Synaptic impairment might precede neuronal degeneration in Parkinson’s disease. However, the intimate mechanisms altering synaptic function by the accumulation of presynaptic α-synuclein in striatal dopaminergic terminals before dopaminergic death occurs, have not been elucidated. Our aim is to unravel the sequence of synaptic functional and structural changes preceding symptomatic dopaminergic cell death. As such, we evaluated the temporal sequence of functional and structural changes at striatal synapses before parkinsonian motor features appear in a rat model of progressive dopaminergic death induced by overexpression of the human mutated A53T α-synuclein in the substantia nigra pars compacta, a protein transported to these synapses. Sequential window acquisition of all theoretical mass spectra proteomics identified deregulated proteins involved first in energy metabolism and later, in vesicle cycling and autophagy. After protein deregulation and when α-synuclein accumulated at striatal synapses, alterations to mitochondrial bioenergetics were observed using a Seahorse XF96 analyser. Sustained dysfunctional mitochondrial bioenergetics was followed by a decrease in the number of dopaminergic terminals, morphological and ultrastructural alterations, and an abnormal accumulation of autophagic/endocytic vesicles inside the remaining dopaminergic fibres was evident by electron microscopy. The total mitochondrial population remained unchanged whereas the number of ultrastructurally damaged mitochondria increases as the pathological process evolved. We also observed ultrastructural signs of plasticity within glutamatergic synapses before the expression of motor abnormalities, such as a reduction in axospinous synapses and an increase in perforated postsynaptic densities. Overall, we found that a synaptic energetic failure and accumulation of dysfunctional organelles occur sequentially at the dopaminergic terminals as the earliest events preceding structural changes and cell death. We also identify key proteins involved in these earliest functional abnormalities that may be modulated and serve as therapeutic targets to counterbalance the degeneration of dopaminergic cells to delay or prevent the development of Parkinson’s disease. et al. evaluate the temporal sequence of functional and ultrastructural changes at striatal synapses prior to the emergence of parkinsonian motor features. They identify key proteins involved in these functional abnormalities that could be targeted to help delay or prevent dopaminergic degeneration.Merino-Galan https://doi.org/10.1093/brain/awac191) for a scientific commentary on this article.See Aleph Prieto and Cotman ( The accumulation of intracellular inclusions (Lewy bodies), in which α-synuclein (α-syn) is the main protein is the pathological hallmark of Parkinson’s disease.2 Neurodegeneration in Parkinson’s disease begins years beforehand, often in conjunction with a variety of non-motor symptoms.3 This long latent phase of the disease, termed premotor Parkinson’s disease, represents an opportunity to study the initial pathophysiological changes in Parkinson’s disease with the ultimate goal of developing therapies able to delay or prevent the degeneration of dopaminergic neurons.Parkinson’s disease is a progressive neurodegenerative disorder characterized by the degeneration of dopaminergic neurons in the substantia nigra pars compacta  account for about 80% of all striatal synapses.25–28 As such, the early changes at the striatal dopaminergic synapse have been poorly characterized. Better understanding the early synaptic events triggered by α-syn accumulation, before the onset of dopaminergic cell death and motor manifestations and, yet once the underlying pathological processes have commenced, could help identify therapeutic strategies that target the synaptic machinery of selective and vulnerable synapses, thereby halting Parkinson’s disease progression.Although synaptic dysfunction seems to be an early pathological event in Parkinson’s disease, preceding neuronal demise, the intimate mechanisms driving these early synaptic changes and the plastic mechanisms that might stabilize the premotor phase remain elusive. This is because most research in this field has been conducted both in Parkinson’s disease patients and in animal models once dopaminergic neurodegeneration produces a phenotypic effect (i.e. once >50% cell loss has occurred), as occurs at Parkinson’s disease diagnosis.pc.To address these issues, we set out to study the earliest temporal sequence of functional and structural changes at striatal synapses associated with α-syn overexpression and dopaminergic degeneration. As such, we used an experimental model of progressive parkinsonism in rats that are induced by viral vector-mediated overexpression of A53T mutated human α-syn (hα-syn) in the SNad libitum access to food and water. Adeno-associated viral vectors (AAV2/9-CMV-WPRE) inducing the overexpression of either A53T mutated hα-syn (8 × 1012 genomic particles/ml) or the empty vector (EVV: 2 × 1013 genomic particles/ml) were obtained from the University of Bordeaux (France). A cross-sectional study was performed on animals receiving either hα-syn or EVV at different time points post-inoculation: 24 and 72 h, or 1, 2 and 4 weeks. Rats were anaesthetized with isoflurane in oxygen-enriched air (1–2%) and placed in a stereotactic head frame (Stoelting). The corresponding AAVs were injected bilaterally into the SNpc  as described previously.30 The coordinates from Bregma were: (i) anteroposterior −4.9, lateral ±2.2 and ventral −7.7 mm; and (ii) anteroposterior −5.4, lateral ±2.0 and ventral −7.7 mm.31 The injection needle was left in place for 2 min before being slowly retracting it from the brain. All the experimental procedures were approved by the animal research committees of the Biodonostia HRI (CEEA16/11) and CIMA-Universidad de Navarra (107-17), and they were carried out in strict accordance with the guidelines of the Spanish Government (RD53/2013) and the European Union Council Directive (2010/63/EU) on the protection of animals used for scientific purposes. All efforts were made to minimize animal suffering and to reduce the number of animals used.Adult male Sprague-Dawley rats  were housed in pairs under standard conditions  and with in vivo monitoring of motor activity, the adjusting stepping test was carried out at baseline, and 24 h, 72 h, 1, 2 and 4 weeks post-inoculation, and the open field test at 1, 2 and 4 weeks post-inoculation The average number of adjusting steps with each forepaw in both directions (adduction and abduction) was analysed,30 as well as the total distance travelled (cm) and the total velocity (cm/s) for the open field test. For more details see the For n = 4/group at 24 h, 72 h, 1, 2 and 4 weeks post-inoculation) were perfused transcardially with 4% paraformaldehyde—0.2% glutaraldehyde in phosphate buffer . Brains were removed, post-fixed overnight and then transferred to a cryoprotective solution (pH 7.4) before freeze–thawing in isopentane. Serial coronal vibratome sections  were collected. Immunohistochemistry was performed on free-floating coronal sections containing the striatum and SNpc to evaluate tyrosine hydroxylase (TH), dopamine transporter and hα-syn expression as described previously.30 Stereological quantification of TH+ neurons and densitometric analysis for hα-syn immunoreactivity was performed in the SNpc. The densitometry for TH, hα-syn and dopamine transporter expression was performed in the striatum as detailed in the Animals  in buffer A , and then using a 26 GA needle. This homogenate was spun down at 1400g for 10 min at 4°C, retaining the supernatant, and resuspending the pellet in buffer A and centrifuging it again at 710g for 10 min at 4°C. Both supernatants were mixed and spun down at 11 600g for 12 min at 4°C. The pellet was again resuspended in buffer A and overlaid on buffer B . After centrifugation at 20 000g for 1 h at 4°C, the interphase was collected as the synaptosome sample. The protein content of individual synaptosomes samples was quantified with the PierceTM BCA protein assay kit (Thermo Fisher Scientific).Striatal synaptosomes were isolated as described previously, with minor modification.pc (72 h) and in the striatum (1 week); and the point at which there was a significant reduction in dopaminergic neurons in the SNpc and of dopaminergic terminals in the striatum (4 weeks). Synaptosomal protein extracts derived from striatal samples were subjected to SWATH-MS . Individual protein extracts (20 µg) from all experimental groups were subjected to in-gel digestion, peptide purification and reconstitution before the SWATH-MS runs using the TripleTOF 5600+ instrument configured as described previously.33 Significantly dysregulated regulatory/metabolic pathways in synaptosomal fractions were identified with Metascape.34 The α-syn interactome was obtained from the curated Biological General Repository for Interaction Datasets (BioGRID: https://thebiogrid.org).35 The synaptic ontology analysis was performed using the SynGo platform .36 To identify individual proteins from the deregulated protein list with potential links to Parkinson’s disease or neurodegeneration, we performed an extensive literature search . Synaptosomes (8 µg per well) were loaded into PEI/Geltrex-coated microplates, centrifuged at 3400g for 1 h at 4°C (Beckman Coulter Allegra X-12R centrifuge) and the ionic medium was replaced with incubation medium at 37°C (pH 7.4). The microplate was incubated in a non-CO2 incubator (Incudigit) for 10–15 min at 37°C and then loaded into the XF96 extracellular analyser following the manufacturer’s instructions. Mitochondrial respiration, as indicated by the OCR, was monitored simultaneously in real-time throughout the assay by sequential injection of modulators of the mitochondrial electron transport chain: oligomycin (5 µM), carbonyl cyanide-4-(trifluoromethoxy) phenylhydrazone  and rotenone/antimycin A (2 µM each). The OCR data represented the mean rates of each measurement cycle, which consisted of a 30 s mixing time, a 30 s waiting time and 3 min of data acquisition. Basal respiration was measured before the first injection (three cycles) and three data-points were obtained following each injection . After the assay, the plate was stained with crystal violet (Sigma-Aldrich) for data normalization, and data were analysed using Wave Desktop 2.6 software . Parameters of mitochondrial respiration were obtained from the OCR data (The oxygen consumption rate (OCR) was measured in freshly purified synaptosomes using a Seahorse XF96 extracellular flux analyser (Agilent) as described previously, with some modifications.OCR data .n = 6 EVV, n = 4 hα-syn per time point), and serial coronal vibratome sections  were collected and stored at 4°C in a preserving solution containing 0.03% sodium azide in PBS (pH 7.4) until their use. Striatal coronal sections were processed for pre-embedding immunoperoxidase labelling as described previously, with some modifications.39 First, the sections were processed following the TH immunohistochemistry staining protocol described to assess the ultrastructural changes at dopaminergic terminals. The sections were then washed twice in 0.1 M PB and they were post-fixed in 0.5% osmium tetroxide diluted in 0.1 M PB for 15 min. After two washes in 0.1 M PB, the sections were dehydrated in ascending ethanol dilutions for 10 min each, and then in 70% ethanol with 1% uranyl acetate. The sections were subsequently incubated in ethoxypropanol and embedded in epoxy resin (Durcupan™ ACM). Ultra-thin sections from the superficial planes were obtained with an Ultracut S ultramicrotome (Reichert Technologies), and they were finally contrasted with lead citrate and examined on a transmission electron microscope (TEM: Hitachi H-7650 microscope) equipped with an SC1000 Orius CCD camera (Gatan). Digital images were obtained randomly from the dorsal striatum of both hemispheres (∼+0.2 mm from Bregma according to the atlas31) at a final magnification of ×15 000 using the Metamorph software . Images were obtained that avoided blood vessels, occasional grouped areas of myelination and astroglial swelling, to ensure that mainly neurons and synapses were analysed. Image resolution in the xy plane was 3.4 nm/pixel and the images were 3284 × 2600 pixels in size. The area per field of view was 104.35 µm2.Brains were collected as described in the immunohistological section previously  to assess different ultrastructural parameters of dopaminergic fibres and glutamatergic synapses. All the values calculated were averaged across the animals in each group and for each time point  and they were then incubated with the corresponding primary antibodies diluted in PBS for 24 h or for 48 h at 4°C when diluted in blocking solution. The following day, the slices were exposed for 1 h and 30 min at room temperature to the corresponding anti-goat, anti-rabbit and anti-mouse secondary antibodies conjugated to Alexa 488, 546 and 647, respectively, protected from the light. The cell nuclei were counterstained with DAPI , and the sections were then mounted onto glass slides using Vectashield mounting medium  and visualized under a Zeiss LSM 800 confocal laser microscope (Carl Zeiss) with a Plan-Apochromat ×63/1.4 numerical aperture oil-immersion objective (Carl Zeiss).Triple immunodetection of either TH, LC3B and Lamp1 or TH, Rab5 and Rab7 was performed as described previously with slight modificationst-test or Mann–Whitney U-test was performed when data were parametric or non-parametric, respectively. For multiple comparisons between the means of the hα-syn and EVV groups over time, a one-way ANOVA followed by a Bonferroni’s post hoc test or a Kruskal–Wallis and Dunn’s post hoc test were performed when data were parametric or non-parametric, respectively. Group data are represented in graphs as the mean ± SEM and significant differences were set at P < 0.05.All the statistical analyses were performed using GraphPad Prism 8.0 software (GraphPad Software Inc.). Data distribution for normality was assessed using the Kolmogorov–Smirnov test and variance equality with a Levene’s test. For repeated measures in the stepping test, a Wilcoxon or Friedman test were used, while the open field data were assessed with a two-way ANOVA. For pair-wise comparisons between the means of the hα-syn and EVV groups, an unpaired An extended version of the materials and methods is available in the pc and striatum occurred from 1 week post-inoculation onwards ; eractome . CRYAB aThese findings indicate that synaptic homeostasis is altered very soon after hα-syn overexpression, initially by proteins involved in bioenergetic regulation, yet closely followed by proteins participating in the clearance of proteins and in translation. Proteins implicated in the postsynaptic density are only altered late following the previous modifications.+ fibres, in the number of mitochondrial subtypes    and in thondria) .+ fibres was unchanged, the density of intact mitochondria was lower  and a decrease in the roundness  of intact mitochondria at 4 weeks post-inoculation  and 4 weeks post-inoculation  and 4 weeks post-inoculation  and autophagic (LC3B and Lamp1) proteins at 4 weeks post-inoculation There was an accumulation of LC3 and Rab5 but no Rab7 and Lamp1 expression in the swollen TH+ fibres relative to the EVV controls  and 4 weeks post-inoculation (P < 0.05 versus EVV: P < 0.05 versus EVV), but no alterations in mitochondrial morphology were evident  and postsynaptic targets of glutamatergic ASs within the dorsal striatum were also studied. No significant differences in the density of ASs were found . When cl evident .P < 0.01 versus EVV) and at 4 weeks post-inoculation  and at 4 weeks post-inoculation (P < 0.05: P < 0.05) and spare respiratory capacity  or ATP synthase (complex V) were detected at any time point. Moreover, there were no alterations to the expression of proteins related to mitochondrial dynamics (fission and fusion), such as mitofusin 2 (MFN2) and optic strophy 1 (OPA1), or in the recruitment of the mitochondrial fission protein dynamin 1 (DNM1L) at any of the time points , NADH dehydrogenase complex (complex I), cytochrome e points .We have studied for the first time the earliest changes in the striatal dopaminergic synapses induced by hα-syn overexpression showing how functional alterations are primary events preceding structural changes and loss of dopaminergic neurons at a premotor stage. We describe that mitochondrial dysfunction and energetic failure is the earliest event followed by autophagic/endocytic flux dysfunction and accumulation of dysfunctional organelles preceding synaptic structural changes and cell death. We also describe the proteins involved in such changes that could be potential therapeutic targets to counterpart synaptic malfunction and prevent degeneration. Changes in striatal glutamatergic synapse, accounting for structural plasticity and potentially helping the maintenance of motor function, are also observed adding value to this model of premotor parkinsonism and to our findings.47 The upregulation of PRKAG2, a sensor protein of the cell energy status, reflects a state of metabolic stress and it suggests that hα-syn overexpression rapidly triggers an energy deficit that accentuates thereafter.48 This condition of stress is also supported by the fact that transthyretin (TTR), the most strongly upregulated protein at this time, is a marker of oxidative stress.50 Therefore, these early proteostatic alterations in synaptic metabolism could initiate a cascade of pathological events leading to the bioenergetic dysfunctions that starts at 1 week post-inoculation At this time point, a deregulation of proteins related to the mitochondrial compartment  commences being the upregulation of ECSIT particularly notable as it responds to signs of oxidative stress or mitochondrial damage, triggering the activation of protective molecular events like mitophagy.51 However, the mitochondrial clearance system might be disrupted as USP15 is a deubiquitinating enzyme that catalyses the removal of ubiquitin from substrates opposing Parkin-mediated mitophagy.42 The upregulation of USP15 at 1 and 4 weeks post-inoculation suggests a possible inhibition of mitophagy, which would coincide with the onset of mitochondrial respiratory defects (1 week post-inoculation) and that might cause the ensuing mitochondrial ultrastructural changes and energetic compromise (4 weeks post-inoculation). Remarkably, this occurs without any reduction in the fundamental mitochondrial proteins often used to study mitochondrial content, such as TOM and cytochrome c, and the number of mitochondria at dopaminergic presynaptic terminals, as well as the mitochondrial subtypes, do not appear to suffer ultrastructural alterations at this time point (1 week post-inoculation). Thus, our findings confirm that these early bioenergetic alterations were due to a uniquely functional deficit in respiration and not to changes in mitochondrial content or structure. It is important to notice that dopaminergic neurons are especially vulnerable to degeneration in Parkinson’s disease. Although the ultimate reason for this is unknown, these neurons have a high-energy demand that has been considered relevant.6 Interestingly, we observe that although hα-syn is overexpressed in the whole striatum, dopaminergic degeneration mainly occurs in the dorsal region, which is where these findings are observed. This indicates that in this model the dopaminergic neurons of the ventro-lateral tier of the SNpc  are especially vulnerable as these occur in patients with Parkinson’s disease, supporting our results. This could be due to their extensive axonal arbour and consequent high bioenergetics demand making them more vulnerable to stressors such as α-syn accumulation.52 Thus, we demonstrate that even modest alterations in synaptic mitochondrial activity in these dopaminergic neurons conveys dramatic changes and therefore, altered energy supply/demand could initiate the cascade of events leading to the neurodegeneration associated with Parkinson’s disease.6The earliest alteration to the striatal synapses (72 h post-inoculation), involves the deregulation of proteins affecting the metabolism. Synaptic activity is tightly coupled to metabolic energy, requiring tight control of the energy balance for correct neuronal activity.54 and other neurodegenerative diseases,55 as well as in animal models of parkinsonism or fibroblasts from idiopathic Parkinson’s disease patients have shown promising results.56 Additionally, other small-molecules enhancing mitophagy have been described as potential therapeutic targets in parkinsonian models.54 Thus, in the same line, we postulate that inhibition of USP15 activity with small molecule inhibitors could selectively re-engage homeostatic redox responses as has been previously suggested with other similar deubiquitinating enzymes.57 As far as mitochondrial deficits have been described in other neurodegenerative diseases, our findings could be relevant to test in other animal models, which could potentially provide early therapeutic targets for such disorders.55Modulation of some of these proteins related to the mitochondrial compartment aiming to restore bioenergetics to preserve synaptic function could be a potential therapeutic strategy. In this regard, recent studies stimulating the clearance of damaged mitochondria in patients with Parkinson’s disease58 Thus, this indicates that synaptic mitochondria are already functionally impaired, compromising the synaptic function and structure accounting for dopaminergic loss. Indeed, following mitochondrial dysfunction there is a loss of dopaminergic axon terminals from the second week of hα-syn overexpression onwards being significant at 4 weeks post-inoculation when ultrastructural changes associated with the degeneration of axons are also present. This indicates that the remaining dopaminergic fibres acquire a pathological conformation with an observable swollen and bulging morphology, in keeping with earlier studies showing swollen TH+ fibres in the striatal terminals of post-mortem brains of Parkinson’s disease patients 60 and in animal models of Parkinson’s disease.62 Moreover, although the total mitochondrial population is maintained, there is an increase in damaged mitochondria at 4 weeks post-inoculation Interestingly, the remaining intact mitochondria have morphological features related to enhanced activity (increment in length-to-width ratio and a decrease in the roundness),63 which, although speculative, might indicate a functional compensation through morphological changes to potentially support continued neural transmission. Furthermore, several proteins linked to ribosomes and protein translation were seen to be deregulated, indicating that the imbalance in the regulation of local protein translation might contribute to synaptic dysfunction at this time point.64At 4 weeks post-inoculation, a reduction in spare respiratory capacity was observed, which has been considered a major factor that defines neuron survival.65–67 We found that from the second week onwards there is a significant increase in the number of electroclear vesicles within dopaminergic fibres, which co-localize with LC3+ and Rab5+ at 4 weeks post-inoculation, markers of autophagy and endocytic vesicles, respectively. This is also witnessed by the deregulation of proteins involved in autophagic flux from 1 week post-inoculation and the deregulation of proteins related to endocytosis and transport at 4 weeks post-inoculation. Of these proteins, the upregulation of VPS39 suggests an impairment in the endosomal maturation cycle.68 Additionally, it has been described that Ist1 and ATG3 inhibition represses autophagic flux69 and RABEP170 and SECC22B71 overexpression triggers the accumulation of large endocytic vesicles.70 Accordingly, a sequence of early proteostatic changes in the autophagic/endocytic system could result in impaired maturation of autophagy intermediates, which may then lead to an accumulation of endosomes and autophagy cargo within the dopaminergic terminals, contributing to neurodegeneration.72 We now demonstrate that mitochondrial and autophagic dysfunction are at play in the earliest phase of dopaminergic degeneration, preceding motor function defects.Correct synaptic autophagy is also critical to maintain dopaminergic synapses.74 YWHAQ or the 14-3-3θ protein also has a chaperone function and it reduces α-syn toxicity and propagation.76 Thus, we speculate that the decrease in CRYAB and 14-3-3θ are directly related to hα-syn accumulation and that it may be a critical mechanism by which α-syn propagation and toxicity arise in Parkinson’s disease.By comparing the hα-syn interactome with our deregulated proteome we found CRYAB and YWHAQ were significantly downregulated at 4 weeks post-inoculation. Interestingly, this finding coincides with the onset of dopaminergic cell body degeneration. CRYAB is a chaperone that act as a potent inhibitor of α-syn amyloid fibril formation, preventing aggregation and its ensuing neurotoxicity.77–79 Similar to our findings, ultrastructural remodelling features have been observed in advanced stages of dopaminergic depletion79–81 and in post-mortem brains of Parkinson’s disease.82 However, there are no previous studies in the early stage of dopaminergic degeneration. The early decrease in the proportion of axospinous synapses at 72 h, which is also later observed at 4 weeks post-inoculation, along with the proteostatic alterations to the PSD after hα-syn overexpression, might be contributing to maintaining stable levels of excitability, reflecting a process analogous to what has been defined as synaptic homeostatic scaling, whereby the strength of a neuron’s postsynaptic inputs are adjusted to compensate for extended changes in overall activity and stabilizing regional synaptic weighting.83 This is also consistent with the increase in the density of perforated ASs observed from 1 week post-inoculation onwards, without alterations to the overall or macular AS density. Perforated synapses have been described as structural intermediates in synaptic turnover85 and their larger PSD perimeter surface leads to more efficient neurotransmission than in macular synapses.87 In addition, the increase in the number of mitochondria within glutamatergic terminals provides further evidence for the possible enhanced glutamatergic activity that could potentially sustain the mechanisms of plasticity observed as suggested in other studies.88 Last, although the increase of GluR1 and GluR2/3 is related to homeostatic plasticity, our study did not show such a rise in PSD fractions. These results are in keeping with previous studies in the striatum of parkinsonian rats where no changes in GluR were found.89–92 Actually, it is thought that they only occur after long-lasting dopaminergic depletion explaining our findings.Another interesting aspect of our study is the observation that glutamatergic synapses in the dorsal striatum undergo complex ultrastructural remodelling already observed in neurotoxic models of parkinsonism and that has been claimed as a sign of homeostatic plasticity.93 are downregulated at 72 h post-inoculation, and cell part morphological proteins that are down- and upregulated have been found at 1 week post-inoculation suggestive of structural remodelling at the synaptic level as has been described in analogous studies where α-syn overexpression leads to abnormalities in spine dynamics and impairments on dendritic spine stability and plasticity.95 Likewise, the decrease in axospinous glutamatergic synapses of spiny projection neurons in Parkinson’s disease patients97 and animal models of advanced parkinsonism99 have been associated with spine pruning. Future studies addressing the striatal spine plasticity and its functional significance in the early stages of nigrostriatal degeneration should help better understand these plastic mechanisms.We also observe that proteins involved in spine formation such as Elmo127These adaptive changes in excitatory synapses might occur in an attempt to maintain the synaptic strength and the normal striatal activity despite deficiencies progressively produced by dopaminergic modulation. Although we have not studied dopaminergic transmission in our model and being one of the limitations of this study, impaired dopaminergic neurotransmission has been described as an early synaptic event that precedes neuronal degeneration in the same animal model.It is important to note that our functional and proteomic studies are performed on isolated striatal nerve terminals that are a mixture of both dopaminergic and glutamatergic synapses as no immunopurification was performed based on synaptic surface markers. In addition, long flow cytometry sorting times would be needed to accumulate sufficient material. However, ultrastructural studies were performed separately on dopaminergic and glutamatergic terminals, and data obtained by both approaches were consistent, although it would be important to perform future studies on sorted synaptic populations.Overall, we describe a progressive striatal synaptopathy in the premotor stages of dopaminergic degeneration that opens new avenues for targeting key synapse proteins to develop disease-modifying therapies aimed at regaining synaptic function in this prodromal phase. Our data pave the way for future development of synapse-targeted therapies to be used in patients at risk of Parkinson’s disease  and hopefully in the future in participants with reliably early biomarkers.awac087_Supplementary_DataClick here for additional data file.


During perceptual decision-making, the firing rates of cortical neurons reflect upcoming choices. Recent work showed that excitatory and inhibitory neurons are equally selective for choice. However, the functional consequences of inhibitory choice selectivity in decision-making circuits are unknown. We developed a circuit model of decision-making which accounts for the specificity of inputs to and outputs from inhibitory neurons. We found that selective inhibition expands the space of circuits supporting decision-making, allowing for weaker or stronger recurrent excitation when connected in a competitive or feedback motif. The specificity of inhibitory outputs sets the trade-off between speed and accuracy of decisions by either stabilizing or destabilizing the saddle-point dynamics underlying decisions in the circuit. Recurrent neural networks trained to make decisions display the same dependence on inhibitory specificity and the strength of recurrent excitation. Our results reveal two concurrent roles for selective inhibition in decision-making circuits: stabilizing strongly connected excitatory populations and maximizing competition between oppositely selective populations. In decision circuits, inhibitory neurons signal animal choices. Here, the authors show that choice-selective inhibition can stabilize the circuit dynamics or promote competition depending on inhibitory output connections, affecting choice behavior. The functional properties of decision-making neural circuits have been extensively studied and modeled9. Central to the function of these circuit models are attractors in the activity space which characterize the population’s encoding of a given choice. The attractor mechanism driving the decision-making activity in these models relies on structured recurrent connections between populations of excitatory neurons that are each selective for a different choice11. Inhibitory neurons, in this view, are merely supporting actors facilitating competition and providing balance to the excitatory neurons.Perceptual decision-making requires neural circuits to integrate evidence and classify a stimulus to trigger the correct behavioral response. Neurons in a range of cortical areas modulate their firing rate to signal animal’s choice12. In primary sensory areas, inhibitory neurons are generally more broadly tuned13 and more densely connected to neighboring excitatory neurons15. These inhibitory neurons reliably modulate spike output to reflect stimulus features and have highly specific connectivity to surrounding excitatory neurons17. The stimulus selectivity of inhibitory neurons is enhanced by learning and attention18 suggesting that task dependent modulation of inhibitory activity is necessary for cognition. Beyond the primary sensory cortex, stimulus information and animal choice can be decoded from the activity of inhibitory neurons in secondary sensory and association areas indicating a role for selective inhibition in higher cognitive functions, such as decision making21. While there is growing evidence that the activity and connectivity of inhibitory neurons is as complex as excitatory neurons, how the selectivity of inhibitory activity and the diversity of their connections affect the decision-making function of cortical circuits is still unknown.Since the canonical models of decision-making circuits were built, the diversity and complexity of inhibitory neurons within the cortex have been characterized in increasing detail4 to account for the presence of inhibitory choice selectivity. Our model allows us to parametrically alter the specificity of connections between two choice-selective excitatory and two choice-selective inhibitory populations. Through analysis of this model, we found that while inhibition must drive competition between choice-selective excitatory populations it must also stabilize activity driven by recurrent excitation at the same time. These two concurrent roles are mediated by inhibitory connections to the excitatory populations and either role can be enhanced by structured inhibitory connectivity. We found that inhibitory selectivity expands the space of possible circuits which support decision-making by enhancing either a competitive or stabilizing role for inhibition. In addition, the connectivity motif between choice selective populations alters the underlying attractor dynamics and modulates the decision-making performance to prioritize speed or accuracy. We generalized these results by training recurrent neural networks (RNNs) to perform the same decision-making task. After training, RNNs had both excitatory and inhibitory units significantly selective for choice and displayed a similar dependence between the specificity of excitatory and inhibitory connections found in the mean-field model. Finally, we perturbed inhibitory neuron activity in these models to probe the dynamical regime in which the circuit operates. We found two regimes in which circuits respond differently to perturbations of inhibitory neurons: one in which the competitive role dominates and the other in which the stabilizing role dominates. Our work demonstrates that choice selective inhibition impacts decision-making behavior by enhancing either the competitive or the stabilizing role for inhibition in the circuit. These results generate testable predictions for perturbation experiments.To reveal the role of choice selective inhibitory neurons in decision-making computations we extended a well established mean-field model of decision-making circuits1 and E2) populations are the dynamic variables4. The average strength of connections between the four choice selective populations is controlled by a specificity parameter γ. For each of three connection classes  is the strength of feedback connections within excitatory populations selective for the same choice, and (1 − γEE) is the strength of connections between excitatory populations selective for the opposite choice. We keep γEE positive due to the importance of recurrent feedback excitation in the function of these circuits4. When γEE = 1, each of E1 and E2 have a strong self-excitatatory feedback and are not connected to each other. When γEE = 0, the strengths of excitatory connections between and within E1 and E2 are all equal. Inhibitory choice selectivity is controlled by γEI defined in the same way, which is also positive because inhibitory neurons inherit choice and stimulus information from the excitatory neurons. When γEI = 1, inhibitory population I1 receives excitatory inputs from E1 but not E2 and vice versa. When γEI = 0, each I1 and I2 receive equal excitatory inputs from E1 and E2. Thus, inhibitory activity is not choice selective when γEI = 0 because inhibitory neurons receive equal input from both excitatory populations. Inhibitory choice selectivity emerges as γEI increases  populations integrate dedicated streams of sensory evidence to produce a categorical choice Fig. a. In conγIE ≠ 0; Fig. γIE can range between  with negative values favoring connections between E and I populations with opposite choice preference and positive values favoring connections between E and I populations with the same choice preference. When γIE = 1, I1 sends inhibitory output to E1 but not E2. When γIE = −1, I1 sends inhibitory output to E2 but not E1. Thus, the specificity of inhibitory output connectivity defines three circuit motifs: contraspecific for γIE < 0, ipsispecific for γIE > 0, and nonspecific for γIE = 0.For inhibitory choice selectivity to have any effect on circuit function, the outputs of inhibitory populations must be structured  or nonspecific (γIE = 0), the strength of recurrent excitation (γEE) is highly constrained and deviations from a narrow range leads to the loss of one of the essential fixed points  governing decision-making behavior. We sampled the specificity parameter space to identify circuits which support these eight fixed points Fig. a. We fouing Fig. b. When ints Fig. c. For ciγEE Fig. b.Fig. 2CγEE is low, nonspecific and ipsispecific circuits lack the fixed points representing choice both in the presence and absence of stimulation as well as the saddle point during the stimulus . This four-variable model produces the firing-rate dynamics and attractors similar to the original two-variable mean-field model , ipsipecific circuits are more accurate at classifying difficult stimuli but more often fail to separate the outputs sufficiently producing invalid trials  to perform a decision-making task21 (Methods). We constructed ROC curves by decoding network choice from a unit’s activity on the time-step following stimulus offset. To identify which units significantly modulated their firing rate to reflect choice, we compared the area under the ROC curve (AUCROC) to a shuffle distribution generated from randomized trial labels . Units that were identified as choice selective increased activation following the onset of a stimulus corresponding to their preferred choice . Also, the proportion of significantly selective units was higher for inhibitory than excitatory units . Thus, inhibitory unit activity contained overall more choice information than excitatory unit activity despite the fact that only excitatory units received stimulus input. In this respect RNNs differ from experimental data in which excitatory and inhibitory neurons contained similar choice information21.We determined whether inhibitory neurons in these RNNs were choice selective. We classified recurrent units as choice selective using receiver operator characteristic (ROC) analysisγ from the weights of trained RNNs defined in the same way as for the mean-field model (Methods). Trained networks consistently had strong excitatory-excitatory (γEE = 0.59 ± 0.07) and excitatory-inhibitory (γEI = 0.39 ± 0.06) specificity  but their distribution showed both ipsispecific and contraspecific motifs. Inhibitory-inhibitory connections were nonspecific on average with higher variation than inhibitory-excitatory connections (γII = −5.0 × 10−3 ± 0.06). Confirming the trend predicted by the mean-field model, excitatory specificity γEE was correlated with the inhibitory specificity index γEIγIE, where networks with stronger recurrent excitation were ipsispecific and networks with weaker recurrent excitation were contraspecific  excitatory gain can be compensated by either an increase (decrease) in excitatory connection specificity or by strengthening of the contraspecific (ipsispecific) motif. Accordingly, we expect that changing the activation function slope of the excitatory units in RNNs should either shift the excitatory-excitatory specificity against the direction of the gain change or shift the inhibitory specificity towards contraselective (for lower slope) or ipsielective motif (for higher slope). We trained two additional sets of networks with hypoexcitable (slope 0.5) or hyperexcitable (slope 1.5) excitatory units. Changing the excitability of excitatory units led to large shifts in ν0,I  leads to a decrease in τslow, i.e. faster dynamics. Around a high baseline value of inhibitory activity  a low-inhibition regime where the competitive role dominates, and (ii) a high-inhibition regime where stabilizing role dominates. In trained RNNs, perturbations of all inhibitory neurons indicate that these networks operate in the stabilizing inhibition regime.γEE) and inhibitory connections (γEIγIE) reveals how the contra- and ipsispecific motifs enhance one of two roles for inhibition: facilitate competition between populations coding for opposite choices or stabilize amplification driven by strongly recurrent excitation. When γEE is low and excitatory populations alone cannot drive selective activation, contraspecific inhibitory motifs support decision-making by maximizing competition. Conversely, when γEE is high and excitatory self-amplification becomes unstable, ipsispecific inhibitory motifs stabilize firing rates.Decision-making circuits with non-selective inhibition exist only within a narrow range of excitatory-excitatory connection specificity. When inhibitory neurons inherit choice-selectivity from excitatory neurons and also project to excitatory neurons via specific connections, a broad range of circuit configurations can support decision-making. In circuits capable of decision-making, the correlation between the specificity of excitatory . Studies of E-I RNNs which focus on the impact of inhibitory connections show that specificity of inhibitory-inhibitory connections can be critical to circuit function23. The apparent difference in the importance of inhibitory-inhibitory selectivity between our networks and previous work could result from differences in the training procedures35. We observed a large impact of RNN training hyperparameters on the emerging circuit structure. Future work is needed to understand how details of training influence the emerging circuit structure and computations performed by RNNs.We found a similar relationship between excitatory and inhibitory connection specificity in RNNs suggesting the balance between competitive and stabilizing inhibition is a general principle in E-I networks. While specific connections between excitatory and inhibitory units were clearly important for the decision-making function in our networks, connections between inhibitory units appeared unconstrained, indicating this connection class has limited effect on circuit function like in the mean-field model. RNNs are increasingly often used to develop theories of how neural circuits perform computations3. While these models reproduce the dynamics of decision-making circuits they do not fully account for the diversity of interneurons within the cortex. Cortical inhibitory neurons show selective activation in many modalities including primary sensory37 and association areas21. Moreover, choice-selectivity of parietal inhibitory neurons is equal to that of excitatory neurons during an audio-visual discrimination task21.Our results show that selective inhibition can have a marked effect on the function of neural circuits. Many models of categorical decision-making rely on a nonspecific pool of inhibitory neurons to enforce winner-take-all competition between excitatory neuronsγEI in our model). While it is possible that choice selectivity could arise from external inputs to interneurons38 or even from random connections between excitatory and inhibitory neurons39, most circuit models assume stimulus information is exclusively provided by inputs to excitatory neurons. Inhibitory choice-selectivity also emerged in our RNNs trained to perform 2AFC task28. In our RNNs, inhibitory units can only inherit stimulus or choice information through specific connections from excitatory populations, unlike in other trained RNNs23. For both excitatory and inhibitory units in trained RNNs, we found that the fraction of selective units was higher than is commonly used in circuit models4 and found in experiments21. This difference could be due to the simplicity of RNNs compared to in vivo circuits, and also a training process which aims to minimize total activity through regularization. In addition, decisions in the RNN are fully determined by the local circuit, whereas an animal’s behavioral output arises from a broadly distributed circuitry. Although higher choice selectivity for inhibitory units was robust to doubling the network size .In attractor networks, modulation of 16. A shift in output specificity could be mediated through top-down activation of inhibitory subnetworks or through neuromodulation of distinct inhibitory subtypes such as PV+, SOM+, or VIP+. Acetylcholine has layer-dependent effects on the responsiveness of both regular spiking and fast spiking neurons in the visual cortex, which could differentially activate distinct inhibitory motifs on behaviorally relevant timescales42. Additionally, acetylcholine can reduce the release of inhibitory neurotransmitters in cortical neurons43, thus directly affecting inhibitory connectivity.Selective neuromodulatory control of genetically identifiable inhibitory subtypes may provide for control of inhibitory motifs. Inhibitory subtypes have distinct connectivity patterns to neighboring excitatory neurons: fast-spiking cells have far more reciprocal connections to excitatory neurons than adapting interneurons4. This assumption is based on the timescale separation between decay time constants of slow NMDA (~100 ms) and fast GABA (~5 ms) conductance. The slow NMDA dynamics dominate the time evolution of network activity, and one can assume that all other variables reach their steady-state nearly instantly4. Despite being fast, the dynamics of GABA synapses can also affect decision-making behavior44. A study44 considered a set of circuits with parameters chosen so that when the steady-state assumption is applied, all models reduce to the two-variable model with the exact same parameter set. Thus, all differences in dynamics of these circuits were driven by GABA dynamics. In these circuits, the GABA dynamics mediated a speed-accuracy trade-off and, moreover, this tradeoff was more efficient in circuits with selective inhibition44. While this study considered only ipsispecific inhibitory connectivity and a narrow space of circuits that all map onto a single parameter set of a two-variable model, our work explores a wide range of circuit configurations ranging from contraspecific to ipsispecific inhibitory motifs. Our findings are robust to the steady-state approximation of GABA dynamics as we show using a four-variable mean-field model  fail to reach the imposed decision threshold on a fraction of trials with low stimulus strength, which we call invalid trials. This behavior is common across spiking45. The stabilizing and competitive regimes can be differentiated by the behavioral response to perturbations of inhibitory activity. Perturbations during reaction time tasks should reveal which inhibitory role is dominant in vivo. The balance of these two roles is critical for circuits to perform decision tasks, and shifts in this balance could align dynamics with changing task requirements. More experimental work is needed to uncover how inhibitory subnetworks strike this balance in the cortex. Specifically, whether functional selectivity is constrained to certain inhibitory subtypes and whether inhibitory neurons are recruited to perform a task in a state dependent manner are important questions for future work.We show that choice selective inhibition can enhance one of two roles for inhibition in decision-making circuits: facilitating competition or stabilizing excitatory feedback. Both these roles are simultaneously fulfilled by inhibition in any decision making circuit. Enhancing activity of all inhibitory neurons can shift the circuit from a regime where the competitive role dominates to a regime where the stabilizing role dominates regardless of which inhibitory motif is present. This effect echos results which find shifts in E/I balance can induce leaky or unstable integration4 and reflects the experimental observation that only a fraction of all recorded neurons shows choice selectivity21. Each selective population contains the fraction f of the total number NE (NI) of excitatory (inhibitory) neurons, so that 1 − 2f is the proportion of nonselective neurons. We reduce the dynamics of the full network with 6 excitatory and inhibitory populations to a dynamical system with two variables representing the activations of N-methyl-D-aspartate (NMDA) conductances (in terms of fraction of channels open) for synapses originating from two choice-selective excitatory populations4. The model reduction to two dimensions leverages the timescale separation between decay time constants of the slow NMDA (~100 ms) and fast γ-aminobutyric acid  and α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid  receptors. The slow NMDA dynamics dominate the time evolution of the system, and one can assume that all other variables reach their steady-state nearly instantly4. The dynamics of the NMDA activation variable for population i  are governed by:τNMDA = 0.1 s and γ = 0.641. The non-linear function Φ transforms input current xi [nA] into firing rate:a = 270 nC−1, b = 108 Hz, and d = 0.154 s. The input to population i is:j refers to the other excitatory population. The complexity of the circuit structure, including interactions between all selective and nonselective excitatory and inhibitory neurons, is collapsed into two-dimensional model through the variables α1, α2, and Ii0, as described in the section Circuit Structure below.Our mean-field model accounts for interactions among 6 populations: 3 excitatory (2 choice selective and 1 nonselective) and 3 inhibitory (2 choice selective and 1 nonselective). Including nonselective neurons in the model is consistent with previous workμ. We define the strength of evidence for one versus the other choice as stimulus coherence c, which can range between −100% and 100%. For population i the stimulus is then defined as:The stimulus μ to 40 Hz. Noise is introduced through the inputs Iη,i to the two excitatory populations filtered through fast synaptic activation of AMPA receptors:τAMPA is 0.002 s and η(t) is a white Gaussian noise with zero mean and standard deviation 0.02 nA. We performed numerical simulations using the Euler method with a 2 ms time step.For all cases, we set α1, α2 between the two excitatory populations, and the background currents Ii0,. This reduced model is based on approximating the firing rates of all three inhibitory populations (two choice-selective and one nonselective) and of the nonselective excitatory population as linear functions of their inputs. Thus, the firing rates of these populations change linearly in response to changes in the firing rates of the two explicitly modeled excitatory populations E1 and E24. We define α1 as a term which describes how activity S1(2) from the excitatory population E1(2) filters through the circuit (i.e. via E2(1), E0, I0, I1, I2, and feeding back onto itself) to impact its own firing rate. Similarly, α2 describes how the activity S1(2) filters through the circuit to impact the firing rate of the opposite excitatory population. Ii0, describes the net input from the population activity that does not depend on the activity of E1 or E2. Thus, this model accounts for interactions between all six populations with only two dynamical system equations Eq. :The effective interaction strengths α1 account for the effect of an excitatory population’s activity on its own activity filtered via (a) direct self-coupling, (b) the activity of the inhibitory population with the same choice selectivity, (c) the activity of the inhibitory population with the opposite choice selectivity, and (d) the activity of nonselective inhibitory neurons. Similarly, α2 describes the influence of one excitatory population’s activity onto the other fed through all other populations in the circuit and also consists of four components α2 = λ2(αa2 + αb2 + αc2 + αd2):These components of α2 account for the effect on an excitatory population’s activity from the oppositely selective excitatory population’s activity filtered via (a) direct coupling, (b) the activity of the inhibitory population with the same selectivity, (c) the activity of the inhibitory population with the opposite selectivity, and (d) the activity of nonselective inhibitory neurons. The effects of nonselective neurons and external background inputs are described by Ii0, = λI:The components of i from the nonselective excitatory population filtered via (a) direct coupling, (b) the nonselective inhibitory population, (c) the inhibitory population with the same choice selectivity, (d) the inhibitory population with the opposite selectivity. The term ψ accounts for the NMDA activation of nonselective excitatory neurons. We calculated the firing rate of inhibitory populations as ΦI,1(2) = α1,IS1(2) + α2,IS2(1) + I0,II, where:These terms account for the input to the excitatory population EAll parameter values are provided in Table 32, whereas many other studies assign a choice at random on trials when the network does not reach the decision threshold45. The random assignment of choices on invalid trials can conceal differences in network dynamics, making distinct dynamical regimes indistinguishable in psychometric functions45.We considered a trial to be valid if the following criteria were met: (i) the firing rate difference between the two choice selective excitatory populations was less than 5 Hz for the entire period prior to stimulus onset, (ii) the firing rate difference was above the decision threshold of 15 Hz for at least one time step during the stimulus period and the time point following stimulus offset. Fraction completed trials for each stimulus level was defined as the number of valid trials out of all trials presented. Only valid trials were considered for computing chronometric and psychometric functions. Our treatment of invalid trials is more conservative than in many other studies, as we report invalid trials as a separate behavioral outcome different from correct or incorrect decision−6. To identify the stability of the fixed points, we computed the Jacobian matrix analytically and found its eigenvalues numerically using the eig function in MatLab. For the saddle points, τslow is the inverse of the positive eigenvalue of the Jacobian matrix.We analyzed the mean-field model to find null-clines and fixed points using MatLab’s fsolve function with the Levenberg-Marquant algorithm and a tolerance of 1 × 10Recurrent neural networks (RNNs) were composed of 100 excitatory and 25 inhibitory units. We obtained the same results with networks twice as large  nonlinearity sE(I)+, where sE(I) sets the excitability of the excitatory or inhibitory units. xin is the input activation and u(t) is the instantaneous input. The time constants of recurrent units and inputs are set by αr and αin. Weights within and between units are housed in the matricies WEE, WEI, WIE, WII. Only the excitatory units receive projections from the input and project to the output through Win and Wout, respectively.Here u(t) =  representing sensory evidence:RNNs received two input streams μ = 3.2 was fixed and stimulus difficulty was set by c which ranged between − 20 and 20.The stimulus period was 21 time steps and σin(t) that are sampled from a Gaussian distribution. We ensure that each element has a standard deviation σ0,r and σ0,in via scaling:The recurrent and input noise are modeled by the elements of z  and targets T . We set the entries in T to the baseline value of 0.2 and, following a stimulus onset, raise the entries to 1 for the output corresponding to the correct choice. This target is designed to train the network to remain in a low activity state until stimulated and elevate the correct output in response to a stimulus. Half of training trials were catch trials, on which no stimulus was presented and target values remained at 0.2 throughout the trial. The training batch consisted of Ntrial = 200 trials which were randomly generated every training epoch. Within the training batch, noncatch trials were equally divided between possible choices and the difficulty was randomly sampled.The goal of RNN training is to minimize the difference between the output wμ = 0.0375 and scale wσ = 0.5 for excitatory weights WEE, WEI, and θwμ and scale wσ for inhibitory weights WIE, WII. The scaling factor θ = NEsE/NIsI adjusts the strength of inhibitory connections to offset for differences in the number and excitability between excitatory and inhibitory units. Input and output weights Win, Wout were randomly initialized from a uniform distribution and then values were normalized so the weights associated with each input and output summed to 1 across units. All weights were trained via back-propagation through time to minimize the loss function:Recurrent network weights were randomly initialized from a Gamma distribution with a shape x is a concatenation of xE and xI of the size Ntrial × Ntime × (NE + NI), and W is a concatenation of WEE, WEI, WIE, and WII of the size (NE + NI) × (NE + NI). To encourage the network to integrate the stimulus for extended time, we used a mask M , where entries were zero during the stimulus period so that time points during the stimulus were not considered when calculating the error term of the loss function. On catch trials, all entries of M were set to 1. The hyperparameter λx = 0.1 controls the amount of L2 regularization intended to minimize the activation of each unit. The hyperparameter λw = 1.0 controls the amount of L1 regularization applied to weights. We updated the weights by stochastic gradient descent using the ADAM optimizer in PyTorch and Python 3.7 with a learning rate 0.01. During training, the norm of the gradient was clipped at 1.Here WEE, WEI, WIE, WII, Win, and Wout were set to 0 after every training step. We prevent self-connections by elementwise multiplying WEE and WII by (1 − I), where I is the identity matrix and 1 is a matrix of 1s, after every training step.To maintain the identity of excitatory and inhibitory units and to keep the input and output weights positive, all negative elements of We terminated RNN training based on its task performance. We tested RNN performance on a validation batch of trials after every training epoch. Each validation batch consisted of 100 trials with stimulus strength ranging between −20 and 20 in steps of 2. The network registered a decision when the difference between the output variables was above a threshold of 0.25. Trials were considered valid if at least 75% of the prestimulus period was below the decision threshold and at least 50% of the post stimulus period was above the decision threshold. Overall performance was measured as the fraction of correct choices out of all trials except for the ambiguous case where stimulus was equal to 0. We compute the accuracy and the psychometric function only using valid trials. We terminated training when a network’s overall performance reached 85%. RNN parameter values are shown in Table 21. For each unit, we computed the receiver operating characteristic (ROC) using the roc function and the area under the ROC curve (AUCROC) using the trapz function in Matlab. A unit with the same activity for either choice will have an AUCROC equal to 0.5, thus our choice selectivity measure was defined by AUCROC − 0.5. To identify significantly selective units, we compared AUCROC to a shuffled distribution generated from that unit’s activity by shuffling the choice outcomes 150 times. We considered units to be choice selective if their AUCROC fell within the lowest or highest 2.5% percentiles of the shuffled AUCROC distribution.After training, we analyzed the activity of excitatory and inhibitory RNN units to quantify their choice selectivity. Our metric is based on the ability to decode the choice registered by the network based on the activity of the unit at the time point immediately following stimulus offsetγ as:We measured the specificity of connections between choice selective units in RNNs. For each connection class , we computed γ used in the mean-field model. To assess significance of correlations between γ for the 4 connection classes, we computed a shuffled distribution constructed by shuffling the network labels 5000 times.This expression is identical to the specificity νI0, by a small amount within the range  around a baseline. We used two baseline values of νI0,: 11.5 for low-inhibitory regime and 14 for high-inhibitory regime. In RNNs, we delivered perturbations in a similar manner, where we delivered a constant input within the range   during the stimulus period.We perturbed activity of inhibitory neurons by delivering the same constant input to all inhibitory neurons during the stimulus period. In the mean-field model, we modified the parameter 1 and E2  are governed by:1 and I2  are governed by:To model the effects of inhibitory-inhibitory specificity and dynamics of inhibitory synapses, we developed a simplified version of our model which explicitly modeled the activity of selective inhibitory populations. In this model, the dynamics of NMDA synapses for excitatory populations Ex) is of the form Eq.  receive stimulus information through Iν,i which is implemented as in the two-variable model (Eq. (ν(t) set to 0.2 nA.Only excitatory populations (l to Eq. . Noise idel (Eq. ) with thγII which can range between . The synaptic parameters JNMDA,E, JNMDA,I, JGABA,E, JGABA,I, and the background input currents I0,E(I) were chosen so that the firing rate dynamics of E1 and E2 matched that of the two-variable model on a noiseless trial with a stimulus strength of 0.05 using PyABC parameter inference51. The values of these parameters are defined in Table The weight parameters Further information on research design is available in the Supplementary InformationPeer Review FileReporting Summary


In the complex tumor microenvironment, TGFβ is a pleiotropic cytokine involved in regulating cellular processes such as cancer cell proliferation, apoptosis and metastasis. TGFβ defines three subtypes , of which TGFβ is highly expressed in many cancers, especially those showing high dissemination potential. In addition, increased expression of TGFβ in multiple cancers is usually positively correlated with epithelial mesenchymal transition (EMT) and coordinated with the expression of genes driving EMT-related genes. TGFβ signaling in the tumor microenvironment inhibits the antitumor function of multiple immune cell populations, including T cells and natural killer cells, and the resulting immunosuppression severely limits the efficacy of immune checkpoint inhibitors and other immunotherapeutic approaches. As a major pathway to enhance the efficacy of cancer immunotherapy effects, the role of TGFβ signaling inhibitors have been evaluated in many clinical trials. However, the potential functions and mechanisms of TGFβ1, TGFβ2 and TGFβ3 in gastric cancer progression and tumor immunology are unclear. In this study, we comprehensively analyzed TGFβ1, TGFβ2 and TGFβ3 and gastric cancer microenvironmental features, including immune cell infiltration, EMT, hypoxia, mutation, immunotherapy and drug treatment, based on HMUCH sequencing data (GSE184336) and public databases. We also validated the protein expression levels of TGFβ in gastric cancer tissues as well as the role of TGFβ factor in cytology experiments. This report reveals the important role of the TGFβ gene family in gastric cancer and provides possible relationships and potential mechanisms of TGFβ in gastric cancer. In the past decades, great progress has been made in surgical treatment techniques and adjuvant therapy for gastric cancer, but its prognosis is still not ideal, and cancer recurrence often occurs , 2. WithTGFβ is a powerful cytokine in the tumor microenvironment that regulates most cellular behaviors in the TME. In general, TGFβ enhances immune tolerance and suppresses inflammation, mechanisms that are often exploited during tumor evolution to evade surveillance and combat by the immune system . In addiCurrently, research on the TGFβ molecule family  has focused on TGFβ1 and TGFβ2 molecules, while studies related to TGFβ3 molecules are still relatively rare –13. To cWe collected frozen tissues from 231 patients who underwent radical gastric cancer surgery from 2016 to 2019 at the Harbin Medical University Cancer Hospital. Inclusion criteria were preoperative CT examination or gastroscopy and pathological examination to confirm gastric cancer, excluding patients with preoperative chemotherapy, radiotherapy and other adjuvant treatments. All gastric cancer tissues were examined independently by two certified pathologists to confirm the histological type. High-throughput sequencing data of the transcriptome of gastric cancer samples were uploaded to the GEO dataset (GSE184336). This study complied with the requirements of the Research Ethics Committee of the Affiliated Cancer Hospital of Harbin Medical University (2019-164-R).In addition, gene expression data for pan-cancer were downloaded from the public database The Cancer Genome Atlas (TCGA), which for gastric adenocarcinoma (STAD) also includes copy number variants, mutation data (MAF) and corresponding clinicopathological data. In the TNM staging system, T refers to the condition of the primary tumor site and the extent of adjacent tissue involvement, N refers to regional lymph node involvement, and M refers to distant metastasis. Abbreviations for Pan-cancer showed in http://www.gsea-msigdb.org/)and used to run GSVA. The optimal cutoff values of TGFβ1, TGFβ2 and TGFβ3 were determined according to the ROC curve and grouped. The limma package was used to analyze enrichment score (ES) matrices between different TGFβ subgroups to explore biological differences between patients in different TGFβ subgroups.In the GSE184336 dataset we performed GSVA calculations using the R package “GSVA” . GSVA isTo assess the gastric cancer microenvironment, the stromalscore, immunescore and ESTIMATEScore were calculated using the ESTIMATE package, where tumor purity = cos(0.6049872018 + 0.0001467884 * ESTIMATEScore) . The ssGhttp://timer.cistrome.org/) compared the relationship between different somatic copy number changes of TGFβ and immune cell infiltration (Gistic 2.0 software was used to identify somatic copy number changes . The R pltration .https://cloud.genepattern.org/gp) to analyze the response of patients with different TGFβ subgroups to immunotherapy (PD-1 and CTLA-4) (http://bioinfo.life.hust.edu.cn/ImmuCellAI) to predict the response of immune checkpoint blockade (ICB) therapy (anti-PD1 or anti-CTLA4 therapy)  . In additherapy) . Patienttherapy) .Six pairs of frozen samples of gastric cancer and adjacent normal tissues (more than 5 cm from the tumor margin) were selected, and total protein was extracted with lysis buffer containing protease inhibitors, and the concentration was determined. PVDF membranes (Merck Millipore) were blocked with 5% skim milk powder and incubated overnight at 4°C with primary antibodies . Gastric cancer tissue was paraffin-embedded and cut into 5 mm thick sections, and immunohistochemistry (IHC) staining was performed as previously described .Gastric cell line (HGC-27) was purchased from Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, and cultured at 37°C in RPMI-1640 medium containing 10% 100 U/ml penicillin and 100 U/ml streptomycin. The active recombinant proteins TGFβ1 , TGFβ2  and TGFβ3  were solubilized and added to normal medium to adjust the concentration of TGFβ factor to 10ng/mL. The gastric cancer cells cultured with TGFβ active protein were added as the TGFβ-treated group, and human serum albumin (HSA) of equal quality was added as the control group and incubated in the cell incubator for 48 hours.HGC27 cells were inoculated in 6-well plates and when the cell growth density reached 40-50%, transfection was performed using jetPRIME transfection reagent  according to the manufacturer’s protocol. siRNA sequences were shown in ® 480 Real-Time PCR System (Roche) to analyze the cDNA expression levels. The 2-ΔΔCt method was used to calculate the relative expression levels . RNA was reverse-transcribed into cDNA using reverse transcription system . The SYBR-Green (Vazyme) mixed system was then assayed in a LightCyclern levels . Transwen levels . The PCRhttps://www.r-project.org/, version 4.01) and Graphpad Pris 7.0. The P-value < 0.05 was considered statistically significant.The chi-square test was used to analyze the association between different TGFβ subgroups and clinicopathological parameters. Kaplan-Meier (KM) survival curves were used to compare the survival analysis of different subgroups, and univariate cox regression analysis was also performed on TGFβ. Pearson correlation analysis was performed to test the relationship between the different variables. The results of the experimental data were expressed as mean ± SD (standard deviation). Statistical analyses were performed in R software (https://genemania.org/), and compared the copy number changes  of different genes , notably TGFβ1, TGFβ2 and TGFβ3 were all poor prognostic factors in STAD. The KM survival analysis results showed that patients with high expression levels of TGFβ1, TGFβ2 and TGFβ3  had a shorter survival  demonstrated the regulatory network of the TGFβ signaling pathway ), and the results showed that the TGFβ signaling pathway may also be involved in apoptosis and cell cycle regulation (The GeneMANIA database was used for protein-protein interaction (PPI) network analysis of TGFβ and related genes, and the KEGG database  matrix among different TGFβ1, TGFβ2 and TGFβ3 groupings, respectively , with correlation options including none and tumor purity , while significantly negatively correlated with epithelial marker CDH1  to reduce TGFβ1, TGFβ2 and TGFβ3 gene expression in gastric cancer cells. 48 hours after transfection, we extracted RNA from all samples and qRT-PCR results showed that TGFβ1, TGFβ2 and TGFβ3 expression levels were significantly reduced , and the migration ability of gastric cancer cells was enhanced under TGFβ1, TGFβ2 and TGFβ3 active protein stimulation conditions (P < 0.05), while higher TMB was beneficial for prolonging patient survival  (We showed the differences in copy number variants (CNV) in different TGFβ groupings (P < 0.001), TGFβ2  and TGFβ3  subgroups had a higher proportion of patients responding to immunotherapy  of TGFβ gene had a greater impact on immune cell infiltration. In the present study TGFβ was significantly and positively correlated with Tregs, which promote the formation of an immunosuppressive microenvironment and attenuate the antitumor effects produced by CD4+ T cells, CD8+ T cells and NK cells through secreted TGFβ , 50. It The results of this study showed that patients in the low TGFβ expression group had higher TMB, and patients with higher TMB had longer survival. Recent studies have shown that high TMB increases the likelihood that immunogenic neoantigens expressed by tumor cells induce a response to immunotherapy. In addition, TGFβ1 and TGFβ3 were negatively associated with MATH, which is prevalent in most cancer patients and is a major driver of acquired resistance to cancer therapy , 56. In Stromal fibroblasts and other cells in tumor tissues shape the immunosuppressive environment of tumors through TGFβ signaling, inhibiting the antitumor activity of immune cells and preventing or weakening the effect of anticancer immunotherapy . TherefoFurthermore, in drug prediction of patients with different TGFβ subgroups, it was found that patients in the high TGFβ group were less sensitive to treatment with small molecule compounds, which may be related to the increased extracellular interstitial component of gastric cancer induced by high TGFβ levels. The rigid Extracellular matrix (ECM) that forms around the tumor reduces the spread of therapeutic agents to cancer cells, while the dense ECM reduces the vascular density and causes the vessels to embed in the matrix, forming a tough barrier that cannot be perfused with drugs .However there are many clinical challenges in developing TGFβ inhibitors, especially patient selection and timing of treatment. Considering the dual role of TGFβ on proliferation, TGFβ inhibitors may be beneficial in advanced tumors. It is worth noting that TGFβ plays a positive factor in SKCM, KIRC and THYM in pan-cancer analysis and should be considered carefully when using TGFβ-related signaling inhibitors. Before TGFβ inhibitors can be used clinically, a lot of research on different types of cancer is still needed. To comprehensively evaluate the efficacy of TGFβ inhibitors in immunotherapy in different cancer types and cancer stages, and whether they need to be used in combination with other immune target inhibitors. Only then can we make accurate screening and evaluation of the target population and therapeutic efficacy of TGFβ inhibitors.The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/The studies involving human participants were reviewed and approved by Research Ethics Committee of the Affiliated Cancer Hospital of Harbin Medical University. The patients/participants provided their written informed consent to participate in this study.BH and TF conceived the project and wrote the manuscript. BH and YZ participated in data analysis. JG and YLZ participated in discussion and language editing. YX reviewed the manuscript. All authors contributed to the article and approved the submitted version.This study was supported by the Harbin Science and Technology Bureau Research and Development Project of Applied Technology (No. 2017RAXXJ054) and Nn 10 Program of Harbin Medical University Cancer Hospital (No. Nn 10 PY 2017-03).We sincerely thank all participants in the study.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.