authors. Throughout the paper, the word sensory seems specifically associated with the somatosensory cortex. I think the authors should replace the word 'sensory' in all cases in which it appears with 'somatosensory'. One thing we have learned from previous studies is that there is very little overlap between visual and somatosensory cortical projections to the striatum, and very little between visual and primary motor cortex. This is evident in  Essential revisions:1) Insert a new figure to show the thalamostriatal projection in more detail. This is clearly a key point in the paper, but it seems like a logical step is missing.We have now, in a new figure , added eAlong the same spirit of illustrating the key steps in our analyses using example images, we have also added Overall, we hope these illustrations will help general readers to better understand our analyses.2) Expand the first part of Due to the intertwining nature of individual injections/projections within the cortico-thalamo-basal ganglia loops, we find it difficult to completely separate the discussion and presentation of thalamostriatal and thalamocortical projections in the original 3) Split the last part of As mentioned above, we have now added As discussed above, we did not split the previous 4) Address the substantial differences between the present results and those of older corticostriatal studies (including those in rat as well as primate).We have now devoted major text contents to discuss our finding in relationship to the available literature in rat and primate. As reviewer 3 accurately stated, the advantages of our study are two folds. First, because mouse brains are small, we were able to cover all cortical subregions and > 93% volume of the thalamus with individual injections of small (500-600 µm) sizes, and image the entire projections of every injection. The level of comprehensiveness in documenting thalamic projections has not been previously achieved in any mammalian species. Second, the comprehensiveness of the dataset allow us to perform quantitative analyses that are only possible with full dataset, but not with example images. For example, conventional descriptions of thalamic projections are mostly based on thalamic nucleus demarcations which, when compared to cats or primates, are less distinct and sometimes ambiguous in mice . As better stated in the textbook by Sherman and Guillery (2006), ‘The concept of the thalamic nucleus as a single structural, functional, and connectional entity has barely survived advancing techniques and new information. We stay with the thalamic nuclei as one of our prime analytical tools because, as yet, we have little to use in its place’. In our studies, we take the unique opportunity to establish nucleus-independent thalamic projection maps by using model thalamus based on averaging 70-100 mouse brains .n numbers we could get and used overlapping injections to increase the resolution beyond individual injection volumes . This algorithm is the operational expression of what reviewer 3 mentioned as ‘the lost resolution……recovered by comparing nearby injections’. Furthermore, our confidence maps are statistical by nature and therefore, are difficult to be compared to the traditional anatomical tracing just by numbers. For cortical injections, we integrated Allen Institute data but with strict criteria for selecting the injections within a given subregion (Methods). However, unlike in the primate literature, the definition of certain cortical subregions themselves can be controversial in mice  and even with the ones that have clear functional definitions, the physical landmarks are often lacking to clearly demarcate them. We did the best practice possible by sticking to the existing mouse atlas using visible landmarks and by being consistent in injection selection process. Even with great care, the lack of sufficient demarcating landmarks may contribute to variability. We are also limited to what is available in AIBS database for cortical injections which is already the best effort available with current technology using over 1600 brains for establishing the model brain to assign the subregions. Because these cortical injections are mostly not-overlapping, we could not employ similar resolution improving algorithms as we did with thalamic injections . Despite these caveats, given that the mouse is an important model system for studying brain circuitry, our effort will provide a framework to examine the subregion-specific circuits in the mouse striatum.The caveats of our data and analyses were also pinpointed by reviewer 3: because the mouse brain is small, the definition of the subregion that gives the projections might be vague and ambiguous. That could be the source of the variation. This is true both for defining cortical injections and thalamic injections. For thalamic injections, as stated above, we took full advantage of the high Two technical notes are also important regarding why our datasets are different from certain traditional data. First, the high resolution imaging is crucial in our analyses because it allowed us to specify the portions of the striatum where there are a lack of projections, and we took consideration of the negative data (lack of projections) when localizing projection origins in the thalamus .We have added the above points in our Discussion.5) Examine the existing dataset to see if it supports or denies the Yeterian and Van Hoesen,  hypothesis that cortical areas strongly connected with each other converge in the striatum.We have now added analyses to address the hypothesis proposed by Yeterian and Van Hoesen. We first downloaded corticocortical projection data from Allen Institute of Brain Science mouse atlas API and then analyzed projection density between cortical subregions . We founAlong this line, we also further integrated cortiocortical connectivity data into our cortico-thalamo-basal ganglia loop analyses .6) Cite and discuss the recent paper by Hintiryan et al. . Many of the corticostriatal projection subdomains identified by these investigators show good overlap with the data in this manuscript, but there may be some areas of disagreement , and if so these should be discussed.We have now added the citation of Hintiryan et al. and discussed the related contents. Hintiryan et al. uses an anterograde tracing dataset from cortical injections to illustrate the corticostriatal circuits and demonstrated the usefulness of large scale mesoscopic projection mapping to study ‘circuitry-specific connectopathies’. This study and ours share the same spirit in terms of using comprehensive mesoscopic cortical projections in the striatum to understand the logic of the striatum circuit. At the same time, our study also differs from Hintiryan et al. as we include the thalamostriatal pathway and eventually integrate information regarding cortico-cortical, corticothalamic and thalamocortical circuits. These two studies also emphasize the complementary aspects of the striatal circuits, as detailed below.First, Hintiryan et al. paper focused on cortical inputs to the dorsal striatum. In addition to cortico-dorsal striatum data, our study also includes cortico-ventral striatum projections and the projections from the thalamus to the entire striatum. Our thalamostriatal dataset is of particular interest because thalamostriatal data for mouse is scarce in the literature and the circuits are much less understood compared to the corticostriatal pathways. As an example of conclusions unique to our study, the thalamic origins of the projections to the dorsal and ventral striatum, respectively, show a large degree of complementary patterns . ConceptSecond, Hintiryan et al. identified 29 distinct striatal domains, which are useful for understanding the sub-organizations of the striatum and studying their functions. However, whether there are hierarchical organizations between these domains is less clear. In our analysis, we described a fourth primary subdivision within the posterior striatum, which is in parallel to the conventional dorsomedial, dorsolateral and ventral striatal subdivision. By lowering the clustered dendrogram thresholds, increasing numbers  of subdivisions can be identified and described, but most of these smaller subdivisions are associated with one primary striatal subdivision, the dorsomedial subdivision, and this is consistent with the associative function of this subdivision .Third, while Hintiryan et al. examined two mouse lines and demonstrated the subregion-specific changes in pathological conditions, we provide physiology/optogenetics mediated functional confirmation of the anatomically defined pathways. Importantly, we showed that the subregion-specific input pathways have distinct plasticity properties and this finding could reconcile the apparent discrepancy in the field (Discussion).Regarding the corticostriatal projections, our data is overall consistent with Hintiryan et al. As we discuss both above  and below (Encouraged but not required #3), the caveats associated with mouse cortical injection is the difficulty in assigning precisely the subregions being injected. This in turn can make it difficult in cases to evaluate whether individual injections are in and only within a claimed cortical subregion. This applied to both work. For this reason, we were very conservative in including any injections. For example, we included <10% of all published AIBS cortical injections, and we did not take entorhinal as an individual group because almost all AIBS injections in this area also covered either entorhinal, perirhinal, or some of temporal cortex. We instead included entorhinal in Rhi/Tem group . The small differences between the two studies in corticostriatal inputs might potentially be from jitters in assigning or grouping the cortical subregions.These above points are now included in the Discussion.Encouraged but not required:1) A small number of retrograde injections labeling select sub-regions of striatum in order to validate key claims about convergence of cortical and thalamic inputs.We have now performed retrograde injection experiments by using retrobeads to target 2 striatal subdivisions described in 2) As suggested by Reviewer #1, show that the oEPSC results are robust.We have now added analysis to show that oEPSC results are robust. The discussion below also refers to P= 0.09, Χ2 = 6.54); we also plotted PPR over the light power and there is no correlation between the PPR and the light power . Similarly, we also plotted EPSC over light power for single light stimulation and repetitive light stimulation (panel B and D). Our findings regarding the functional heterogeneity of striatal inputs are relative measures obtained within each recording, namely paired-pulse ratio and relative standing current to EPSC amplitude size. Since there is no correlation between PPR or relative standing current with light power, the observed heterogeneity is a feature of the biological system.Reviewer 1brought up one of the most important parameters in any channelrhodopsin-related quantitative comparison. We were fully aware of the ‘effective’ expression variations across animals. For this reason, our viral titers, infection time etc. parameters were the same for all four groups (we kept them even from the same batch of virus). Even with the best experimental effort, there could be intrinsic factors that give rise to different ‘effective’ expression levels for certain groups. Therefore, we plotted light power used for all four groups  and there is no overall difference in light power used among the four groups (panel A in We want to emphasize that this set of experiments were carried out in two different setups and performed by two independent experimenters (pink and blue dots in the attached plot represent two independent experimenters) to minimize this types of bias in channelrhodopsin experiments, the results obtained from the two setups gave the same conclusion (the data in the manuscript was pooled data).3) Consider including data on entorhinal projections to striatum. This is a highly interesting cortical area for a number of reasons, so it is not clear why it was not included.Our data do include entorhinal (ENT) inputs , and the overlap of the connections. This was accomplished using a combination of existing databases and new experiments. The authors combined this anatomical connectivity work with detailed computer based analyses of injection volumes, averaging of striatal volumes, and effective subtraction of fasciculated fibers of passage. These approaches were supplemented with high quality optogenetic/electrophysiogical experiments showing the location and properties of intriguing selected cortical and thalamic inputs. Overall this work represents a tour-de-force of structural and functional connectivity analysis that will be valuable to everyone studying the cortico-basal ganglia-thalamic circuits and their functions. The mapping of overlapping cortical and thalamic inputs to striatum is especially valuable. There are a few areas where data presentation might be improved, and I strongly urge the authors to think about ways that input pathways and especially convergent pathways could be represented in schematic or 3d formats that would be easier for readers to understand. There are also a few questions about some aspects of the data and some small corrections that are needed.1) It would be helpful to have at least one figure or panel that included schematic drawings of a section of cortex, a section of striatum and a section of thalamus that would illustrate the convergence of inputs to the striatum, for at least one good example case. Ultimately, it would be great to have several such schematics, maybe posted online. A 3-dimensional plot of some sort would be even more useful, but admittedly difficult to put into the manuscript itself. All the necessary information is certainly present in the figures, it is just very hard to appreciate the overlap without some sort of direct visual representation.We have now added a new figure  that walWe agree with the reviewer that 3-D plots would be helpful. Data visualization at this scale is challenging and by itself is a rapidly developing field actively pursued by computer scientists and bioinformatists besides biologists. With the current resource we have, presenting the data in 3-D will be beyond the scope of this manuscript. However, along the spirit of the suggestion, we have made efforts in better presenting the data in reduced dimensionality , and hav2) There did not seem to be any data on entorhinal projections to striatum. This is a highly interesting cortical area for a number of reasons, so it is not clear why it was not included.As discussed earlier, our data do include entorhinal (ENT) inputs (e.g. injection number 142656218 and 182794184 listed in 3) Why were the injections, tracing and recordings performed using such young animals? Is there reason to believe that no anatomical or physiological differences would be observed in adult brain?The thalamic injected mice were sacrificed ~1 month age. This age was chosen mainly based on our experience with physiological characterizations of the thalamocortical projections with the consideration of two aspects: the technical consideration and the biological consideration. On the technical part, the cortical slices are more robust, and pyramidal neurons, especially layer 5 pyramidal neurons, are much more healthy in young adult mice compared to older mice. So, to provide cortical recordings to validate the thalamocortical projections, as we have done in Hunnicutt et al. 2014 paper, we chose the age (P14-18) that we could do consistent stereotaxic injections and after two weeks of viral infection, we could still have high success rate of cortical neuron recordings. Although the slice health is less of an issue for the striatum, we aimed the injections to accommodate both thalamocortical and thalamostriatal pathways for physiology. On the biological part, the motor and sensory areas are considered functionally and structurally matured by our time-point (P30) and this time point is widely used to investigate sensorimotor integration , although the frontal area is still dynamic around this time. In our previous work , we tested whether there may be large rearrangements in thalamocortical projection distributions, especially the thalamo-frontal projections in young adult mice. We found that thalamocortical projections from at least 24 nuclei have reached the frontal area and form functional synapses by P30, with all frontal subregions being contacted by at least 5 thalamic nuclei. Since the frontal subregions innervated by each nucleus are comparable to those seen in the adult rat, our data suggest that the majority of thalamocortical projections to the frontal area have reached their final targets by P30 in mouse. Therefore, the behavioral changes during young adult rodents are more likely due to local refinements and synaptic pruning, rather than larger rearrangements in thalamocortical projection distributions to frontal subregions.The reviewer’s comment also brought up a really interesting question: would anatomical and/or physiological connectivity be different in older mouse brains at the mesoscopic resolution. This question is definitely worth testing but is beyond the scope of the current manuscript.4) Regarding the oEPSCs, did the paired-pulse and frequency responses and the sustained current vary with light stimulus intensity, duration or the amplitude of the initial oEPSC? The data certainly suggest differences in synaptic properties, but care should be taken to be sure that these differences are not due to differences in the level of channel rhodopsin expression or activation in different projections .P= 0.09, Χ2 = 6.54); we also plotted PPR over the light power and there is no correlation between the PPR and the light power . Similarly, we also plotted EPSC over light power for single light stimulation and repetitive light stimulation (panel B and D). Our findings regarding the functional heterogeneity of striatal inputs are relative measures obtained within each recording, namely paired-pulse ratio and relative standing current to EPSC amplitude size. Since there is no correlation between PPR or relative standing current with light power, the observed heterogeneity is a feature of the biological system.The reviewerbrought up one of the most important parameters in any channelrhodopsin-related quantitative comparison. We were fully aware of the ‘effective’ expression variations across animals. For this reason, our viral titers, infection time etc. parameters were the same for all four groups. Even with the best effort, there could be intrinsic factors that give rise to different ‘effective’ expression levels for certain groups. Therefore, we plotted light power used for all four groups  and there is no overall difference in light power used among the four groups (panel A in We want to emphasize that this set of experiments were carried out in two different setups and performed by two independent experimenters (pink and blue dots in the attached plot represent two independent experimenters) to minimize this types of bias in channelrhodopsin experiments, the results obtained from the two setups gave the same conclusion (the data in the manuscript was pooled data).We decided not to put these plots as a figure in the manuscript. This is because although it is an important point, channelrhodopsin is more of a standard reagent now. We do state this point in the manuscript. We wrote: ‘The results presented here do not show correlations with the light power used for photostimulation (data not shown).’We now change the label for scales for both panels C and D and as the reviewer indicated, they are the same labels.5) The authors need to cite and discuss the recent paper by Hintiryan et al. . Many of the corticostriatal projection subdomains identified by these investigators show good overlap with the data in this manuscript, but there may be some areas of disagreement , and if so these should be discussed.See our response to Essential revision #6.6) In the second paragraph of the subsection “Corticostriatal projectome data overview”, the authors note that the striatum does not receive olfactory information, and cite a previous publication and the AIBS site. However, it is not clear if they include the olfactory tubercle in their definition of the striatum. It might be worth revising this statement to indicate that olfactory information does not go to dorsal striatum and only weakly to ventral striatum/accumbens, but that the olfactory tubercle that has many striatal-like properties does receive this input, but will not be the subject of this study. On a related subject, Hintiryan et al. report that piriform cortex projects to ventral/ventromedial striatum, and the Allen Brain connectivity site shows a weak projection to parts of the nucleus accumbens, so perhaps statements regarding this cortical subregion should be revised.Our original statement did not mean to include olfactory tubercles. We now revised the statement in Methods regarding the olfactory processing to make this clearer.We wrote: ‘Since that olfactory information does not project directly to the dorsal striatum and only very weakly to the ventral striatum and with olfactory tubercle not considered, olfactory areas and the piriform cortex were not included , leaving 957 injections’.Reviewer #2:[…]While I think there is a lot of interesting information in the paper, the presentation was often confusing, and more example data, analysis, and explanation are needed. It should be noted that a recent study also studied the cortico-striatal projectome in great detail . In my view, the current paper is complementary, as it focuses on thalamostriatal projections and larger circuits. I think it has the potential to be a valuable resource, but several major points first need to be addressed.1) The authors jump from mapping corticostriatal inputs in To add more example data and to improve the presentation and logic flow, we have now added the following full figures: Along the same spirit of illustrating the key steps in our analyses using example images, we have also added 2) See our response to Encouraged but not required #1.3) The first part of We have now added a new figure  to expan4) The last part of We have now included datasets for thalamocortical, thalamostriatal, corticothalamic and corticocortical pathways. As discussed above, we have added a new main figure  and two Overall, we kept S1/2 subregion as an example to explain the concept of the triangular loop  and use 5) In 2= 0.01, 0.61, -0.01, and 0.07 respectively for Thal1, Thal2, Vis and d/vACC). Although R2 for Thal2 is 0.61, the intercept and the slope for the fitting are very small (-0.009 and 0.02 respectively), consistent with the idea that there is no slow current for Thal2 compared toThal1 and d/vACC  The authors should discuss in detail how their work relates to Hintiryan et al., 2016.See our response to Essential revision #6.Reviewer #3:This paper shows how far we have come technically. The future of connectivity studies looks a lot like this paper. It is exciting to see a comprehensive approach to a large and diverse projection like the corticostriatal pathway.However, the results make a stark contrast with the results from traditional methods of mapping the corticostriatal projection, and I have more confidence in the previous results. The results here indicate much less structure in the corticostriatal projection than expected, and it may be the projection is less structured in mice than in larger animals. Both the old and new methods (and primates and mice) have their strong and weak points. It is great to cover the entire cortex. In the previous generation of this kind of work it was never possible to do more than a fragment of the cortex in any one study. The positive side of the old primate work is that cortical regions were much better defined, and the projections were seen at higher resolution. Not only are functional regions larger in primates, but it was often possible to define regions functionally, rather than only by position on the cortex. This allowed injections to be placed far from functional boundaries, and even in functional subregions (e.g. hand area of somatosensory cortex). A lesson of those old papers was that cortical regions that are close to each other might be functionally disparate and have disparate projections in the striatum. That is, although they projected to the same general area of the striatum (e.g. the somatosensory cortex recipient region), injections in the hand and foot somatosensory cortex did not have convergent projections at the microscopic level, and so did not converge on single striatal neurons. My first concern about this study is that all of those principles learned from the primate work have been jettisoned, and a strictly spatial scheme is used. Because functional areas of the cortex in the mouse are so small, it is likely that most of these injections involve very different functional cortical regions with different projections in the striatum, and are being superimposed to make the projection look less specific than it is. The same could be said about the thalamic projections. Stated differently, if the cortex or thalamus contained an affine map of function, the lost resolution in the injections could be recovered by comparing nearby injections. If it is not, then this will not work. I think the previous work suggests that it is not.One method that has served well in the past when functional mapping was unavailable was to look at the projections in the thalamus. Often, the precise set of cortical areas involved in a tracer injection can be inferred from its thalamic projections, as these have such sharp boundaries and corticothalamic relations are so well known.n numbers we could get and used overlapping injections to dramatically increase the resolution . This algorithm is the operational expressing of what this reviewer mentioned as ‘the lost resolution…recovered by comparing nearby injections’. For cortical injections, we did the best practice possible by sticking to the existing mouse atlas and by being consistent in selection process (that is why < 10% of AIBS injections was selected into the dataset). Even with great care, the lack of clear landmarks in mouse cortical subregion definition may still be a source of variability. We are also limited to what is available in AIBS database for cortical injections which is already the best effort available with current technology with using over 1600 brains for establishing the model brain to assign the subregions. These cortical injections are mostly not overlapping with one another, so we could not employ similar resolution-improving algorithms we did with thalamic injections.The reviewer has pointed out the exact advantages and the disadvantages of the mice mesoscopic mapping. The advantages of our study in mice are two folds. First, because mouse brains are small, we were able to cover all cortical subregions and > 93% volume of the thalamus with individual injections of small (500-600 µm) sizes, and image the entire projections of every injection. The level of comprehensiveness in documenting thalamic projections has not been previously achieved in any mammalian species. Second, the comprehensiveness of the dataset allow us to perform quantitative analyses that are only possible with full dataset, but not with example images. For example, conventional descriptions of thalamic projections are mostly based on thalamic nucleus demarcations which, when compared to cats or primates, are less distinct and sometimes ambiguous in mice . As better stated in the textbook by Sherman and Guillery (2006), ‘The concept of the thalamic nucleus as a single structural, functional, and connectional entity has barely survived advancing techniques and new information. We stay with the thalamic nuclei as one of our prime analytical tools because, as yet, we have little to use in its place’. In our studies, we take the unique opportunity to establish nucleus-independent thalamic projection maps by using model thalamus based on averaging 70-100 mouse brains . We also devoted significant effort to connect with the existing literature: for all the confidence maps presented, we overlay the confidence maps with the nucleus demarcations of both Paxinos atlas and Allen Brain atlas .But becaWe do not want to claim that we have solved the issue of dealing with less accurate brain region assignment (compared to primate work). Instead, we feel that our work illustrates the exact issues . Many of our analyses were, in fact, inspired by the primate work  because we had to think how to take advantage of our data and combat the disadvantages. In the meantime, mouse has become an essential model in understanding the brain circuits. We reason that providing such anatomical maps, despite possible drawbacks, is important.Two technical notes are also important regarding why our datasets are different from certain traditional data. First, the high resolution imaging is crucial in our analyses because it allowed us to specify the portions of the striatum where there are a lack of projections, and we took consideration of the negative data (lack of projections) when localizing projection origins in the thalamus .We have added the discussion for above points (Discussion).One test of whether the results are showing us something really new or are just a blurred version of what we already know would be to find some new principle emerge, or to test an old one. I do not see any clear statement of a new principle. I think in this the authors may have missed an opportunity. One principle proposed by previous authors was that cortical areas that are strongly connected with each other converge in the striatum . It seems to me that the information required to test this idea may be already available in the authors' data set, just by including corticocortical projections.We have now added analyses to address the hypothesis proposed by Yeterian and Van Hoesen. We first downloaded corticocortical projection data from Allen Institute of Brain Science mouse atlas website and then analyzed projection density between cortical subregions . We founAlong this line, we also further integrated cortiocortical connectivity data into our cortico-thalamo-basal ganglia loop analyses .Declaring there to be a sensorimotor domain in the striatum is misleading. The somatosensory and motor systems are very closely connected and in the past many studies have shown that somatosensory and motor function may even overlap spatially in the cortex (of rodents). However, this does not extend to other sensory modalities. For example, visual cortex or its projections to the striatum do not go to the region called sensory by the authors. Throughout the paper, the word sensory seems specifically associated with the somatosensory cortex. I think the authors should replace the word 'sensory' in all cases in which it appears with 'somatosensory'. One thing we have learned from previous studies is that there is very little overlap between visual and somatosensory cortical projections to the striatum, and very little between visual and primary motor cortex. This is evident in We agree with the reviewer. The term meant to be used for somatosensory cortical area and it was misleading. We change ‘sensory’ to ‘somatosensory’ throughout the manuscript.