Phosphorus (P) is a key yield-limiting nutrient for crops, but the main source of P fertiliser is finite. Therefore, efficient fertilisation is crucial. Optimal P application requires understanding of the dynamic processes affecting P availability to plants, including fertiliser dissolution rate and soil buffer power. However, standard soil testing methods sample at fixed time points, preventing a mechanistic understanding of P uptake variability. We used image-based modelling to investigate the effects of fertiliser dissolution rate and soil buffer power on P uptake by wheat roots imaged using X-ray CT. We modelled uptake based on 1-day, 1-week, and 14-week dissolution of a fixed quantity of total P for two common soil buffer powers. We found rapid fertiliser dissolution increased short-term root uptake, but total uptake from 1-week matched 1-day dissolution. We quantified the large effects root system architecture had on P uptake, finding that there were trade-offs between total P uptake and uptake per unit root length, representing a carbon investment/phosphorus uptake balance. These results provide a starting point for predictive modelling of uptake from different P fertilisers in different soils. With the addition of further X-ray CT image datasets and a wider range of conditions, our simulation approach could be developed further for rapid trialling of fertiliser-soil combinations to inform field-scale trials or management. To keep up with global food demand, phosphate fertiliser is applied to fields at a rate of nearly 50,000,000 tonnes each year, and the demand for P is growing at a rate of around 2.2% a year3. However, most of the global P fertiliser supply is mined from rock phosphate, a resource so limited that it could run out entirely in just over 250 years or sooner depending on how demand grows4. Despite this growing threat, modern agriculture often applies excessive fertiliser quantities with the goal of maximising short term yields5. Not only is this an inefficient use of the increasingly-scarce resource, it can also lead to a saturation of P in the soil which can cause P run-off and leaching into water sources with serious environmental consequences. To ensure sustainable use of P in agriculture, strategies are required to decrease unnecessary P fertilisation and increase plant uptake from the fertiliser (fertiliser use efficiency) while maintaining high yields and nutritious crops6.Phosphorus (P) is a key limiting nutrient for plant growth7. This requires an understanding of how different fertilisers respond to different soils, how soluble they are, and what that means for its availability to plants. While conventional experimental approaches can test overall efficacy of a particular application on final crop yields, they provide limited insight into the dynamic processes that influence how effective a particular fertiliser in a particular environment can be8. An understanding of the early dynamics of P availability is particularly important given yield is known to depend on early P uptake9.Approaches to increase fertiliser efficiency typically make improvements to at least one of the ‘4Rs’: the right nutrient source, the right rate of application, the right time of application, and the right place10. Thus, the P uptake from soil and added fertilisers is often inefficient1. In the context of fertilisation, P availability is also affected by the solubility of the fertiliser9. Some fertilisers such as triple-super phosphate (TSP) dissolve rapidly in water11, while others, such as struvite, dissolve much more slowly, as only 30% of the mass of struvite pellets dissolved after a 14 week experiment in soil12. Solubility in water is not necessarily indicative of how effective a fertiliser will be in an agricultural context since typical agricultural soils are often unsaturated and contain a complex mixture of biological and abiotic components that affect dissolution rate and dispersal13.The availability of P to plants is largely determined by the buffering of phosphates to soil constituents; this is high due to the low solubility of P minerals and fast adsorption of P onto soil particles. This reduces the distance that P moves in the soil and ultimately limits the uptake of P by plant roots14. Nkebiwe et al.15 found that fertiliser sources with fast dissolution had greater yield and nutrient uptake compared to slow dissolution fertilisers. In contrast, other researchers9 found fertiliser recovery was similar between diammonium phosphate (DAP), which is a relatively soluble source, and struvite for spring wheat at harvest.Due to the numerous processes affecting P availability it is not clear if faster fertiliser dissolution necessarily results in higher plant uptake. For example, a highly soluble fertiliser could release its P before roots are within range and the P could become immobilized to soil particles before the plant has the chance to use it16. These slow release fertilisers have potential benefits in reducing runoff17, and some are considered more sustainable due to their recycled sources. However, the efficacy of these different fertilisers varies between studies, with some indicating that slow release fertilisers can be highly effective19, and others suggesting that they are insufficient20. Some of these differences are likely due to experimental designs21, soil variability, and variation in root architectures22. Additionally, plants have different nutrient requirements at different stages of growth. They need a large amount of P early in growth, so a P fertiliser that dissolves more slowly may not provide enough P at the critical time and may be less effective in terms of increased yield23. Finally, P that is bound to soil may resolubilise and become available again. However, this is seldom considered in the context of agricultural systems24. Therefore, the trade-offs between rapid release vs sustained release are not fully understood.Precision application of fertilisers requires an understanding of dissolution rates, sorption onto soil, plant availability, and potential run-off. Fertilisers with different dissolution rates aim to target either rapid release for immediate plant uptake or sustained release, with some claiming to only release fertiliser in the presence of plant roots. There is a current focus on alternative P sources from recycled materials and methods to increase P availability to plants that include coating soluble pellets25 and microdialysis sensors26. These studies have provided new insights into the P dynamics in soil and highlighted the need to revisit and update our preconceptions of these processes at fundamentally relevant scales.To rigorously assess the function of different fertilisers applied at different stages in plant development, it is important to understand how the P movement and availability evolves over time. However, standard soil field protocols often only provide snapshots and can lack the capacity to disentangle small-scale biogeochemical processes and proximity of roots to fertilisers. Recent studies have begun to focus on these processes at greater spatial and temporal resolution using soil solution sampling techniques such as suction cups27. When using the latter modelling approach, root geometry including proximity to high P sources is averaged and thus important processes for fertiliser efficiency are overlooked. In particular, the details of root competition for the same soil/fertiliser P supply within a discretised area/length are averaged without consideration for localised root interactions27. With image-based modelling, not only is explicit root fertiliser proximity calculated from experimental data, but competition between roots (of the same plant) for local P supplies are simulated22, allowing accurate assessment of P uptake efficiency, in the sense of unit of P absorbed per unit of photosynthate spent of root growth, from fertilisers. Including such geometric detail in image-based models comes at large computational cost. Thus, image-based models are less suitable for research questions where multiple-physics and/or larger spatial scales need to be considered, and models that represent roots by parameters or functions are the preferred modelling approach27.While experimental approaches can provide some spatial and temporal resolution, modelling approaches can fill in measurement gaps and allow us to test different scenarios more rapidly. This could provide a complementary approach to focus and enhance more costly and time consuming field trials. Image-based modelling  offers vital advantages over models where root structure are represented by parameters or functions for the problem of assessing fertiliser dissolution rates and P availability and can complement results from imaging studies12 and compared anatomically the root systems of spring wheat plants treated with a fertiliser pellet. We then assessed P uptake from the fertiliser pellet using image-based modelling assuming a range of model fertiliser dissolution rates (similar to TSP or struvite) and soil P sorption properties. We aimed to quantify the effect of fertiliser dissolution on plant uptake of P from the pellet, and investigate the role of root system architecture and pellet location in these processes. We expected the benefit of the fertiliser pellet to be more noticeable in the high buffering soil due to lower available P in the bulk soil. We also expected that there would be more variability in P uptake in the high buffering soil because low P mobility would cause greater local variation in P concentrations.This study investigated the fundamental processes governing the efficacy of different P fertiliser dissolution rates and developed a protocol that could be used to test a range of different fertiliser and soil conditions, which can be used to predict efficient fertiliser applications in space, time and quantity given soil and fertiliser properties. To this aim, we developed an image-based modelling approach to investigate how P dissolution rates and soil buffering affect P availability to plant roots. We used X-ray CT data from a previous study12 was a PVC column with a diameter of 110 mm and height of 500 mm. The column was filled with a P-deficient (Olsen P = 12.6 mg l−1) sandy loam textured Eutric Cambisol taken from depths between 0–15 cm depth (Ahp horizon) collected from a freely draining grassland located in Abergwyngregyn, UK  and sieved to pass 5 mm. The columns were sufficiently large to allow the plants to grow to maturity. The cylindrical shape and relatively narrow diameter facilitated XCT imaging. Single spring wheat plants  were grown in each column. See Ahmed, et al.12 for full experimental setup including water, light, and actual fertiliser conditions. Three replicates were analysed.The experimental system for X-ray CT imagingColumns were imaged using a custom-design micro-focus Nikon ‘hutch’ μCT scanner  at 14 weeks post-planting. For each column, image data were acquired using an energy of 150 kV, and a power of 15.15 W. 3142 projections were collected over 360° rotation using a flat panel detector with 2048 × 2048 pixels, and the voxel size was 60 μm. The average scan time for each column was 56 min. Reconstructions were carried out using a proprietary filtered back-projection algorithm implemented in CTPro 3D .29, a free, open-source software package for image processing and analysis, and Dragonfly , a commercial software package for image processing, analysis, and visualisation . Due to imaging constraints (60 µm resolution) and processing throughput (1 pixel opening operation), any roots smaller than 180 µm will not be visible in the image.Roots were segmented from the surrounding soil using a custom workflow, with a combination of non-local means filtering, grey value thresholds, morphological filters, and shape-based filters implemented in a combination of the FIJI distribution of ImageJon Figs.  and S2. 32  from the soil surface and therefore this depth was chosen to represent the fertiliser pellet depth. The exact radial positions of each pellet were unknown, but P sources representing the pellets were added in silico as detailed below in the modelling section. This ‘pellet’ was then used as the basis for a distance transform using ‘Exact Distance Transform 3D’ in ImageJ Fig. . GreyscaTo measure the mean distance from the pellet to the roots, the skeleton of the roots was multiplied with the distance map of the pellet, and the mean grey value calculated across the stack. Lastly, a proxy for the extent that a root system architecture had explored was assessed by computing the mean distance between soil (i.e. each voxel within the soil domain) and its nearest root. This we refer to as mean soil–root distance and is calculated using the mean grey value of a distance transform from the roots Fig. .1. The roots and fertiliser pellets were treated as surfaces extracted from the XCT images in the soil domain  or 519 (high buffering soil11) [–] is the buffer power of Pl in this soil, defined as k1 [s−1] and k2 [s−1] are the adsorption desorption rates in the first-order kinetics describing soil sorption respectively. 2 s−1] is diffusion coefficient of P in soil solution and f = 0.3 [–] is the geometric impedance to diffusion from the soil structure1. At the edge of the soil column we impose a no-flux boundary condition so P cannot leave the domainn is the outward pointing unit normal to the surface.We developed an image-based model to allow us to better resolve spatial and temporal transport of P fertiliser. The model enabled inferences for unmeasured quantities in the study such as P concentrations in the soil and root P uptake. Furthermore, the modelled system was used to simulate different fertiliser solubilities for different root system architectures. The classical Nye–Tinker–Barber model was used to represent P movement in a 3D homogeneous-soil domain containing 14-week root systems−2 s−1] is the maximum possible root P uptake rate, −3] is P concentration when the root uptake rate is half 1 and 34,−2 s−1] is the initial release rate  we use a step function to activate the roots once they have reached the fertiliser pellet depth,As the root systems extracted from the X-ray CT scans were very large, simulations using the full root system geometry could not be obtained due to computer memory and processing limitations. Instead, each column was divided into 6 sectors each running the full length of the image stack and one sector was chosen at random to be used for simulations, as each sector is expected to contain 1 fertiliser pellet. This process introduces an artificial boundary to −1] and P uptake efficiency, defined as either total uptake per root surface area [µmol m−2] or uptake rate per root surface area [µmol m−2 s−1].We hypothesised that faster fertiliser dissolution would improve plant P uptake from the pellet, but root system architecture would play a large role in fertiliser uptake efficiency. Additionally, we expected the benefit of the fertiliser pellet to be more noticeable in the high buffering soil due to lower available P and the role of root system architecture to be more prominent due to low P mobility. To assess these hypothesis, the modelled dynamic uptake rate was quantified for a number of scenarios. For each buffer power and root system, four cases were simulated: Complete fertiliser dissolution takes 14-weeks . The homogeneous-soil volume meshes typically had an element–volume ratio of 36.One way ANOVA was used to test for differences between means while two-sample t-tests were used for specific pairwise comparisons between means of model results. A threshold of p = 0.05 was chosen as significant. All statistics were calculated using scipyAll local, national or international guidelines and legislation were adhered for the use of plants in this study.To model the P uptake, the whole image stack was divided into six equal sectors using the full depth of the stack and a sector from each stack was selected randomly. For measurements, and for modelling going forwards, results refer to the selected sector only. A P source was placed within the sector, in a location consistent with the depth of the experimental pellet, Fig. In Fig. t test, two tailed, p = 0.0007 and 0.004 respectively) and only P uptake in the high buffering soil (p = 0.0067) compared to the zero fertiliser treatment. By adding the fertiliser, the total uptake was improved by 104% in the low buffering soil and 63% in the high buffering soil on average. Introducing fertiliser also increased the variability in total uptake, but to a lesser extent the variability in uptake efficiency between the root systems compared to the zero fertiliser treatment, Fig. In terms of total P uptakes over the 14-week simulation, there was no significant difference in either absolute P uptake or P uptake efficiency between the different fertiliser dissolution rates (one way ANOVA)  and struvite for spring wheat at harvest, but that wheat plants fertilised with struvite developed significantly fewer mature grain heads than those fertilised with TSP. As with our model, they also found that early uptake of P was significantly reduced (39%) in the struvite despite total uptake being similar, and this may be the reason for differences in grain development. Talboys et al.9 therefore argue for a combination of slow-release and fast-release fertilisers to ensure the needs of the plant at different times are being met. Our model could be further developed to identify the optimal combinations of the two types of fertiliser to provide appropriate levels of P to the plant.Based on the model results, when averaged across the different root systems, P uptake was similar between the different dissolution rates for the pellet. In fact, the only difference between the mean results is that P fertilisation is expected to lead to higher P uptake, which is perhaps an obvious result, but at least demonstrates that the fertiliser from the “pellet” is reaching the plant roots and affecting overall uptake. However, the major advantage of the image-based modelling approach used here is the ability to more thoroughly explore the variability over time and between different root systems, effectively allowing us to disentangle the sources of variation between replicates. This would not be possible using one dimensional models where root length density is a function of depth since these functions would be very similar between replicates in this study, see Fig. Root system architecture and the distance between the pellet and the roots play a more prevalent role in high buffering soils, as the active region of root uptake and uptake variability is higher under these buffering conditions Fig. . However8. In the zero fertiliser case, the root system with the highest surface area had the highest initial uptake rate, , more so than for total uptake. However, the traits that are most beneficial to uptake evolve over time, which could be easily overlooked on shorter term simulationste, Fig.  sample 039, this should also be balanced with carbon investment40. This observation is important because optimal root traits may depend on which factor is yield limiting in a particular agricultural scenario. For example, growing corn in northern Europe may be limited by light but not P availability, thus photosynthate should be spent on above ground organs to maximise yield. In this case, assuming the soil has plenty of available P, high P uptake efficiency is preferable to total P uptake as this will allow greater investment in above ground material, which functions to increase carbon capture and therefore yield40. In this scenario, the sample 02 root system which has high uptake efficiency is optimal. On the other hand, where low P availability is the limiting factor rather than light, root systems which have high total P uptake would be preferable and the sample 01 root system is more optimal.In addition, the root traits that are beneficial for uptake efficiency (greater exploration) may not be beneficial for total P uptake. For example, in the fertilising cases sample 03 has higher total P uptake than sample 02 in the low buffering case but the worst uptake efficiency Figs.  and 7. T42. The roots we measured from the CT images were only those roots larger than an absolute minimum of 180 µm, due to the limitations of XCT imaging on samples at this scale. Therefore, fine roots and certainly root hairs were missed in these analyses, and these roots are likely to be those that would respond to localised P since they are less costly to produce in terms of carbon42. The advantages of XCT imaging for maintaining geometric information e.g. about root distribution vs clustering outweigh the disadvantage in losing fine roots for this study, though future work should investigate the role of lateral roots and root hairs further. Soil moisture was assumed constant in the simulation, an assumption that is more relevant to laboratory experiments, where soil moisture is controlled, than to crops in the field. However, P movement is often assumed to be dominated by diffusion due its rapid sorption onto soil particles, and ignoring advection is a common simplification for P models43. Although the validity of this assumption for P has been brought into question by the recent study of Petroselli et al.11, who found evidence of advection when measuring P concentrations at increasing distance from a fertiliser pellet using microdialysis. However, the results were more likely explained by the advection of fertiliser particulates rather than of P in solution, which is outside the scope of this study. Including soil moisture equations to monitor the advection of P by water flow was not feasible in the current study due to the size of the computational mesh. Ultimately, there was a trade-off between the detail of the root systems and the complexity of mathematical model; we did not have available memory to solve both P advection–diffusion and soil moisture with Richards’ equation while using large and detailed model domains in reasonable time.All the comparisons made in this study are between the same species and variety of plant, grown under the same conditions. As such, the study provides better controlled protocols than any field experiment can guarantee. Despite these controls, results highlight large variabilities. Given the variability seen even under these controlled conditions, we expect much greater differences would be observed between different species, varieties and environments. In addition, plants have many different strategies to increase P uptake that are not considered by our present model, including organic-acid exudation, mycorrhizal symbioses and growing fine lateral roots and root hairsIn conclusion, our spatially explicit and time-resolved model allowed investigation of the relative roles of root architecture, fertiliser dissolution rate, and soil buffer power in determining P uptake by a plant. More specifically, faster dissolution led to a higher short-term uptake rate, but we found diminishing returns with fertilisers dissolving within one week. In addition, while greater total root length increased total P uptake, smaller, more spread out root systems took up more P per unit root length and therefore may be considered more efficient, and the specific amount of root investment required might depend on variables such as availability of P, and availability of light and water. Monitoring over an extended time period is important since uptake can change through time, though early P uptake is known to be crucial for high yields. Finally, explicitly including the spatial distributions of the root systems highlights that the optimal balance of root exploration and root density is different in different soils with different buffer powers. These results provide the foundation for a predictive modelling approach to understanding uptake from different P fertilisers in different soils. This approach can be used to investigate sources of high variability within single cultivars, which is not identifiable under larger experimental conditions but is important for understanding the underlying processes rather than only average responses. In addition, it could be a highly valuable tool in rapid screening of different fertilisation approaches for specific soil properties, root architectures, and fertiliser properties, complementing and potentially informing more comprehensive field studies.Supplementary Figures.


Drosophila genetics and behavioral analyses combined with genome-wide association studies on 578 patients clinically diagnosed with LID. Similar to the therapeutic effect of levodopa in patients, acute levodopa treatments restore the motor defect of Parkinson’s disease model flies, while prolonged treatments cause LID-related symptoms, such as increased yawing, freezing and abrupt acceleration of locomotion. These symptoms require dopamine 1-like receptor 1 and are induced by neuronal overexpression of the receptor. Among genes selected from our analyses in the patient genome, neuronal knockdown of adenylyl cyclase 2 suppresses the levodopa-induced phenotypes and the receptor overexpression-induced symptoms in Drosophila. Together, our study provides genetic insights for LID pathogenesis through the D1-like receptor-adenylyl cyclase 2 signaling axis.Although levodopa is the most effective medication for Parkinson’s disease, long-term levodopa treatment is largely compromised due to late motor complications, including levodopa-induced dyskinesia (LID). However, the genetic basis of LID pathogenesis has not been fully understood. Here, we discover genes pathogenic for LID using  Drosophila model of L-DOPA-induced dyskinesia (LID) reveals that LID is linked to ADCY2 signaling.A combined research approach using GWAS on Parkinson's disease patients and a  The symptomatology of PD is heterogeneously composed of classical motor features of resting tremor, bradykinesia, rigidity and gait disturbance, along with non-motor features3. Despite these complicated symptoms, oral intake of levodopa (L-DOPA) has been the most effective medication for decades to treat the disease4. However, the therapeutic effect by L-DOPA is largely compromised due to the occurrence of late motor complications, termed L-DOPA-induced dyskinesia (LID) caused by the long-term treatment of L-DOPA5. LID represents a spectrum of abnormal involuntary movements (AIMs) including chorea (quick involuntary movements of the limbs), dystonia , and athetosis (involuntary writhing movements)8. The vast majority of PD patients eventually develops one of these LID symptoms several years after L-DOPA treatment and is challenged in their quality of life. Almost ninety percent of PD patients suffer from LID nine years after L-DOPA treatment9. Initial LID symptoms usually occur at the peak level of L-DOPA in the plasma, but as L-DOPA therapy persists, LID may appear even when its level is arising or declining in the form of diphasic dyskinesia10. These suggest that chronic L-DOPA application causes adaptation processes that involve pathogenic changes in the dopamine signaling pathway11. In order to delay the onset of LID, minimal dosing of L-DOPA in combination with carbidopa or late initiation of L-DOPA treatment is taken as an alternative approach12. However, once LID has developed, it is irreversible and termination of L-DOPA treatments worsens the motor symptoms, making LID difficult to control13. Therefore, studying the pathogenic alterations in the dopamine signaling system upon prolonged L-DOPA treatment is imminent to provide therapeutic implications for LID14.Parkinson’s disease (PD) is a common neurodegenerative movement disorder characterized by the progressive loss of dopaminergic neurons in the substantia nigra of the midbrain and 15. Chronic dosing of L-DOPA supplies non-physiological levels of dopamine in the presynaptic terminals, and in turn can override the endogenous capacity of dopamine clearance, leading to fluctuations in dopamine levels that can evoke involuntary motor symptoms16. Abnormalities in dopamine transporters and auto-receptors that regulate conversion and reuptake of dopamine have been shown to correlate with LID in PD patients17. In addition to these presynaptic changes, pulsatile supply of excessive dopamine in the synaptic cleft can cause abnormal activity/expression of dopamine receptors in the postsynaptic neurons, resulting in irreversible alterations in downstream signaling molecules that control transcription and translation14. These molecular alterations in the postsynaptic neurons could eventually impact functional and structural facets of the neurons that govern motor output8. Indeed, a wide range of dysregulations in cellular signaling is associated with LID pathogenesis18. Lines of evidence have shown that D1-like family receptors and their downstream signaling are important for LID pathogenesis19. D1-like family receptors include dopamine receptor D1 (DRD1) and dopamine receptor D5 (DRD5) coupled to Gsα that stimulates adenylyl cyclases (ADCYs) for induction of cyclic adenosine monophosphate (cAMP)20. Dopamine receptor D2 (DRD2), dopamine receptor D3 (DRD3), and dopamine receptor D4 (DRD4) belong to D2-like family receptors that bind to Giα which inhibits cAMP signaling21. In particular, cAMP-associated signaling components, such as ADCYs, protein kinase A (PKA) and dopamine and cAMP-regulated phosphoprotein of 32 kDa (DARPP-32) are implicated in LID22. There have been many attempts to examine the role of these molecules in LID pathogenesis by manipulating them genetically and pharmacologically23. However, natural variant mutations on these molecules linked to PD patients diagnosed with LID have been less understood.There are two different views on the pathogenic mechanism underlying LID: presynaptic vs postsynaptic mechanism in the dopamine signaling systemadenylyl cyclase 5 (ADCY5) are implicated in familial dyskinesia and knocking out of ADCY5 ameliorated the LID symptoms of L-DOPA-treated PD model mice24. Considering varying LID responses to L-DOPA treatment, there could be risk factors that genetically make PD patients vulnerable to LID pathogenesis25. In an effort to identify such a genetic factor, we established a large collection of single nucleotide polymorphisms (SNPs) from PD patients  who developed LID. To screen one that is related to LID symptoms, we sought a model organism that enables a high-throughput screen with preserved motor symptoms upon L-DOPA treatment. In Drosophila, artificial supplement of dietary L-DOPA rescues the motor symptoms of flies that lack dopamine synthesis pathway in the nervous system27. L-DOPA treatment produces therapeutic effects on the motor deficits of a PD model fly line, demonstrating that L-DOPA administration impacts fly motor control via dopamine signaling system28. Furthermore, dopamine pathways are preserved anatomically and molecularly in the fly with simplicity and accessibility permitted by fluent genetic tools29. Thus, Drosophila provides a highly tractable system for studying the genetic mechanism of L-DOPA-induced motor symptoms.Autosomal dominant mutations on Drosophila genetic tools to investigate LID pathogenesis. First, we established a behavioral paradigm to analyze L-DOPA-induced alterations in fly locomotion. Then, we characterized three components in fly locomotion, speed, yawing and freezing that are statistically correlated with the dose and duration of L-DOPA treatment. In particular, flies with L-DOPA treatment showed abrupt peaks of accelerated movements that deviated from the nearest time points. Using a scoring algorithm, we defined the peaks as AIMs, indicative of LID along with yawing and freezing behaviors. Remarkably, prolonged L-DOPA treatment markedly elevated AIM scores in PD model flies, while acute L-DOPA treatment improved the motor defects of the flies with no sign of AIMs. Neuronal knockdown of dopamine 1-like receptor 1  suppressed LID-like symptoms involving pathological elevation of AIM scores, whereas neuronal overexpression of Dop1R1 was sufficient to trigger the LID-like symptoms even without L-DOPA treatment. Using the fly genetic tools, we performed an RNAi-based genetic screen on the conserved fly genes that were selected from the SNPs of PD patients diagnosed with LID. The screen revealed that knockdown of Drosophila adenylyl cyclase 2 (dADCY2) blocked L-DOPA-induced pathological symptoms. Interestingly, the LID-like symptoms caused by neuronal Dop1R1 overexpression were also completely suppressed by knockdown of dADCY2. Based on these data, we propose that the D1-like family receptor-ADCY2 signaling axis underlies LID pathogenesis.Here, we combined genome-wide association studies (GWAS) on LID-diagnosed PD patients with 4. We asked behavioral specificity induced upon L-DOPA diet by examining flies on a diet with dextrodopa (D-DOPA), the biologically inactive isomer of L-DOPA. We observed that behavioral abnormalities were caused by L-DOPA, but not by D-DOPA , the angular difference between the two orientations (θ), and the fraction of time with zero distance moved (d = 0) during the total time period (t0 to t1), respectively  as a well-established LID parameter34. To establish the Drosophila version of AIM, we sought to characterize the abruptly accelerated fly movements that are highly irregular and deviated from the regular movement at the near time point upon L-DOPA treatment. Using computational analysis, we systematically calculated instantaneous speeds and deviations in the fly movement, and indeed found that L-DOPA diet caused abrupt accelerations of fly movements upon making a directional change associated with larger angular changes  were divided by mean speeds (vs) within certain time periods defined as windows (s). The sliding window was used to obtain serial values of vi over vs (vi/vs) within the entire recording time (n). Then, the serial vi/vs values were logarithmized to examine whether a given log(vi/vs) was qualified for a threshold value (c) that had been previously determined by WT flies on control diet. The AIM score (H) is the sum of 0 or 1 given to each frame  during the entire recording based on whether the value of log (vi/vs) at a frame qualifies the threshold value (c). If the value of log (vi/vs) is larger than c, 1 is given to the frame. If the value of log (vi/vs) is smaller than c, 0 is given. For example, if a fly showed the value of log (vi/vs) larger than c at seven different frames, the AIM score of the fly is 7. For better visualization of data, the AIM score of control flies is normalized to be 0, where the threshold value (c) is 0.4  are well-established PD model animals, recapitulating the primary symptoms of PD35. To monitor PD symptoms affected by L-DOPA, we designed a four-day video-recording experiment intercalated with one-day L-DOPA diet sequences  without L-DOPA diet showed retarded movements and speed with minimal changes in AIM scores comparable to revertant flies (PINK1RV)  Fig. . On day res Fig. . Howeversis Fig. . Upon reies Fig. . Reversipes Fig. .Fig. 3AcPINK1B9 flies on day 0 were normalized, while control flies developed LID-like phenotypes with an increase in AIM scores upon L-DOPA diet on day 1 , and Dop2R37. We decided to use n-Synaptobrevin-GAL4 (nSyb-GAL4) to drive the expression of UAS-Dop1R1 RNAi and UAS-Dop2R RNAi in synaptically mature neurons. Neuronal knockdown of Dop1R1 expression not only reversed L-DOPA-induced motor defects to normal state, but also decreased AIM scores in both acute and prolonged L-DOPA diet  and the age of onset (>50 years), five hundred seventy-eight patients were selected for GWAS , and sorting of SNPs within protein-coding genes and open reading frames reduced the number of SNPs down to one hundred seventy four. These SNPs were then analyzed for their genetic loci on the human genome, leading to identification of one hundred seventy-one genes potentially correlated with LID , and calcium/calmodulin-dependent protein kinase II gamma (CAMK2G) were mapped downstream of the dopamine receptors. In particular, ADCY2 and PLCB1 were direct targets of D1 type receptors depending on the types of coupling with G-proteins. Dopaminergic synapse pathway predicts various pathogenic routes via the postsynaptic signaling system including synaptic vesicle cycle, calcium signaling, glutamatergic synapse pathway LID Fig. . With ceLID Fig. . To gainLID Fig. . Based oLID Fig.  and 7. ILID Fig. . To asseLID Fig.  and 8. TLID Fig. . LAGs inway Fig. 40–42. ThLID Fig. .Fig. 5dADrosophila genome and association with nerve and muscle, nineteen genes were chosen for test in the fly LID behaviors  increased baseline movements both in control and L-DOPA diet, and strongly suppressed the pathological AIM increase , dADCY2, Ac3 (dADCY3), CG43373 (dADCY5), Ac78C (dADCY8), and Ac13E (dADCY9)  and independent of Dop1R1. Together, we identified four functionally distinct groups of ADCYs in response to L-DOPA resembling the phylogenetic categorization . We have demonstrated that the hyperkinetic movements were deviated from the regular movements at the near time period and were manifested in an uncontrolled fashion (involuntary), reminiscence of AIMs in LID. To model fly AIMs, we developed a computational algorithm that enables systematic quantification of AIM scores from fly trajectories. Remarkably, prolonged L-DOPA diet induced the pathological increase of AIM scores in PD model flies, while acute L-DOPA diet improved the motor abnormalities of PD model flies with no sign of AIMs. We found that either neuronal knockdown or whole-body knockout of Taken together, we propose that the D1-like family receptor-ADCY2 signaling axis in the postsynaptic dopamine neurons slows down locomotor speed in normal situations. Upon L-DOPA diet, non-physiologically high levels of dopamine are synthesized by L-DOPA-dopamine conversion pathway in the presynaptic neurons. This can lead to pathogenic levels of D1-like family receptor stimulation on the postsynaptic terminals, which in turn hyper-stimulates ADCY2. Hyper-stimulation of ADCY2 results in exaggerated signaling in the web of downstream molecules and eventually, it alters the function and structure of the postsynaptic neurons to cause LID. In the model, we propose that dysregulation of the D1-like family receptor-ADCY2 signaling axis underlies LID pathogenesis Fig. .Fig. 8A Drosophila larvae, providing another useful model system to study LID32. In the study, the authors generated a PD model of Drosophila larvae expressing human mutant α-synuclein (A53T) and characterized a body bending behavior upon L-DOPA feeding, which corresponds to axial dyskinesia in the rodent model of LID. In our study, we identify conserved Drosophila genes pathogenic for LID using combination of GWAS on LID-diagnosed PD patients, and genetic studies on the adult Drosophila model of LID. Our unbiased searches of natural variant mutations on the patient genome allowed us to identify LID-associated genes (LAGs). Then, we crafted a computational analysis of the Drosophila locomotion to capture motor traits that resemble the symptoms of the rodent model of LID including the AIMs. By examining the conserved LAGs in the fly LID paradigm, we identified ADCY2, whose role in LID has not been previously reported. The approaches used in this study offer three main implications for the field. Our Drosophila model of LID provides a quick and reliable in vivo platform to evaluate the LID motor symptoms from various angles by using fluent genetic tools to manipulate each of the molecular and cellular components of the dopaminergic system. Our GWAS establishes a patient genomic dataset that lists natural variants potentially associated with LID, allowing us to investigate additional molecules involved in the pathogenesis of LID. Our collaborative study establishes a framework for the combined research on LID pathogenesis between clinical data and mechanistic studies using animal models.LID is classically studied using rodents and primates. Recently, however, a study has reported the characterization of L-DOPA-induced motor abnormalities in a simpler model organism such as the ADCY2 in PD patients? It is conceivable that the mutation on ADCY2 induces pathological changes in ADCY2 expression/activity in the patient brain. For example, a gain-of function mutation on ADCY5 with increased activity was shown to correlate with familial dyskinesia48. Intriguingly, studies show that striatal ADCY5 levels were elevated upon L-DOPA treatment and knocking out of ADCY5 ameliorated L-DOPA-induced pathological symptoms in the PD model mice24. Alternatively, in our study, the ADCY2 mutations could induce pathogenic effects above the threshold set by pulsatile dopamine fluctuations during the course of L-DOPA treatment. In this case, we may need to monitor changes in ADCY2 expression/activity before and after L-DOPA therapy to capture the pathological changes. It would be difficult to address all these questions directly in humans, but one way to test could be to design a mutation mimicking the SNP on the ADCY2 locus in animal models. In vivo and in vitro examination of the expression/activity of the mutant ADCY2 could provide mechanistic insight into how the SNP on ADCY2 influences LID pathogenesis.Our study raises some open questions. What is the pathogenic mechanism underlying the occurrence of LID by the SNP on PINKB9 flies with the mild AIM score upon the prior L-DOPA diet displayed a somewhat reversed phenotype during the non-L-DOPA diet  of the mutant fly, reversibility here may represent the therapeutic effect during the early treatment, and the mild AIM score from the L-DOPA treatment may indicate a very early phase of LID progression. It is generally accepted that once LID develops, it is difficult to reverse the process. Low dosing of L-DOPA can be an option, but the dyskinetic symptoms may still persist. Pulsatile stimulation of the receptors in the dopaminergic pathway is thought to underlie the abrupt appearance of dyskinetic symptoms6. This offers a clinical opportunity that L-DOPA dosing complemented with pharmacological intervention of the receptors underlying the pulsatility may be a way to reverse the symptoms. Interestingly, a recent study reported that viral delivery-guided inhibition of CaV1.3 channels in the striatum reversed the dyskinetic symptoms of animal models with severe LID. Importantly, the motor benefit from low dosing of L-DOPA was still preserved with the inhibition of the CaV1.3 channels, suggesting that this ion channel may underlie the reversibility of LID pathogenesis, independently of the dyskinesia-inducing mechanism51.A portion of iet Fig. . We want52. Depending on which upstream receptors and downstream signaling components are coupled, how strong the signaling cascade persists, and which cells they are expressed in, there could be temporal and spatial dimensions in which these ADCYs interplay in mediating LID pathogenesis. Mammalian ADCY5 is the best studied ADCY member for its role in a variety of tissues and physiologies54. ADCY5 is implicated in a protective role in the heart, including responding to pressure overload and cardiac stress through the ADCY signaling in the cardiomyocytes54. ADCY5 knockout increases longevity in mice via activation of Raf/MEK/ERK signaling pathway and upregulation of protective molecules in fibrocytes53. ADCY5 in the striatum neurons responds to L-DOPA treatment and has a protective role in the pathogenesis of LID in mice when knocked out56. ADCY3 mediates the rhythmic control of the morning anticipatory response by functioning in a subset of clock cells in the Drosophila brain45. Likewise, ADCYs mediate diverse functions in tissues with the ability to time their functions. Using a genetically tractable model organism such as Drosophila which permits fluent cell-specific manipulations and recordings of ADCYs, it would be possible to further address the temporal and spatial pattern of ADCYs expression in LID-associated tissues and their function in the L-DOPA-induced motor response.Our data show that each dADCY group is involved in distinct motor controls at multiple time points during L-DOPA treatment. Perhaps, ADCYs engage different cellular signaling machineries in a discrete neuronal subset in response to L-DOPAS6KII and S6KL, orthologous to human ribosomal protein S6 kinase alpha-2 (RPS6KA2) and downstream members of ERK-MAP kinase and mTOR signaling pathways, effectively normalized L-DOPA-induced AIMs , and the Vienna Drosophila Resource Center (VDRC)  cycle at 25˚C with 70% humidity. /12 h cycvi) equals the subtracted value from the traveled distance at the next time point (di+1) to the traveled distance at a time point (di).The instantaneous speed (vs) was obtained from a window size. The interval (s) was 1/2 (s-1), the region before and after a time point (i), and s was defined as the window size. The sliding window was scanned over the entire frame to calculate the serial ratio (vi/vs) within it60.The mean speed (vi/vs (log vi/vs) was then compared to a threshold value (c). c was experimentally determined from WT fly movements on control diet. For better visualization of data, the AIM score of control flies is normalized to be 0, where the threshold value (c) is 0.4.The logarithmic value of H) is the sum of 0 or 1 given to each frame  during the entire recording based on whether the value of log (vi/vs) at a frame qualifies the threshold value (c). If the value of log (vi/vs) is larger than c, 1 is given to the frame. If the value of log (vi/vs) is smaller than c, 0 is given. For example, if a fly showed the value of log (vi/vs) larger than c at seven different frames, the AIM score of the fly is 7.The AIM score , or D-DOPA  dissolved in 10% sucrose for 1 day as acute L-DOPA diet, or for 7 days as chronic L-DOPA diet. Using an oral aspirator, a single fly was carefully transferred into an arena made of a petridish  filled with silicon . To ensure reliable object detection in Ctrax fly tracking program61, two-dimensional movements of the fly were permitted by filling the arena space <2 mm below the top of the arena with silicon. An insulated recording chamber (675 mm × 440 mm × 410 mm) was built with a white LED light on the inside wall and a white table at the bottom under a camera . Flies were acclimated in the chamber for 1 h prior to testing. Exposure, gain, focus, and white balance of the camera were adjusted for optimal object detection. Total trajectories of the fly were recorded for five minutes with 30.06 frames per second. A frame corresponds to 0.033 seconds.Newly born flies were collected under COThe 5 minute-video  was analyzed using Ctrax program, fly movements at every 0.033 s were tracked. The values of speed, angular velocity, acceleration, distance, orientation and direction were obtained using Behavioral Microarray MATLAB toolbox available from Ctrax website. AIM score, mean speed, yawing, freezing rate and trajectory graphs were calculated by modified MATLAB codes.Flies were reared on agar media containing 10% sucrose plus D-DOPA or L-DOPA at 25 °C. Percent of surviving flies was recorded daily when flies were transferred to fresh media.62. Human functional PPI network composed 18,891 genes and 82,657 interactions . The member of genes in the enriched pathway from pathway analysis was obtained using KEGG mapper.Drosophila ADCY family members were obtained from Uniprot. These sequences were then aligned and analyzed using the Clustal Omega software to generate multiple sequence alignments and construct phylogenetic trees.The orthologs of the ADCY family proteins were extracted from HomoloGene (NCBI) and the Pan-taxonomic compara (Ensembl genome) database. The paralog sequences for human and 39. Demographic characteristics of the patient groups were investigated at each stage of genome-wide association study39. Men PD patients were 507 (47.4%) and women patients were 563 (52.6%). The mean age at the onset of PD was 58.7 years old. The mean of disease duration from the onset to the last follow-up was 9.1 years. The group of 741 patients with disease duration over 5 years was composed of 325 men (43.9%) and 416 women (56.1%)  from 1 January 2011 to 30 April 2016. Institutional Review Board (IRB) of Asan Medical Center approved the study, and all subjects provided informed consents in accordance with the IRB regulationswere 507 7.4% and 39. A GWAS flow diagram was provided to describe the number of subjects and analyses performed  test. p < 0.05 was considered statistically significant. Data are presented as mean ± SEM. Statistical analyses were calculated with GraphPad Prism and R Statistics for GWAS were performed as previously stated.All behavioral experiments were independently performed and measured for statistical significance evaluation. Details of quantification and statistical validations were indicated for each experiment in Figure Legends. All Further information on research design is available in the Peer Review FileSupplementary InformationDescription of Additional Supplementary FilesSupplementary Data 1Reporting Summary


Mycobacterium tuberculosis growth inhibitors and demonstrate that our model's visualization modality of attention at the atomic level could be an insightful way to investigate molecular atoms or functional groups associated with desired biological properties. The new model provides an innovative way to investigate the effect of self-attention on chemical substructures and functional groups in molecular representation learning, which increases the interpretability of the traditional MPNN and can serve as a valuable way to investigate the mechanism of action of drugs.Graph convolutional neural networks (GCNs) have been repeatedly shown to have robust capacities for modeling graph data such as small molecules. Message-passing neural networks (MPNNs), a group of GCN variants that can learn and aggregate local information of molecules through iterative message-passing iterations, have exhibited advancements in molecular modeling and property prediction. Moreover, given the merits of Transformers in multiple artificial intelligence domains, it is desirable to combine the self-attention mechanism with MPNNs for better molecular representation. We propose an atom-bond transformer-based message-passing neural network (ABT-MPNN), to improve the molecular representation embedding process for molecular property predictions. By designing corresponding attention mechanisms in the message-passing and readout phases of the MPNN, our method provides a novel architecture that integrates molecular representations at the bond, atom and molecule levels in an end-to-end way. The experimental results across nine datasets show that the proposed ABT-MPNN outperforms or is comparable to the state-of-the-art baseline models in quantitative structure–property relationship tasks. We provide case examples of The online version contains supplementary material available at 10.1186/s13321-023-00698-9. With the rapid development and expanding applications of artificial intelligence (AI) in academia and industry, molecular property prediction has played a fundamental role in the early stage of drug discovery. By training effective computational models and delivering accurate prediction of molecular properties, potential drug candidates were identified from virtual screening libraries of small molecules, thus addressing the intensive monetary investment and time-consuming nature of early stage drug discovery process –3. In thSimilar to convolutional neural networks (CNNs) that learn latent featurization of structural data by conducting convolutional operations, graph convolutional neural networks (GCNs) can generalize the convolutional operation to non-structural data and aggregate global information from local features. As small molecules can be naturally considered as graph data in the computational context, GCNs have been wildly applied to molecular property prediction tasks and have achieved remarkable success , 5. The The integration of the self-attention  mechanisGiven the strengths and various successful practices of the Transformer models, several previous works have augmented self-attention to GCNs, whereas the majority of them employed the self-attention mechanism during the node (atom) embedding –12. For In this work, we propose an Atom-Bond Transformer-based Message-passing Neural Network (ABT-MPNN), in which we adopted additive attention and scaled dot-product attention to the MPNN framework at both bond and atom levels, respectively. The additive attention  is an atThe novelty of this model can be summarized as follows: i) our work integrates the additive attention and the scaled dot-product attention into graph-based models and highlights the effect of self-attention on both atoms and bonds of molecules; ii) we introduce the Coulomb matrix to the network and design a feature-engineering scheme in which each attention head only comprises one type of scaled feature matrix in addition to the trained attention weights. This improvement is inspired by the Molecule Attention Transformer (MAT) proposed by Maziarka et al. , where aWe conduct a brief description of the preliminaries related to this work, including several graph-based molecular representations, message-passing neural networks, as well as the attention mechanism and Transformer.A graph Various chemical properties can be calculated for atoms and bonds of molecules. The extracted atom and bond features are usually mapped into two-dimensional data arrays that can be easily handled by computers . SpecifiAdjacency and distance matrices are two graph representations of molecules that contain the information of connectivity and distance for each pair of atoms, respectively. For an adjacency matrix, entries are set to 1 if chemical bonds exist between the corresponding atom pairs while nonbonded atom pairs are denoted with 0. In contrast to this binary definition of bonding, a distance matrix depicts the topological distances of atoms. For each molecule, a distance matrix is based on the molecular conformation and is calculated according to the 3D coordinates of atom pairs.The Coulomb matrix proposed by Rupp et al.  is a molThe elements on diagonal v are updated at t + 1 according toThe MPNN proposed by Gilmer et al.  is anoth(D-MPNN)  engineerThe Transformer , a new dThe Transformer network  is builtInstead of calculating a single attention function to the queries, keys, and values, the Transformer uses multi-head self-attention, where multiple attention functions are performed in parallel and then projected to form the overall output. Specifically, for each attention head  is shown in Fig. More concretely, molecules represented by the simplified molecular-input line-entry system (SMILES) are first entered into the featurization step, and node features . The growth inhibition property of a molecule on each M. tuberculosis mutant strain was gauged by the statistical test (Z-score) obtained from the experimental results [M. tuberculosis mutant strain. We later clustered the chemical-genetic interaction profiles in gene clusters by first identifying M. tuberculosis H37Rv homologs in Escherichia coli K12 according to their gene products. Then, the semantic gene similarity of biological process for the homologs were calculated and hierarchical clustering was performed [M. tuberculosis gene groups were formed, and the target value for each gene cluster was obtained by finding the median Z-score of the genes in that cluster. Besides training regression models with continuous Z-scores, we built binary classification tasks for each of the 13 gene clusters with a class criterion equal to -4, where Z-score < −4 was considered growth inhibitory or active (1), or otherwise inactive (0). For this dataset  Random forest (RF)  with binThe RF was implemented with 500 trees based on binary Morgan fingerprints  on the partitioned data splits and reported the mean and standard deviation of the metrics. The ABT-MPNN used PyTorch  as the dWe compared our proposed ABT-MPNN with 6 baseline models on 10 classification and regression tasks, covering chemical-genetic interaction profiles  andRegarding regression tasks, we observed that the ABT-MPNN model achieved substantial improvements over classification, as it consistently outperformed all baseline models according to the results of the fivefold CV. The outstanding performance of ABT-MPNN on regression datasets could be associated with the modeling of inter-atomic attention with topological and electrostatic features, as regression tasks focus on linking quantum chemical properties to molecular structures, in which such information is of high relevance. In regression tasks of the Johnson et al. dataset, the ABT-MPNN model improved upon D-MPNN by a modest margin of 0.15%, and it boosted the results of QM8 with a 10% relative MAE optimization compared to MPNN, D-MPNN and GEM. Moreover, our model yielded superior results in RMSE compared to the second-best baselines on ESOL (1.57%), Freesolv (1.42%), and Lipophilicity (0.72%), respectively.Overall, ABT-MPNN achieved state-of-the-art results on 9 out of 10 classification and regression tasks according to the fivefold CV, showing the robustness of the molecular representation learned by our model. The superior performances across multiple datasets compared to D-MPNN further support the effectiveness of complementing the directed message-passing scheme with the bond and atomic level attention.To validate the impact and contribution of each component to the performance of the proposed ABT-MPNN, we conducted a series of ablation studies on both classification (ClinTox) and regression (ESOL) datasets from our benchmarks. For each run, we kept the same hyper-parameter settings, and the performance was evaluated on the same fivefold CV, as is shown in Table One of the most important distinctions between ABT-MPNN and previous works is the integration of bond-level attention during the message-passing phase. In ABT-MPNN, we chose Fastformer  as the bAs introduced in the methods section, we constructed atom-level self-attention with Transformer and incorporated additional adjacency, distance, and coulomb matrices into each attention head as auxiliaries. We analyzed the choices and optimizations at the atom embedding phase Table  in experFinally, we evaluated the effect of using bond attention and atom attention to justify our architecture design. Specifically, model #7 Table  is the cBesides assessing the model’s performance, it is often beneficial to look into the “black box” of the trained model and have a deeper understanding of which substructures of molecules contribute more to the compound activities/properties. With the interpretability of attention weights of atoms, it is possible to investigate the latent linkage between the molecular substructure and the predicted outcomes. Here, we visualize the atomic attention weights using the similarity map   biochemical validation of the molecular targets. Specifically, Nisa et al. [M. tuberculosis. Amsacrine is an antineoplastic agent that has been shown to inhibit mycobacterial TopA, the essential topoisomerase I involved in mycobacterial cell viability [M. tuberculosis protein synthesis inhibitor [LeuS gene. Since we added an adjacency matrix to the Figure lothepin ; Amsacrilothepin ; Compounlothepin ) curatedlothepin . The coma et al.  reportediability . Compounnhibitor , can forM. tuberculosis might be associated with its sulfonamide functional group, in agreement with the suggested interaction between the sulfonamide moiety and the mycobacterial topoisomerase I TopA [First of all, we observe that the atom attention layer only focuses on a few atoms or substructures of the molecule and different attention branches have different “views” of the input. For instance, the weights in the e I TopA .In this study, we proposed a novel message-passing framework called ABT-MPNN that incorporates both additive attention and scaled dot-product attention at the bond and atomic levels, respectively. To incorporate the topological and electrostatic information of molecules into the model, we further designed a feature engineering scheme that embedded adjacency, distance and Coulomb matrices derived from molecular conformations with each atom attention head.M. tuberculosis inhibitors, we highlighted the effect of self-attention on chemical substructures and functional groups during molecular representation learning, which not only increases the interpretability of the MPNN but also serves as a valuable way to investigate the mechanism of action.Overall, our proposed model consistently outperformed or is comparable with the state-of-the-art baseline models on a wide range of molecular datasets. By introducing the attention schemes at the atomic level, we realized the visualization modality of the model via the predicted probability map. Through the demonstration of the three Additional file 1:Table S1. Atom and bond features. Table S2. 200 Molecular descriptors generated by RDKit. Table S3. Algorithm of Bond Attention. Table S4. Algorithm of Atom Attention. Fig. S1. Comparison of ablation experiments using 5-fold cross-validation (A) Performance evaluation of each fold for the classification task (ClinTox) measured with AUROC. Experiments settings: #1: baseline; #2: use bond attention (Transformer); #3: use bond attention (Fastformer); #4 use atom attention; #5 use atom attention with inter-atomic matrices #6 use bond attention (Fastformer) and atom attention; #7 use bond attention (Fastformer) and atom attention with inter-atomic matrices (B) Performance evaluation of each fold for the regression task (ESOL) measured by RMSE. The settings of each experiment in the regression task are identical to those in the classification one.


Kiwi starch (KS) is a fruit-derived starch; in order to improve its processing performance and increase its added value, it is necessary to modify KS to enhance the positive attributes and to enlarge its application. In this study, KS was modified by high-power ultrasound treatment (HUT) to reveal the relationship between the structure and function of KS with different treatment powers  and different treatment times . The results showed that HUT destroyed the granular morphology of KS, formed holes and cracks on the surface, and reduced the particle size and the short-range molecular order of KS. After different HUTs, the apparent amylose content (AAC), swelling power (SP), water solubility index (WSI), viscosity and setback value (SB) of KS were significantly increased, while the gelatinization temperature was significantly decreased. In addition, HUT significantly reduced the content of rapidly digestible starch (RDS) and slowly digestible starch (SDS), while it significantly enhanced the content of resistant starch (RS) (64.08–72.73%). In a word, HUT as a novel physical modification method for KS, enlarged its application, and fulfilled different demands of a starch-based product, which introduces another possibility for kiwi fruit further processing. Diverse approaches such as chemical, enzymatic, or physical modifications are used to improve the features of native starch to expand its applications in the food industry. However, the chemical modification is often polluted and easily generates the residual, which has certain adverse effects on the human body. Enzymatic modification is easily restricted by temperature, pressure, pH, and salt ions, and it is hard to control the reaction process 2 is called high-power ultrasound treatment (HUT), which has been gradually applied to starch modification The physical effects  and/or chemical effects of cavitation determines the results of ultrasonic modification in vitro digestibility of KS. Additionally, it discussed the relationships between molecular structure and various properties of KS. The study focused on the green preparation of high-performance modified KS, accumulated relevant data for the wide application of ultrasound technology, and advanced starch technology and ultrasonic chemistry to a certain extent.Considering the positive effects of UST or HUT on physical modification, this study aimed to explore the effects of different ultrasonic powers  and ultrasonic times  on the structural, physicochemical and functional properties, and 22.1Pectinase (500 U/mg); cellulase (50 U/mg), amylose (potato source), amylopectin (corn source), 2,4,6-tripyridine-s-triazine (TPTZ),α-amylase (bacillus subtilis source), and glucose determination kit (glucose oxidase method) were all purchased from Shanghai yuanye Bio-Technology Co., Ltd. Porcine pancreatic α-amylase (23 U/mg); pancreatic lipase (57 U/mg); invertase (≥300 U/mg); glucosidase (8 × USP) were all purchased from Sigma-Aldrich Co. Ltd. .2.2Huayou kiwifruits, 100 kg, with a fruit hardness of 90–100 N and soluble solid content of 6.5–8.0 Brix° were picked during the commercial picking period. Kiwifruits were peeled manually and the seeds were removed. 20% ice water (w/w) was added, and the pulp was obtained by a high-speed blender. The pH of the pulp was adjusted to 3.5–4, then, 0.2% pectinase was added and placed at 45–50 °C for 2 h. The precipitate was collected by vacuum filtration, washed 3 times with distilled water, and dissolved in water again. Then the pH was adjusted to 4.5–5.5, 0.2% cellulase was added, and it was placed at 45–50 °C for 2 h. The precipitate was collected as above, then dried in an oven at 30 °C. Finally, KS was milled, sieved, and collected (200 mesh). 3% NaOH solution was to adjust pH.2.32) for 30 min, 400 W (16.84 W/cm2) for 30 min, 600 W (25.27 W/cm2) for 30 min, 600 W (25.27 W/cm2) for 20 min, and 600 W (25.27 W/cm2) for 10 min. HUT were conducted using an ultrasonic probe processor  with a probe  and a constant temperature water bath .According to Ding et al. (2019) 2.42.4.1KS granules were observed using a SEM . All samples were observed at a magnification of × 1000 and × 3000.2.4.2KS dispersion, 2% (w/v), was observed using a PLM .2.4.3PSD was performed using an LS13320 laser particle size analyzer . KS, 100 mg, was suspended in deionized water, dispersed by ultrasonic wave, and used to determine the PSD.2.4.4XRD was performed by a Bruker D8 Advance A25 X-ray diffractive analyzer (Germany) 2.4.5An FTIR spectrometer  was used to analyze the change in functional group. The KS sample was loaded on the ATR plate 2.52.5.10, DW) was dissolved in 10 mL deionized water and heated at 45 °C、55 °C、65 °C、75 °C、85 °C, and 95 °C for 30 min separately. During heating, the samples were oscillated for 5 s every 2 min. Then the samples were cooled to 25 °C immediately and centrifuged at 3000 × g for 30 min. The precipitate was precisely weighed (Ws), and the supernatant was decanted and dried to a constant weight (W1). The values of WSI and SP were measured as followed:The SP and WSI determination were based on the method by Zhang, Li, Wang, Yao, & Zhu (2017) with some changes 2.5.2g for 30 min, and the liquid was poured out A 1 g (DW) KS was put into a 25 mL pre-weighed centrifuge tube, and 10 mL peanut oil  was added to it, stirred for 10 min, stood for 30 min, centrifuged at 2200 × 2.5.3According to our previous study 2.5.4The gel texture determination was assessed using a TA-XT plus texture analyzer . KS dispersions of 20% were heated at 95 °C in containers for 30 min with shaking and stored at 4 °C for 24 h to prepare a homogenous gel structure. A P/0.5R probe was used, the test distance was 5 mm, the probe speed during the whole test was 1 mm/s, the force induction was 5 g, and the interval between two tests was 5 s 2.5.5According to our previous study 2.6Starch content, apparent amylose content (AAC), the TPC determination and the antioxidant capacity (by DPPH method and ferric reducing antioxidant power (FRAP) methods) were all based on the methods by Wang et al. (2021b) 2.7g for 5 min. The supernatant was to measure the glucose content.According to Wang et al. (2021b) 2.8p < 0.05 was considered statistically significant. Figures were mainly drawn by Excel 2016 and Origin 9.1. The correlation test was performed by OriginPro 2020 .Analysis was performed using SPSS 23, and the data were expressed as the means ± standard deviations (SD). Each sample was prepared and analyzed in triplicate. One-way ANOVA, followed by the Duncan post hoc test, was used to determine the significant differences. 33.13.1.1Morphological characteristics of KS granules treated with HUT are shown in The effects of ultrasound are mainly dependent on the physical effects and/or chemical effects of cavitation 3.1.2The PSD curve and the average particle size results of KS after HUT were shown in It could be seen from 3.1.3The crystal structure of starch granules generally uses XRD. XRD patterns of KS proved that native KS presented a typical B-type structure, and its main peaks were located at 15.1°, 17.1°, 22.2°, and 24°, but with the increase in ultrasonic power and the prolongation of ultrasonic time, KS gradually transformed to C-type  B1.Fig. As a natural polymer, the crystallization behavior of starch is usually complex. It can be seen from 3.1.4−1 (R1047/1022), because the range of 1200–800 cm−1 of FTIR spectra were sensitive to changes in the short-range order structure 1047/1022 showed regular changes with the change in ultrasonic parameters. The R1047/1022 of CK was 1.0176, while that of KS decreased successively with the increase in ultrasonic power and time, and the R1047/1022 of  was 1.0048 . Falsafi et al. (2019) also reported this phenomenon in oat starch As shown in 3.2.2p < 0.05), this was consistent with the conclusion obtained by Singh & Sharanagat in elephant foot yam starch Oil is mainly stored in the starch network through physical embedding, and OAC is an effective way to detect the emulsification ability of starch 3.2.3p < 0.05), all showed a trend of moving to low temperature. Wang et al. (2020) also found that ultrasonic treatment could move To, Tp, and Tc of sweet potato starch to low temperature p > 0.05), which was different from previous studies. For example, Wang et al. (2020) reported that ultrasonic power reduced the ΔH of sweet potato starch DSC was used to measure the temperature parameters of KS after different HUT, including To, Tp, Tc, and ΔH, which were 63.70–66.96 °C, 67.10–70.40 °C, 73.40–76.45 °C and 11.13–11.77 J/g, respectively . Compare3.2.4p < 0.05), and with the increase in ultrasonic power and ultrasonic time, these values all first increased and then decreased, and reached the maximum at  and . Wang et al. (2021c) also observed a similar phenomenon in rice starch p < 0.05). In addition, HUT significantly reduced the gelatinization temperature of KS (p < 0.05), which confirmed the conclusion drawn by DSC (The RVA gelatinization results of KS after different HUTs were shown in n by DSC . The cav3.2.5p < 0.05). Similar to the hardness, HUT significantly improved the gumminess and chewiness of starch gels (p < 0.05) (TPA model was used. The hardness of 20% (w/v) KS gel was 216.13–441.69 g, the springiness was 0.98–1.00, the cohesiveness was 0.50–0.65, the gumminess was 110.62–277.13 g, the chewiness was 110.48–298.15 g, and the resilience was 0.13–0.32 . Hardnes < 0.05) . All the3.2.6Thixotropy is one of the important rheological properties of water-soluble polymer solutions, and its influence on food taste is reflected in the refreshing and soft feeling. As shown in A temperature scan was performed on the 20% starch paste. In the range of 25 °C–60 °C, G' and G'' were basically stable, then G' and G'' raised sharply and reached the maximum value after 60 °C. However, when the temperature continued to rise, G' and G'' had a downward trend. In addition, compared with the CK  A2, the 3.33.3.1p < 0.05) and increased with the increase in ultrasonic power and time, with the maximum increase of 70.19% , the AAC of KS after HUT was significantly increased (f 70.19% . This wap < 0.05), and there were significant differences between different ultrasonic powers and ultrasonic times (p < 0.05). Among them, 600 W at 30 min had the most significant effect, reducing the TPC from 5079.53 μg GAE/g to 3453.80 μg GAE/g. This might be due to the destruction of the starch granule structure caused by HUT, the release of soluble phenolic compounds, and the dissociation of bound polyphenols Zhang et al. (2020) found that the antioxidant capacity of kiwifruit was mainly related to phenols 3.3.2p < 0.05). The DPPH and FRAP decreased by 32.01% and 43.15% after HUT . Therefore, on the one hand, HUT significantly weakened the antioxidant capacity of KS, which might be attributed to the release and reduction in antioxidant substances (mainly phenols) after HUT. On the other hand, it effectively improved the purity of KS and removed the other substances except starch.As shown in 3.4p < 0.05). In particular, 200 W at 30 min increased the content of RS by 26.07%, up to 72.73%, while it decreased the content of RDS (7.78%) and SDS (19.49%) by 34.51% and 35.93%. These results indicated that HUT increased the enzymatic resistance of starch and increased the difficulty of starch digestion. However, with the increase of ultrasonic power and ultrasonic time, the digestion process and composition changes of KS did not show obvious regularity. It might be because HUT degrades KS molecules and partially destroys the double helix structure, causing the disorder of the starch structure, and the physical effect of ultrasound increases the internal temperature of KS, which leads to the reassembly of the starch chain to form a new helix. The newly formed helix may have a highly ordered structure that could lead to resistance to enzyme digestion, which is consistent with the results obtained by Ding et al. (2019) in retrograded starch According to the digestion behavior under the action of enzymes, starch can be divided into three types: RDS, SDS and RS 3.5To = 0.75, RTp = 0.77, and RTc = 0.76), indicating that different methods had consistency in the characterization of properties. The AAC of KS was positively correlated with BD (R = 0.93) and was negatively correlated with PT (R = −0.88), Ptime (R = −0.84), To, Tp, and Tc  but had almost no correlation with RDS (R = −0.38), SDS (R = −0.18), and RS (R = 0.25). It showed that AAC and particle size determined the gelatinization performance of starch to a certain extent, but had little effect on its digestibility. RC was positively correlated with R1047/1022 (R = 0.96), which jointly characterized the structural order of KS, and RC was positively correlated with To, Tp, and Tc , indicating that the higher the RC, the harder KS was to gelatinize. In addition, studies have reported that the antioxidant capacity of kiwifruit is mainly due to the phenolic compounds present in samples DPPH = 0.97, RFRAP = 0.83). The increasing or decreasing antioxidant capacity might be attributable to the enhancing or reducing of phenolic components. In KS, TPC was positively correlated with many structural indexes such as R1047/1022 (R = 0.97), RC (R = 0.94), and PSD (R = 0.82), indicating that phenols greatly affected the structure of KS and then affected the functional properties of KS.Plenty of studies have shown that some properties of starch are closely related to its structure. For example, the SP of starch granules is considered to be a characteristic of amylopectin 4In this study, KS was modified by HUT for the first time. It was found that the native KS presented a typical B-type structure; with the increase in ultrasonic power and ultrasonic time, KS gradually transformed to C-type, and HUT destroyed the granular morphology of KS and formed holes and cracks on the surface of starch. In addition, HUT significantly reduced the particle size, the RC, and the short-range order of KS. In contrast, the WSI, SP, and OAC of HUT-modified KS were significantly higher than that of native KS. In terms of pasting properties and gel texture properties, HUT might destroy the ordered double helix structure of starch and reduce the number of crystals, thus reducing the gelatinization temperature of KS. HUT significantly enhanced the granular properties of KS. On the whole, G' and G' of KS had an upward trend due to ultrasonic modification, which showed obvious elastic characteristics. Meanwhile, HUT significantly increased the content of AAC and RS, while all HUT significantly reduced the TPC and antioxidant capacity of KS; however, it effectively improved the purity of KS. The research provides a theoretical basis for reasonably changing the function of KS and expanding its industrial application.Jiaqi Wang: Conceptualization, Methodology, Software, Investigation, Data curation, Writing – original draft. Xinran Lv: . Tian Lan: Data curation, Writing – original draft. Yushan Lei: Visualization, Investigation, Supervision. Jiangtao Suo: Writing – review & editing, Writing – review & editing. Qinyu Zhao: Software, Validation. Jing Lei: Writing – review & editing, Writing – review & editing. Xiangyu Sun: Writing – review & editing, Writing – review & editing. Tingting Ma: Writing – review & editing, Writing – review & editing.The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.


However, there are several downsides to such approaches, for example limited spatial resolution or restriction of the measurement to synaptic processes of dopaminergic neurons. To overcome these limitations and to measure complementary aspects of dopamine transmission, magnetic resonance imaging (MRI)-based approaches investigating the macrostructure, metabolism, and connectivity of dopaminergic nuclei, i.e., substantia nigra pars compacta and ventral tegmental area, can be employed. In this scoping review, we focus on four dopamine MRI methods that have been employed in patients with schizophrenia so far: neuromelanin MRI, which is thought to measure long-term dopamine function in dopaminergic nuclei; morphometric MRI, which is assumed to measure the volume of dopaminergic nuclei; diffusion MRI, which is assumed to measure fiber-based structural connectivity of dopaminergic nuclei; and resting-state blood-oxygenation-level-dependent functional MRI, which is thought to measure functional connectivity of dopaminergic nuclei based on correlated blood oxygenation fluctuations. For each method, we describe the underlying signal, outcome measures, and downsides. We present the current state of research in schizophrenia and compare it to other disorders with either similar (psychotic) symptoms, i.e., bipolar disorder and major depressive disorder, or dopaminergic abnormalities, i.e., substance use disorder and Parkinson’s disease. Finally, we discuss overarching issues and outline future research questions.For decades, aberrant dopamine transmission has been proposed to play a central role in schizophrenia pathophysiology. These theories are supported by human  TheConcerning the consistency of findings using these molecular imaging methods, several meta-analyses identified increased presynaptic dopamine synthesis and storage in the dorsal striatum as the most consistent aberrance of dopamine transmission in schizophrenia –18. MostWhile molecular imaging methods have enabled great advances in the understanding of dopaminergic pathophysiology in schizophrenia and might even predict the course of symptoms , there aFirst, molecular imaging can only investigate synaptic dopaminergic transmission. Other aspects of the dopamine system, such as the structure or connectivity of dopaminergic nuclei, are not analyzed. Furthermore, since the highest density of dopaminergic synapses is located in the striatum, signal quality of molecular dopamine imaging is usually highest in the striatum, whereas signals of extrastriatal regions are often unreliable , 12, 25.in vivo imaging tools to map the dopamine system and its changes in schizophrenia. Magnetic resonance imaging (MRI)-based methods that investigate distinct aspects of the dopamine system are promising candidates for such tools, at least for the study of further aspects of the dopamine system beyond synaptic transmission. In the following, we perform a scoping review on MRI of the dopamine system in schizophrenia; particularly, we will focus on four MRI methods which have been applied in schizophrenia: (i) neuromelanin MRI, which is thought to measure long-term dopamine function in SNc and VTA; (ii) morphometric MRI, which is assumed to measure the volume of SNc and VTA; (iii) diffusion MRI, which is assumed to measure fiber-based structural connectivity of SNc and VTA; and (iv) blood oxygen level-dependent (BOLD) functional MRI (fMRI) at rest, which is thought to measure blood oxygenation fluctuations of SNc and VTA and their functional connectivity, i.e., their correlations with other brain regions’ ongoing blood oxygenation fluctuations.For these reasons, it is necessary to complement molecular imaging with other For each of these four methods, we initially describe its underlying signal, its outcome measures, and its downsides. Then, we present the current state of research in (i) schizophrenia, (ii) other psychiatric disorders – with a focus on disorders with similar (psychotic) symptoms, e.g., bipolar disorder or major depressive disorder, or with assumed dopaminergic abnormalities, e.g., substance use disorder –, and (iii) Parkinson’s disease, which is frequently used as a model for dopamine MRI due to dopamine cell loss in this disease.After these method-specific paragraphs, we summarize and discuss the main findings as well as methodological issues and missing points within and across modalities. Furthermore, we outline questions for future research.Neuromelanin is a dark pigment produced by catecholamine metabolism. Over the life span, it gradually accumulates in the form of neuromelanin-iron complexes within organelles in the cell bodies of catecholamine-producing neurons, mostly in SNc, VTA, and locus coeruleus  30–33).–33.30–33Since neuromelanin-iron complexes are paramagnetic , they shin vivo in subjects without neurodegeneration: Cassidy et al. showed a relationship between substantia nigra CNR and both post-mortem neuromelanin concentration in the substantia nigra and amphetamine-induced striatal dopamine release measured by PET , which describes the ratio between the signal in a presumably neuromelanin-rich region, for example substantia nigra, and the signal of a reference region that presumably contains no neuromelanin, for example crus cerebri  39, 40), 4039, 4d by PET .Downsides of the neuromelanin MRI signal comprise, first, the issue whether neuromelanin MRI specifically measures neuromelanin concentrations or also other substances/processes; further multimodal work combining imaging and histology is needed here. Second, although the spatial resolution of neuromelanin MRI (about 0.5 × 0.5 × 1.5 mm) is much higher than with PET, enabling the investigation of substantia nigra subregions , more reIn patients with schizophrenia, studies have shown a reliable elevation of substantia nigra CNR , whereasRegarding other psychiatric disorders, increased substantia nigra CNR has been shown for substance use disorder , 51. ForIn Parkinson’s disease, neuromelanin MRI has been widely used. The first study by Sasaki et al. observed reduced CNR of both substantia nigra and locus coeruleus in patients . Two recFor the analysis of the macrostructure of dopaminergic nuclei, structural magnetic resonance imaging based on T1- or T2-weighted sequences can be used . These sAutomated techniques can be divided into normalization-based and segmentation-based approaches . Concern3. A downside of FreeSurfer is that regions-of-interest have to be predefined in the software framework based on an atlas. For substantia nigra, for example, no such predefined region-of-interest exists, thus it cannot be investigated.Concerning segmentation-based tools, FreeSurfer is a common tool. FreeSurfer defines brain regions-of-interest on the subject level based on atlases and tissue borders , 59. TheTaken together, a reliable automated segmentation of the substantia nigra and ventral tegmental area remains challenging , 61. ThiFor these reasons, manual segmentation remains the gold standard to delineate dopaminergic nuclei. Downsides of this approach are: it requires high expertise, is prone to inaccuracies, and is impracticable for applications to large cohorts , 61.Further – more general – downsides of T1w-based morphometric MRI derive from its underlying signal. The MRI signal has a high sensitivity to common artifacts and non-structural factors, such as head motion, breathing effects, substance use, and body weight . In addiIn schizophrenia, we are not aware of any region-of-interest-based studies that directly focused on VTA or SNc – therefore, no clear trend of alterations has emerged so far . HoweverConcerning other psychiatric disorders, e.g., bipolar disorder, major depressive disorder, or substance use disorder, we are not aware of any direct morphometric studies of dopaminergic nuclei either. Although some voxel-based whole-brain analyses revealed a morphometric increase in substantia nigra or midbrain in bipolar and major depressive disorder, and a decrease in substance use disorder e.g., –74], coo, coo74)]The few studies in psychiatric diseases are in stark contrast to the research in Parkinson’s disease, where several studies about morphometric differences of the dopaminergic nuclei have been performed so far. For example, Geng et al.  manuallyin vivo in humans. DWI relies on the principle that water molecules are in constant motion. In free water , molecules can equally move or diffuse in all directions – this is called isotropic diffusion. In biological tissues comprising certain cell architectures, the diffusion of water molecules is hindered by cell membranes, thus resulting in anisotropic diffusion  is the only technique available to study white matter fibers and tracts iffusion –81. Thusiffusion , 83. DWIiffusion –81. The Magnitude and directionality of water diffusion can be modeled by so-called 3D diffusion tensors . In mathematical terms, a voxel-wise diffusion tensor is a 3 × 3 matrix, which subjects to diagonalization and results in a set of three eigenvectors. Such a tensor can be visualized as an ellipsoid, for which the three eigenvectors represent the major, medium, and minor axis of the ellipsoid, respectively, and the corresponding three eigenvalues represent the apparent diffusivities along these axes . From thWhile FA can inform about the microstructure of white matter, it does not provide information about pathways of white matter, for example tracts connecting two brain regions such as VTA and parts of the prefrontal cortex. However, 3D representations of fiber bundles can be obtained by identifying streamlines of adjacent primary eigenvectors of diffusion across neighboring voxels . This pra priori defined tract connecting two regions , which makes tractography of small regions such as VTA difficult. Therefore, some studies have opted for manually delineating these structures  or drawi regions . Changes regions , 104 or  regions , an inde regions . As for Several studies have investigated both substantia nigra and VTA structural connectivity in healthy humans. Most studies performed either DTI-based or CSD-based probabilistic tractography with seeds in the VTA or SN, respectively , 108. AnFew studies have investigated VTA/substantia nigra structural connectivity in schizophrenia so far, each using a different measure of structural connectivity. Thus, findings are rather mixed, and no clear pattern has emerged so far . Bracht In other psychiatric disorders, most studies investigated the microstructure of the medial forebrain bundle. For example, in major depressive disorder, Bracht and colleagues used a bilateral probabilistic fiber tracking approach to extract pathways between the VTA and ventral striatum and medial and lateral prefrontal cortex, respectively, and compared mean FA values  between In Parkinson’s disease, lower FA  and reduBlood oxygenation reflects the amount of oxygenated hemoglobin of erythrocytes in the blood. Blood oxygenation level dependent functional magnetic resonance imaging (BOLD fMRI) is a neuroimaging technique that non-invasively depicts local changes of blood oxygenation along time . First, via pair-wise correlation coefficients   , 126 (Fiicients) . Correlaicients) , 127 (Fiicients) , 5C. Paricients) , 129. Thicients) , prefronicients) , 131, asicients)   , 5C. Furicients) –136.However, potential downsides of the BOLD fMRI signal, BOLD FC in general, and BOLD FC of the VTA/SNc in particular have to be mentioned. In general, BOLD fMRI is sensitive to several confounds, including, for example, scanner-related artifacts such as hardware-related instabilities and undesired variations in the magnetization field, scanning-dependent low frequency drifts, and subject-induced motion artifacts, making multi-scanner or multi-site studies challenging . SpecifiIn patients with schizophrenia, studies of VTA/substantia nigra resting-state BOLD FC demonstrated an overall trend to reduced functional connectivity between VTA/substantia nigra and forebrain structures in patients –147. HadIn other psychiatric disorders, VTA/SNc resting-state BOLD FC results are mixed. For instance, regarding substance-related and addictive disorders, reduced connectivity between substantia nigra and caudate nucleus as well as between VTA and medial orbitofrontal cortex, anterior cingulate cortex, and nucleus accumbens has been observed in opioid use disorder , while rIn Parkinson’s disease, the only study so far observed reduced FC of substantia nigra with thalamus, pallidum, caudate, putamen .We performed a scoping review of four MRI methods imaging the dopamine system in schizophrenia, namely neuromelanin MRI, morphometric MRI, diffusion MRI, and BOLD resting-state fMRI. We set these studies in context regarding methodological underpinnings , typical results in healthy controls, and aberrances in both other psychiatric disorders frequently comprising psychotic symptoms and Parkinson’s disease, the reference disorder for MRI-based dopamine system imaging. A sufficient number of studies with homogeneous methodology (five) was identified only for neuromelanin MRI , for whiWhile molecular imaging, e.g., PET, of the dopamine system in schizophrenia focuses on aberrant dopamine transmission at the symaptic level, MRI-based imaging methods complementarily focus on other parts of the dopamine system : neuromeNeuromelanin MRI studies showed a consistent increase of contrast-to-noise-ratio in the substantia nigra in patients with schizophrenia , 47 Fig. This isThe few morphometric MRI studies of dopaminergic nuclei showed no clear trend for morphometric alterations in SNc and VTA in patients with schizophrenia : while tDiffusion MRI studies in schizophrenia showed alterations in several structural connectivity measures, hinting at an aberrant macroscopic fiber architecture and fiber microstructure. However, due to the considerable heterogeneity of methods and measures, no clear pattern has emerged so far , 106 Fi. FurtherResting-state BOLD fMRI studies in schizophrenia showed a trend for reduced resting-state functional connectivity of dopaminergic nuclei with widespread forebrain areas –147 Fig. Howeverin vivo dopamine imaging, – for example in the form of simultaneous PET/MRI – or neuromelanin MRI, as the most specific dopamine MRI approach . As already described further above, automated segmentation of SNc/VTA is not reliably feasible so far due to insufficient contrast properties of standard MRI techniques. Thus, typical publicly available masks are based on manual delineation of structures in “standard samples” , 159. No3 ; SN ∼3503 . As alternative underpinning pathophysiologies, other disturbed transmitter systems might be relevant, e.g., glutamate or acetylcholine . Furtherin vivo in schizophrenia. We observed a clear trend of results only in neuromelanin MRI – due to a sufficient number of studies and homogeneous outcome measures –, suggesting increased neuromelanin accumulation in the substantia nigra in patients with schizophrenia. A clear trend for gray matter volume changes in VTA/SNc morphometry is not observable so far due to the low number of studies and methodological challenges. In diffusion MRI, there was no clear trend either, largely due to the heterogeneity of outcome measures used across studies. In resting-state BOLD fMRI, there was a weak trend for reduced resting-state functional connectivity of dopaminergic nuclei with forebrain regions, whose interpretation is difficult since the direction of BOLD changes cannot easily be interpreted. As major overarching problems across modalities, we identified most modalities’ lack of specificity for dopaminergic neurons, the low number of studies – particularly systematic investigations of specific patient groups and longitudinal studies are lacking –, and the accurate segmentation of dopaminergic nuclei. Advanced novel MRI techniques and multimodal studies, particularly linking dopamine PET and MRI, might alleviate these problems in the future.Although the number of studies is still low, neuromelanin MRI, morphometric MRI, diffusion MRI, and BOLD resting-state fMRI have proven to be viable and complementary methods to investigate the human dopamine system All authors contributed to conceiving, researching, and writing of this manuscript.