Automated Artificial Intelligence Model Trained on a Large Data Set Can Detect Pancreas Cancer on Diagnostic Computed Tomography Scans As Well As Visually Occult Preinvasive Cancer on Prediagnostic Computed Tomography Scans.

BACKGROUND & AIMS: The aims of our case-control study were (1) to develop an automated 3-dimensional (3D) Convolutional Neural Network (CNN) for detection of pancreatic ductal adenocarcinoma (PDA) on diagnostic computed tomography scans (CTs), (2) evaluate its generalizability on multi-institutional public data sets, (3) its utility as a potential screening tool using a simulated cohort with high pretest probability, and (4) its ability to detect visually occult preinvasive cancer on prediagnostic CTs. METHODS: A 3D-CNN classification system was trained using algorithmically generated bounding boxes and pancreatic masks on a curated data set of 696 portal phase diagnostic CTs with PDA and 1080 control images with a nonneoplastic pancreas. The model was evaluated on (1) an intramural hold-out test subset (409 CTs with PDA, 829 controls); (2) a simulated cohort with a case-control distribution that matched the risk of PDA in glycemically defined new-onset diabetes, and Enriching New-Onset Diabetes for Pancreatic Cancer score ≥3; (3) multi-institutional public data sets (194 CTs with PDA, 80 controls), and (4) a cohort of 100 prediagnostic CTs (i.e., CTs incidentally acquired 3-36 months before clinical diagnosis of PDA) without a focal mass, and 134 controls. RESULTS: Of the CTs in the intramural test subset, 798 (64%) were from other hospitals. The model correctly classified 360 CTs (88%) with PDA and 783 control CTs (94%), with a mean accuracy 0.92 (95% CI, 0.91-0.94), area under the receiver operating characteristic (AUROC) curve of 0.97 (95% CI, 0.96-0.98), sensitivity of 0.88 (95% CI, 0.85-0.91), and specificity of 0.95 (95% CI, 0.93-0.96). Activation areas on heat maps overlapped with the tumor in 350 of 360 CTs (97%). Performance was high across tumor stages (sensitivity of 0.80, 0.87, 0.95, and 1.0 on T1 through T4 stages, respectively), comparable for hypodense vs isodense tumors (sensitivity: 0.90 vs 0.82), different age, sex, CT slice thicknesses, and vendors (all P > .05), and generalizable on both the simulated cohort (accuracy, 0.95 [95% 0.94-0.95]; AUROC curve, 0.97 [95% CI, 0.94-0.99]) and public data sets (accuracy, 0.86 [95% CI, 0.82-0.90]; AUROC curve, 0.90 [95% CI, 0.86-0.95]). Despite being exclusively trained on diagnostic CTs with larger tumors, the model could detect occult PDA on prediagnostic CTs (accuracy, 0.84 [95% CI, 0.79-0.88]; AUROC curve, 0.91 [95% CI, 0.86-0.94]; sensitivity, 0.75 [95% CI, 0.67-0.84]; and specificity, 0.90 [95% CI, 0.85-0.95]) at a median 475 days (range, 93-1082 days) before clinical diagnosis. CONCLUSIONS: This automated artificial intelligence model trained on a large and diverse data set shows high accuracy and generalizable performance for detection of PDA on diagnostic CTs as well as for visually occult PDA on prediagnostic CTs. Prospective validation with blood-based biomarkers is warranted to assess the potential for early detection of sporadic PDA in high-risk individuals.

Association Between Body Composition Measured by Artificial Intelligence and Long-Term Sequelae After Acute Pancreatitis.

BACKGROUND/OBJECTIVES: The clinical utility of body composition in the development of complications of acute pancreatitis (AP) remains unclear. We aimed to describe the associations between body composition and the recurrence of AP. METHODS: We performed a retrospective study of patients hospitalized with AP at three tertiary care centers. Patients with computer tomography (CT) imaging of the abdomen at admission were included. A previously validated and fully automated abdominal segmentation algorithm was used for body composition analysis. Hospitalization for a recurrent episode of AP was the primary endpoint. Secondary endpoints included the development of chronic pancreatitis (CP) or diabetes mellitus (DM) in patients who were evaluated. Cox Proportional Hazards regression was used. RESULTS: From a total of 347 patients, 89 (25.6%) were hospitalized for recurrent AP (median time: 219 days). Thirty-four of 112 patients (30.4%) developed CP (median time: 311 days) and 22 of 88 (25.0%) developed DM (median time: 1104 days). After adjusting for age, male sex, first episode of AP, BUN, and severity of AP, we found that obesity, body mass index, alcohol pancreatitis, and gallstone pancreatitis were significantly associated with a recurrent episode of AP. Body composition was not associated with recurrent AP. In unadjusted analysis, subcutaneous adipose tissue (SAT) (HR 0.87 per 10 cm2, p = 0.002) was associated with CP. Skeletal muscle (SM) mass approached significance for CP (p = 0.0546). Intermuscular adipose tissue (IMAT) (HR 1.45 per 5 cm2, p = 0.0264) was associated with DM. CONCLUSION: Body composition was not associated with having a recurrent AP. At follow-up, 30% and 25% of evaluated patients developed CP and DM, respectively. A higher SAT and IMAT were associated with a lower incidence of CP and higher incidence of DM, respectively.

Sarcopenia identified by computed tomography imaging using a deep learning-based segmentation approach impacts survival in patients with newly diagnosed multiple myeloma.

BACKGROUND: Sarcopenia increases with age and is associated with poor survival outcomes in patients with cancer. By using a deep learning-based segmentation approach, clinical computed tomography (CT) images of the abdomen of patients with newly diagnosed multiple myeloma (NDMM) were reviewed to determine whether the presence of sarcopenia had any prognostic value. METHODS: Sarcopenia was detected by accurate segmentation and measurement of the skeletal muscle components present at the level of the L3 vertebrae. These skeletal muscle measurements were further normalized by the height of the patient to obtain the skeletal muscle index for each patient to classify them as sarcopenic or not. RESULTS: The study cohort consisted of 322 patients of which 67 (28%) were categorized as having high risk (HR) fluorescence in situ hybridization (FISH) cytogenetics. A total of 171 (53%) patients were sarcopenic based on their peri-diagnosis standard-dose CT scan. The median overall survival (OS) and 2-year mortality rate for sarcopenic patients was 44 months and 40% compared to 90 months and 18% for those not sarcopenic, respectively (p < .0001 for both comparisons). In a multivariable model, the adverse prognostic impact of sarcopenia was independent of International Staging System stage, age, and HR FISH cytogenetics. CONCLUSIONS: Sarcopenia identified by a machine learning-based convolutional neural network algorithm significantly affects OS in patients with NDMM. Future studies using this machine learning-based methodology of assessing sarcopenia in larger prospective clinical trials are required to validate these findings.

Volumetric Pancreas Segmentation on Computed Tomography: Accuracy and Efficiency of a Convolutional Neural Network Versus Manual Segmentation in 3D Slicer in the Context of Interreader Variability of Expert Radiologists.

PURPOSE: This study aimed to compare accuracy and efficiency of a convolutional neural network (CNN)-enhanced workflow for pancreas segmentation versus radiologists in the context of interreader reliability. METHODS: Volumetric pancreas segmentations on a data set of 294 portal venous computed tomographies were performed by 3 radiologists (R1, R2, and R3) and by a CNN. Convolutional neural network segmentations were reviewed and, if needed, corrected ("corrected CNN [c-CNN]" segmentations) by radiologists. Ground truth was obtained from radiologists' manual segmentations using simultaneous truth and performance level estimation algorithm. Interreader reliability and model's accuracy were evaluated with Dice-Sorenson coefficient (DSC) and Jaccard coefficient (JC). Equivalence was determined using a two 1-sided test. Convolutional neural network segmentations below the 25th percentile DSC were reviewed to evaluate segmentation errors. Time for manual segmentation and c-CNN was compared. RESULTS: Pancreas volumes from 3 sets of segmentations (manual, CNN, and c-CNN) were noninferior to simultaneous truth and performance level estimation-derived volumes [76.6 cm 3 (20.2 cm 3 ), P < 0.05]. Interreader reliability was high (mean [SD] DSC between R2-R1, 0.87 [0.04]; R3-R1, 0.90 [0.05]; R2-R3, 0.87 [0.04]). Convolutional neural network segmentations were highly accurate (DSC, 0.88 [0.05]; JC, 0.79 [0.07]) and required minimal-to-no corrections (c-CNN: DSC, 0.89 [0.04]; JC, 0.81 [0.06]; equivalence, P < 0.05). Undersegmentation (n = 47 [64%]) was common in the 73 CNN segmentations below 25th percentile DSC, but there were no major errors. Total inference time (minutes) for CNN was 1.2 (0.3). Average time (minutes) taken by radiologists for c-CNN (0.6 [0.97]) was substantially lower compared with manual segmentation (3.37 [1.47]; savings of 77.9%-87% [ P < 0.0001]). CONCLUSIONS: Convolutional neural network-enhanced workflow provides high accuracy and efficiency for volumetric pancreas segmentation on computed tomography.

GluN2B subunit deletion reveals key role in acute and chronic ethanol sensitivity of glutamate synapses in bed nucleus of the stria terminalis.

The bed nucleus of the stria terminalis (BNST) is a critical region for alcohol/drug-induced negative affect and stress-induced reinstatement. NMDA receptor (NMDAR)-dependent plasticity, such as long-term potentiation (LTP), has been postulated to play key roles in alcohol and drug addiction; yet, to date, little is understood regarding the mechanisms underlying LTP of the BNST, or its regulation by ethanol. Acute and chronic exposure to ethanol modulates glutamate transmission via actions on NMDARs. Despite intense investigation, tests of subunit specificity of ethanol actions on NMDARs using pharmacological approaches have produced mixed results. Thus, we use a conditional GluN2B KO mouse line to assess both basal and ethanol-dependent function of this subunit at glutamate synapses in the BNST. Deletion of GluN2B eliminated LTP, as well as actions of ethanol on NMDAR function. Further, we show that chronic ethanol exposure enhances LTP formation in the BNST. Using KO-validated pharmacological approaches with Ro25-6981 and memantine, we provide evidence suggesting that chronic ethanol exposure enhances LTP in the BNST via paradoxical extrasynaptic NMDAR involvement. These findings demonstrate that GluN2B is a key point of regulation for ethanol's actions and suggest a unique role of extrasynaptic GluN2B-containing receptors in facilitating LTP.

Transition-zone PSA-density calculated from MRI deep learning prostate zonal segmentation model for prediction of clinically significant prostate cancer.

PURPOSE: To develop a deep learning (DL) zonal segmentation model of prostate MR from T2-weighted images and evaluate TZ-PSAD for prediction of the presence of csPCa (Gleason score of 7 or higher) compared to PSAD. METHODS: 1020 patients with a prostate MRI were randomly selected to develop a DL zonal segmentation model. Test dataset included 20 cases in which 2 radiologists manually segmented both the peripheral zone (PZ) and TZ. Pair-wise Dice index was calculated for each zone. For the prediction of csPCa using PSAD and TZ-PSAD, we used 3461 consecutive MRI exams performed in patients without a history of prostate cancer, with pathological confirmation and available PSA values, but not used in the development of the segmentation model as internal test set and 1460 MRI exams from PI-CAI challenge as external test set. PSAD and TZ-PSAD were calculated from the segmentation model output. The area under the receiver operating curve (AUC) was compared between PSAD and TZ-PSAD using univariate and multivariate analysis (adjusts age) with the DeLong test. RESULTS: Dice scores of the model against two radiologists were 0.87/0.87 and 0.74/0.72 for TZ and PZ, while those between the two radiologists were 0.88 for TZ and 0.75 for PZ. For the prediction of csPCa, the AUCs of TZPSAD were significantly higher than those of PSAD in both internal test set (univariate analysis, 0.75 vs. 0.73, p < 0.001; multivariate analysis, 0.80 vs. 0.78, p < 0.001) and external test set (univariate analysis, 0.76 vs. 0.74, p < 0.001; multivariate analysis, 0.77 vs. 0.75, p < 0.001 in external test set). CONCLUSION: DL model-derived zonal segmentation facilitates the practical measurement of TZ-PSAD and shows it to be a slightly better predictor of csPCa compared to the conventional PSAD. Use of TZ-PSAD may increase the sensitivity of detecting csPCa by 2-5% for a commonly used specificity level.

AMPAR-independent effect of striatal αCaMKII promotes the sensitization of cocaine reward.

Changes in CaMKII-regulated synaptic excitability are a means through which experience may modify neuronal function and shape behavior. While behavior in rodent addiction models is linked with CaMKII activity in the nucleus accumbens (NAc) shell, the key cellular adaptations that forge this link are unclear. Using a mouse strain with striatal-specific expression of autonomously active CaMKII (T286D), we demonstrate that while persistent CaMKII activity induces behaviors comparable to those in mice repeatedly exposed to psychostimulants, it is insufficient to increase AMPAR-mediated synaptic strength in NAc shell. However, autonomous CaMKII upregulates A-type K(+) current (IA) and decreases firing in shell neurons. Importantly, inactivating the transgene with doxycycline eliminates both the IA-mediated firing decrease and the elevated behavioral response to cocaine. This study identifies CaMKII regulation of IA in NAc shell neurons as a novel cellular contributor to the sensitization of cocaine reward.

Distinct forms of Gq-receptor-dependent plasticity of excitatory transmission in the BNST are differentially affected by stress.

Long-term depression (LTD) is an important synaptic mechanism for limiting excitatory influence over circuits subserving cognitive and emotional behavior. A major means of LTD induction is through the recruitment of signaling via G(q)-linked receptors activated by norepinephrine (NE), acetylcholine, and glutamate. Receptors from these transmitter families have been proposed to converge on a common postsynaptic LTD maintenance mechanism, such that hetero- and homosynaptic induction produce similar alterations in glutamate synapse efficacy. We report that in the dorsolateral and ventrolateral bed nucleus of the stria terminalis (BNST), recruitment of G(q)-linked receptors by glutamate or NE initiates mechanistically distinct forms of postsynaptically maintained LTD and these LTDs are differentially regulated by stress exposure. In particular, we show that although both mGluR5- and alpha(1)-adrenergic receptor (AR)-dependent LTDs involve postsynaptic endocytosis, the alpha(1)-AR-initiated LTD exclusively involves modulation of signaling through calcium-permeable AMPA receptors. Further, alpha(1)-AR- but not mGluR5- dependent LTD is disrupted by restraint stress. alpha(1)-AR LTD is also impaired in mice chronically exposed to ethanol. These data thus suggest that in the BNST, NE- and glutamate-activated G(q)-linked signaling pathways differentially tune glutamate synapse efficacy in response to stress.

Asymmetric cortical projections to striatal direct and indirect pathways distinctly control actions.

The striatal direct and indirect pathways constitute the core for basal ganglia function in action control. Although both striatal D1- and D2-spiny projection neurons (SPNs) receive excitatory inputs from the cerebral cortex, whether or not they share inputs from the same cortical neurons, and how pathway-specific corticostriatal projections control behavior remain largely unknown. Here using a new G-deleted rabies system in mice, we found that more than two-thirds of excitatory inputs to D2-SPNs also target D1-SPNs, while only one-third do so vice versa. Optogenetic stimulation of striatal D1- vs. D2-SPN-projecting cortical neurons differently regulate locomotion, reinforcement learning and sequence behavior, implying the functional dichotomy of pathway-specific corticostriatal subcircuits. These results reveal the partially segregated yet asymmetrically overlapping cortical projections on striatal D1- vs. D2-SPNs, and that the pathway-specific corticostriatal subcircuits distinctly control behavior. It has important implications in a wide range of neurological and psychiatric diseases affecting cortico-basal ganglia circuitry.

Clinical Implementation of an Artificial Intelligence Algorithm for Magnetic Resonance-Derived Measurement of Total Kidney Volume.

OBJECTIVE: To evaluate the performance of an internally developed and previously validated artificial intelligence (AI) algorithm for magnetic resonance (MR)-derived total kidney volume (TKV) in autosomal dominant polycystic kidney disease (ADPKD) when implemented in clinical practice. PATIENTS AND METHODS: The study included adult patients with ADPKD seen by a nephrologist at our institution between November 2019 and January 2021 and undergoing an MR imaging examination as part of standard clinical care. Thirty-three nephrologists ordered MR imaging, requesting AI-based TKV calculation for 170 cases in these 161 unique patients. We tracked implementation and performance of the algorithm over 1 year. A radiologist and a radiology technologist reviewed all cases (N=170) for quality and accuracy. Manual editing of algorithm output occurred at radiology or radiology technologist discretion. Performance was assessed by comparing AI-based and manually edited segmentations via measures of similarity and dissimilarity to ensure expected performance. We analyzed ADPKD severity class assignment of algorithm-derived vs manually edited TKV to assess impact. RESULTS: Clinical implementation was successful. Artificial intelligence algorithm-based segmentation showed high levels of agreement and was noninferior to interobserver variability and other methods for determining TKV. Of manually edited cases (n=84), the AI-algorithm TKV output showed a small mean volume difference of -3.3%. Agreement for disease class between AI-based and manually edited segmentation was high (five cases differed). CONCLUSION: Performance of an AI algorithm in real-life clinical practice can be preserved if there is careful development and validation and if the implementation environment closely matches the development conditions.

Cocaine experience controls bidirectional synaptic plasticity in the nucleus accumbens.

Plasticity of glutamatergic synapses is a fundamental mechanism through which experience changes neural function to impact future behavior. In animal models of addiction, glutamatergic signaling in the nucleus accumbens (NAc) exerts powerful control over drug-seeking behavior. However, little is known about whether, how or when experience with drugs may trigger synaptic plasticity in this key nucleus. Using whole-cell synaptic physiology in NAc brain slices, we demonstrate that a progression of bidirectional changes in glutamatergic synaptic strength occurs after repeated in vivo exposure to cocaine. During a protracted drug-free period, NAc neurons from cocaine-experienced mice develop a robust potentiation of AMPAR-mediated synaptic transmission. However, a single re-exposure to cocaine during extended withdrawal becomes a potent stimulus for synaptic depression, abruptly reversing the initial potentiation. These enduring modifications in AMPAR-mediated responses and plasticity may provide a neural substrate for disrupted processing of drug-related stimuli in drug-experienced individuals.

D1 dopamine receptor activation of NFAT-mediated striatal gene expression.

Exposure to drugs of abuse activates gene expression and protein synthesis that result in long-lasting adaptations in striatal signaling. Therefore, identification of the transcription factors that couple drug exposure to gene expression is of particular importance. Members of the nuclear factor of activated T-cells (NFATc) family of transcription factors have recently been implicated in shaping neuronal function throughout the rodent nervous system. Here we demonstrate that regulation of NFAT-mediated gene expression may also be a factor in drug-induced changes to striatal functioning. In cultured rat striatal neurons, stimulation of D1 dopamine receptors induces NFAT-dependent transcription through activation of L-type calcium channels. Additionally, the genes encoding inositol-1,4,5-trisphosphate receptor type 1 and glutamate receptor subunit 2 are regulated by striatal NFATc4 activity. Consistent with these in-vitro data, repeated exposure to cocaine triggers striatal NFATc4 nuclear translocation and the up-regulation of inositol-1,4,5-trisphosphate receptor type 1 and glutamate receptor subunit 2 gene expression in vivo, suggesting that cocaine-induced increases in gene expression may be partially mediated through activation of NFAT-dependent transcription. Collectively, these findings reveal a novel molecular pathway that may contribute to the enduring modifications in striatal functioning that occur following the administration of drugs of abuse.

Differential inputs to striatal cholinergic and parvalbumin interneurons imply functional distinctions.

Striatal cholinergic (ChAT) and parvalbumin (PV) interneurons exert powerful influences on striatal function in health and disease, yet little is known about the organization of their inputs. Here using rabies tracing, electrophysiology and genetic tools, we compare the whole-brain inputs to these two types of striatal interneurons and dissect their functional connectivity in mice. ChAT interneurons receive a substantial cortical input from associative regions of cortex, such as the orbitofrontal cortex. Amongst subcortical inputs, a previously unknown inhibitory thalamic reticular nucleus input to striatal PV interneurons is identified. Additionally, the external segment of the globus pallidus targets striatal ChAT interneurons, which is sufficient to inhibit tonic ChAT interneuron firing. Finally, we describe a novel excitatory pathway from the pedunculopontine nucleus that innervates ChAT interneurons. These results establish the brain-wide direct inputs of two major types of striatal interneurons and allude to distinct roles in regulating striatal activity and controlling behavior.

Efficient Generation of CA3 Neurons from Human Pluripotent Stem Cells Enables Modeling of Hippocampal Connectivity In Vitro.

Despite widespread interest in using human induced pluripotent stem cells (hiPSCs) in neurological disease modeling, a suitable model system to study human neuronal connectivity is lacking. Here, we report a comprehensive and efficient differentiation paradigm for hiPSCs that generate multiple CA3 pyramidal neuron subtypes as detected by single-cell RNA sequencing (RNA-seq). This differentiation paradigm exhibits characteristics of neuronal network maturation, and rabies virus tracing revealed synaptic connections between stem cell-derived dentate gyrus (DG) and CA3 neurons in vitro recapitulating the neuronal connectivity within the hippocampus. Because hippocampal dysfunction has been implicated in schizophrenia, we applied DG and CA3 differentiation paradigms to schizophrenia-patient-derived hiPSCs. We detected reduced activity in DG-CA3 co-culture and deficits in spontaneous and evoked activity in CA3 neurons from schizophrenia-patient-derived hiPSCs. Our approach offers critical insights into the network activity aspects of schizophrenia and may serve as a promising tool for modeling diseases with hippocampal vulnerability. VIDEO ABSTRACT.

Genetic-Based Dissection Unveils the Inputs and Outputs of Striatal Patch and Matrix Compartments.

The striatum contains neurochemically defined compartments termed patches and matrix. Previous studies suggest patches preferentially receive limbic inputs and project to dopamine neurons in substantia nigra pars compacta (SNc), whereas matrix neurons receive sensorimotor inputs and do not innervate SNc. Using BAC-Cre transgenic mice with viral tracing techniques, we mapped brain-wide differences in the input-output organization of the patch/matrix. Findings reveal a displaced population of striatal patch neurons termed "exo-patch," which reside in matrix zones but have neurochemistry, connectivity, and electrophysiological characteristics resembling patch neurons. Contrary to previous studies, results show patch/exo-patch and matrix neurons receive both limbic and sensorimotor information. A novel inhibitory projection from bed nucleus of the stria terminalis to patch/exo-patch neurons was revealed. Projections to SNc were found to originate from patch/exo-patch and matrix neurons. These findings redefine patch/matrix beyond traditional neurochemical topography and reveal new principles about their input-output connectivity, providing a foundation for future functional studies.

Loss of motoneuron-specific microRNA-218 causes systemic neuromuscular failure.

Dysfunction of microRNA (miRNA) metabolism is thought to underlie diseases affecting motoneurons. One miRNA, miR-218, is abundantly and selectively expressed by developing and mature motoneurons. Here we show that mutant mice lacking miR-218 die neonatally and exhibit neuromuscular junction defects, motoneuron hyperexcitability, and progressive motoneuron cell loss, all of which are hallmarks of motoneuron diseases such as amyotrophic lateral sclerosis and spinal muscular atrophy. Gene profiling reveals that miR-218 modestly represses a cohort of hundreds of genes that are neuronally enriched but are not specific to a single neuron subpopulation. Thus, the set of messenger RNAs targeted by miR-218, designated TARGET(218), defines a neuronal gene network that is selectively tuned down in motoneurons to prevent neuromuscular failure and neurodegeneration.

Genetic inhibition of CaMKII in dorsal striatal medium spiny neurons reduces functional excitatory synapses and enhances intrinsic excitability.

Ca(2+)/calmodulin-dependent protein kinase II (CaMKII) is abundant in striatal medium spiny neurons (MSNs). CaMKII is dynamically regulated by changes in dopamine signaling, as occurs in Parkinson's disease as well as addiction. Although CaMKII has been extensively studied in the hippocampus where it regulates excitatory synaptic transmission, relatively little is known about how it modulates neuronal function in the striatum. Therefore, we examined the impact of selectively overexpressing an EGFP-fused CaMKII inhibitory peptide (EAC3I) in striatal medium spiny neurons (MSNs) using a novel transgenic mouse model. EAC3I-expressing cells exhibited markedly decreased excitatory transmission, indicated by a decrease in the frequency of spontaneous excitatory postsynaptic currents (sEPSCs). This decrease was not accompanied by changes in the probability of release, levels of glutamate at the synapse, or changes in dendritic spine density. CaMKII regulation of the AMPA receptor subunit GluA1 is a major means by which the kinase regulates neuronal function in the hippocampus. We found that the decrease in striatal excitatory transmission seen in the EAC3I mice is mimicked by deletion of GluA1. Further, while CaMKII inhibition decreased excitatory transmission onto MSNs, it increased their intrinsic excitability. These data suggest that CaMKII plays a critical role in setting the excitability rheostat of striatal MSNs by coordinating excitatory synaptic drive and the resulting depolarization response.