Machine learning model comparison for freezing of gait prediction in advanced Parkinson's disease.

Introduction: Freezing of gait (FOG) is a paroxysmal motor phenomenon that increases in prevalence as Parkinson's disease (PD) progresses. It is associated with a reduced quality of life and an increased risk of falls in this population. Precision-based detection and classification of freezers are critical to developing tailored treatments rooted in kinematic assessments. Methods: This study analyzed instrumented stand-and-walk (SAW) trials from advanced PD patients with STN-DBS. Each patient performed two SAW trials in their OFF Medication-OFF DBS state. For each trial, gait summary statistics from wearable sensors were analyzed by machine learning classification algorithms. These algorithms include k-nearest neighbors, logistic regression, naïve Bayes, random forest, and support vector machines (SVM). Each of these models were selected for their high interpretability. Each algorithm was tasked with classifying patients whose SAW trials MDS-UPDRS FOG subscore was non-zero as assessed by a trained movement disorder specialist. These algorithms' performance was evaluated using stratified five-fold cross-validation. Results: A total of 21 PD subjects were evaluated (average age 64.24 years, 16 males, mean disease duration of 14 years). Fourteen subjects had freezing of gait in the OFF MED/OFF DBS. All machine learning models achieved statistically similar predictive performance (p < 0.05) with high accuracy. Analysis of random forests' feature estimation revealed the top-ten spatiotemporal predictive features utilized in the model: foot strike angle, coronal range of motion [trunk and lumbar], stride length, gait speed, lateral step variability, and toe-off angle. Conclusion: These results indicate that machine learning effectively classifies advanced PD patients as freezers or nonfreezers based on SAW trials in their non-medicated/non-stimulated condition. The machine learning models, specifically random forests, not only rely on but utilize salient spatial and temporal gait features for FOG classification.

Differential Responses to Low- and High-Frequency Subthalamic Nucleus Deep Brain Stimulation on Sensor-Measured Components of Bradykinesia in Parkinson's Disease.

INTRODUCTION: The current approach to assessing bradykinesia in Parkinson's Disease relies on the Unified Parkinson's Disease Rating Scale (UPDRS), which is a numeric scale. Inertial sensors offer the ability to probe subcomponents of bradykinesia: motor speed, amplitude, and rhythm. Thus, we sought to investigate the differential effects of high-frequency compared to low-frequency subthalamic nucleus (STN) deep brain stimulation (DBS) on these quantified facets of bradykinesia. METHODS: We recruited advanced Parkinson's Disease subjects with a chronic bilateral subthalamic nucleus (STN) DBS implantation to a single-blind stimulation trial where each combination of medication state (OFF/ON), electrode contacts, and stimulation frequency (60 Hz/180 Hz) was assessed. The Kinesia One sensor system was used to measure upper limb bradykinesia. For each stimulation trial, subjects performed extremity motor tasks. Sensor data were recorded continuously. We identified STN DBS parameters that were associated with improved upper extremity bradykinesia symptoms using a mixed linear regression model. RESULTS: We recruited 22 subjects (6 females) for this study. The 180 Hz STN DBS (compared to the 60 Hz STN DBS) and dopaminergic medications improved all subcomponents of upper extremity bradykinesia (motor speed, amplitude, and rhythm). For the motor rhythm subcomponent of bradykinesia, ventral contacts yielded improved symptom improvement compared to dorsal contacts. CONCLUSION: The differential impact of high- and low-frequency STN DBS on the symptoms of bradykinesia may advise programming for these patients but warrants further investigation. Wearable sensors represent a valuable addition to the armamentarium that furthers our ability to conduct objective, quantitative clinical assessments.

Towards Faster Gene Expression Prediction via Dimensionality Reduction and Feature Selection.

The majority of genes have a genetic component to their expression. Elastic nets have been shown effective at predicting tissue-specific, individual-level gene expression from genotype data. We apply principal component analysis (PCA), linkage disequilibrium pruning, or the combination of the two to reduce, or generate, a lower-dimensional representation of the genetic variants used as inputs to the elastic net models for the prediction of gene expression. Our results show that, in general, elastic nets attain their best performance when all genetic variants are included as inputs; however, a relatively low number of principal components can effectively summarize the majority of genetic variation while reducing the overall computation time. Specifically, 100 principal components reduce the computational time of the models by over 80% with only an 8% loss in R2. Finally, linkage disequilibrium pruning does not effectively reduce the genetic variants for predicting gene expression. As predictive models are commonly made for over 27,000 genes for more than 50 tissues, PCA may provide an effective method for reducing the computational burden of gene expression analysis.

Differential spatiotemporal gait effects with frequency and dopaminergic modulation in STN-DBS.

Objective: The spatiotemporal gait changes in advanced Parkinson's disease (PD) remain a treatment challenge and have variable responses to L-dopa and subthalamic deep brain stimulation (STN-DBS). The purpose of this study was to determine whether low-frequency STN-DBS (LFS; 60 Hz) elicits a differential response to high-frequency STN-DBS (HFS; 180 Hz) in spatiotemporal gait kinematics. Methods: Advanced PD subjects with chronic STN-DBS were evaluated in both the OFF and ON medication states with LFS and HFS stimulation. Randomization of electrode contact pairs and frequency conditions was conducted. Instrumented Stand and Walk assessments were carried out for every stimulation/medication condition. LM-ANOVA was employed for analysis. Results: Twenty-two PD subjects participated in the study, with a mean age (SD) of 63.9 years. Significant interactions between frequency (both LFS and HFS) and electrode contact pairs (particularly ventrally located contacts) were observed for both spatial (foot elevation, toe-off angle, stride length) and temporal (foot speed, stance, single limb support (SLS) and foot swing) gait parameters. A synergistic effect was also demonstrated with L-dopa and both HFS and LFS for right SLS, left stance, left foot swing, right toe-off angle, and left arm range of motion. HFS produced significant improvement in trunk and lumbar range of motion compared to LFS. Conclusion: The study provides evidence of synergism of L-dopa and STN-DBS on lower limb spatial and temporal measures in advanced PD. HFS and LFS STN-DBS produced equivalent effects among all other tested lower limb gait features. HFS produced significant trunk and lumbar kinematic improvements.

Mechanical Activation and Cation Site Disorder in MgAl2O4.

The synthesis and crystallographic site occupancy were investigated for MgAl2O4 with and without mechanical activation of the precursor powders. Heating to 1200 °C or higher resulted in the formation of a single spinel phase regardless of whether the powders were mechanically activated or not. Neutron diffraction analysis was used to determine cation site occupancy and revealed that mechanical activation resulted in a lower degree of cation site inversion compared to the nonactivated materials, which indicated that the powders were closer to thermodynamic equilibrium. This is the first study to characterize the effects of mechanical activation on crystallographic site occupancy in magnesium aluminate spinel using neutron diffraction.

Adapting Random Forests to Predict Obesity-Associated Gene Expression.

Random forests (RFs) are effective at predicting gene expression from genotype data. However, a comparison of RF regressors and classifiers, including feature selection and encoding, has been under-explored in the context of gene expression prediction. Specifically, we examine the role of ordinal or one-hot encoding and of data balancing via oversam-pling in the prediction of obesity-associated gene expression. Our work shows that RFs compete with PrediXcan in the prediction of obesity-associated gene expression in subcutaneous adipose tissue, a highly relevant tissue to obesity. Additionally, RFs generate predictions for obesity-associated genes where PrediXcan fails to do so.

In vivo brain endocannabinoid metabolism is related to hippocampus glutamate and structure - a multimodal imaging study with PET, 1H-MRS, and MRI.

Dysregulation of hippocampus glutamatergic neurotransmission and reductions in hippocampal volume have been associated with psychiatric disorders. The endocannabinoid system modulates glutamate neurotransmission and brain development, including hippocampal remodeling. In humans, elevated levels of anandamide and lower activity of its catabolic enzyme fatty acid amide hydrolase (FAAH) are associated with schizophrenia diagnosis and psychotic symptom severity, respectively (Neuropsychopharmacol, 29(11), 2108-2114; Biol. Psychiatry 88 (9), 727-735). Although preclinical studies provide strong evidence linking anandamide and FAAH to hippocampus neurotransmission and structure, these relationships remain poorly understood in humans. We recruited young adults with and without psychotic disorders and measured FAAH activity, hippocampal glutamate and glutamine (Glx), and hippocampal volume using [11C]CURB positron emission tomography (PET), proton magnetic resonance spectroscopy (1H-MRS) and T1-weighted structural MRI, respectively. We hypothesized that higher FAAH activity would be associated with greater hippocampus Glx and lower hippocampus volume, and that these effects would differ in patients with psychotic disorders relative to healthy control participants. After attrition and quality control, a total of 37 participants (62% male) completed [11C]CURB PET and 1H-MRS of the left hippocampus, and 45 (69% male) completed [11C]CURB PET and hippocampal volumetry. Higher FAAH activity was associated with greater concentration of hippocampal Glx (F1,36.36 = 9.17, p = 0.0045; Cohen's f = 0.30, medium effect size) and smaller hippocampal volume (F1,44.70 = 5.94, p = 0.019, Cohen's f = 0.26, medium effect size). These effects did not differ between psychosis and healthy control groups (no group interaction). This multimodal imaging study provides the first in vivo evidence linking hippocampal Glx and hippocampus volume with endocannabinoid metabolism in the human brain.

Improving Medication Regimen Recommendation for Parkinson's Disease Using Sensor Technology.

Parkinson's disease medication treatment planning is generally based on subjective data obtained through clinical, physician-patient interactions. The Personal KinetiGraph™ (PKG) and similar wearable sensors have shown promise in enabling objective, continuous remote health monitoring for Parkinson's patients. In this proof-of-concept study, we propose to use objective sensor data from the PKG and apply machine learning to cluster patients based on levodopa regimens and response. The resulting clusters are then used to enhance treatment planning by providing improved initial treatment estimates to supplement a physician's initial assessment. We apply k-means clustering to a dataset of within-subject Parkinson's medication changes-clinically assessed by the MDS-Unified Parkinson's Disease Rating Scale-III (MDS-UPDRS-III) and the PKG sensor for movement staging. A random forest classification model was then used to predict patients' cluster allocation based on their respective demographic information, MDS-UPDRS-III scores, and PKG time-series data. Clinically relevant clusters were partitioned by levodopa dose, medication administration frequency, and total levodopa equivalent daily dose-with the PKG providing similar symptomatic assessments to physician MDS-UPDRS-III scores. A random forest classifier trained on demographic information, MDS-UPDRS-III scores, and PKG time-series data was able to accurately classify subjects of the two most demographically similar clusters with an accuracy of 86.9%, an F1 score of 90.7%, and an AUC of 0.871. A model that relied solely on demographic information and PKG time-series data provided the next best performance with an accuracy of 83.8%, an F1 score of 88.5%, and an AUC of 0.831, hence further enabling fully remote assessments. These computational methods demonstrate the feasibility of using sensor-based data to cluster patients based on their medication responses with further potential to assist with medication recommendations.

Lower amygdala fatty acid amide hydrolase in violent offenders with antisocial personality disorder: an [11C]CURB positron emission tomography study.

Antisocial personality disorder (ASPD) imposes a high societal burden given the repetitive reactive aggression that affected individuals perpetrate. Since the brain endocannabinoid system (ECS) has been implicated in ASPD and aggressive behavior, we utilized [11C]CURB positron emission tomography to investigate fatty acid amide hydrolase (FAAH), an enzyme of the ECS that degrades anandamide, in 16 individuals with ASPD and 16 control participants. We hypothesized that FAAH density would be lower in the amygdala for several reasons. First, decreased FAAH expression is associated with increased cannabinoid receptor 1 stimulation, which may be responsible for amygdala hyper-reactivity in reactive aggression. Second, the amygdala is the seat of the neural circuit mediating reactive aggression. Third, other PET studies of externalizing populations show reduced brain FAAH density. Conversely, we hypothesized that FAAH expression would be greater in the orbitofrontal cortex. Consistent with our hypothesis, we found that amygdala FAAH density was lower in the amygdala of ASPD (p = 0.013). Cerebellar and striatal FAAH expression were inversely related with impulsivity (cerebellum: r = -0.60, p = 0.017; dorsal caudate: r = -0.58, p = 0.023; dorsal putamen: r = -0.55, p = 0.034), while cerebellar FAAH density was also negatively associated with assaultive aggression (r = -0.54, p = 0.035). ASPD presents high levels of disruptive behavior with few, if any, efficacious treatment options. Novel therapeutics that increase FAAH brain levels in a region-specific manner could hold promise for attenuating certain symptom clusters of ASPD, although our results require replication.

Fatty acid amide hydrolase is lower in young cannabis users.

We have recently shown that levels of fatty acid amide hydrolase (FAAH), the enzyme that metabolizes the endocannabinoid anandamide, are lower in the brains of adult cannabis users (CUs) (34 ± 11 years of age), tested during early abstinence. Here, we examine replication of the lower FAAH levels in a separate, younger cohort (23 ± 5 years of age). Eighteen healthy volunteers (HVs) and fourteen CUs underwent a positron emission tomography scan using the FAAH radioligand [11 C]CURB. Regional [11 C]CURB binding was calculated using an irreversible two-tissue compartment model with a metabolite-corrected arterial plasma input function. The FAAH C385A genetic polymorphism (rs324420) was included as a covariate. All CUs underwent a urine screen to confirm recent cannabis use and had serum cannabinoids measured. One CU screened negative for cannabinoids via serum and was removed from analysis. All HVs reported less than five lifetime cannabis exposures more than a month prior to study initiation. There was a significant effect of group (F1,26 = 4.31; P = .048) when two A/A (rs324420) HVs were removed from analysis to match the genotype of the CU group (n = 16 HVs, n = 13 CUs). Overall, [11 C]CURB λk3 was 12% lower in CU compared with HV. Exploratory correlations showed that lower brain [11 C]CURB binding was related to greater use of cannabis throughout the past year. We confirmed our previous report and extended these findings by detecting lower [11 C]CURB binding in a younger cohort with less cumulative cannabis exposure.

Endocannabinoid system in psychotic and mood disorders, a review of human studies.

Despite widespread evidence of endocannabinoid system involvement in the pathophysiology of psychiatric disorders, our understanding remains rudimentary. Here we review studies of the endocannabinoid system in humans with psychotic and mood disorders. Postmortem, peripheral, cerebrospinal fluid and in vivo imaging studies provide evidence for the involvement of the endocannabinoid system in psychotic and mood disorders. Psychotic disorders and major depressive disorder exhibit alterations of brain cannabinoid CB1 receptors and peripheral blood endocannabinoids. Further, these changes may be sensitive to treatment status, disease state, and symptom severity. Evidence from psychotic disorder extend to endocannabinoid metabolizing enzymes in the brain and periphery, whereas these lines of evidence remain poorly developed in mood disorders. A paucity of studies examining this system in bipolar disorder represents a notable gap in the literature. Despite a growing body of productive work in this field of research, there is a clear need for investigation beyond the CB1 receptor in order to more fully elucidate the role of the endocannabinoid system in psychotic and mood disorders.

Machine Learning's Application in Deep Brain Stimulation for Parkinson's Disease: A Review.

Deep brain stimulation (DBS) is a surgical treatment for advanced Parkinson's disease (PD) that has undergone technological evolution that parallels an expansion in clinical phenotyping, neurophysiology, and neuroimaging of the disease state. Machine learning (ML) has been successfully used in a wide range of healthcare problems, including DBS. As computational power increases and more data become available, the application of ML in DBS is expected to grow. We review the literature of ML in DBS and discuss future opportunities for such applications. Specifically, we perform a comprehensive review of the literature from PubMed, the Institute for Scientific Information's Web of Science, Cochrane Database of Systematic Reviews, and Institute of Electrical and Electronics Engineers' (IEEE) Xplore Digital Library for ML applications in DBS. These studies are broadly placed in the following categories: (1) DBS candidate selection; (2) programming optimization; (3) surgical targeting; and (4) insights into DBS mechanisms. For each category, we provide and contextualize the current body of research and discuss potential future directions for the application of ML in DBS.

Optimizing Individualized Treatment Planning for Parkinson's Disease Using Deep Reinforcement Learning.

More than one million people currently live with Parkinson's Disease (PD) in the U.S. alone. Medications, such as levodopa, can help manage PD symptoms. However, medication treatment planning is generally based on patient history and limited interaction between physicians and patients during office visits. This limits the extent of benefit that may be derived from the treatment as disease/patient characteristics are generally non-stationary. Wearable sensors that provide continuous monitoring of various symptoms, such as bradykinesia and dyskinesia, can enhance symptom management. However, using such data to overhaul the current static medication treatment planning approach and prescribe personalized medication timing and dosage that accounts for patient/care-giver/physician feedback/preferences remains an open question. We develop a model to prescribe timing and dosage of medications, given the motor fluctuation data collected using wearable sensors in real-time. We solve the resulting model using deep reinforcement learning (DRL). The prescribed policy determines the optimal treatment plan that minimizes patient's symptoms. Our results show that the model-prescribed policy outperforms the static a priori treatment plan in improving patients' symptoms, providing a proof-of-concept that DRL can augment medical decision making for treatment planning of chronic disease patients.

Evidence That Cannabis Exposure, Abuse, and Dependence Are Related to Glutamate Metabolism and Glial Function in the Anterior Cingulate Cortex: A 1H-Magnetic Resonance Spectroscopy Study.

There is evidence that long-term cannabis use is associated with alterations to glutamate neurotransmission and glial function. In this study, 26 long-term cannabis users (males=65.4%) and 47 non-cannabis using healthy controls (males=44.6%) underwent proton magnetic resonance spectroscopy (1H-MRS) of the anterior cingulate cortex (ACC) in order to characterize neurometabolite alterations in cannabis users and to examine associations between neurometabolites, cannabis exposure, and cannabis use behaviors. Myo-inositol, a marker of glial function, and glutamate metabolites did not differ between healthy controls and cannabis users or cannabis users who met criteria for DSM5 cannabis use disorder (n=17). Lower myo-inositol, a putative marker of glial function, was related to greater problematic drug use (F1,22 = 11.95, p=.002; Cohen's f=0.59, large effect; Drug Abuse Screening Test) and severity of cannabis dependence (F1,22 = 6.61, p=.17; Cohen's f=0.44, large effect). Further, past-year cannabis exposure exerted different effects on glutamate and glutamate+glutamine in males and females (glutamate: F1,21 = 6.31, p=.02; glutamate+glutamine: F1,21 = 7.20, p=.014), such that greater past-year cannabis exposure was related to higher concentrations of glutamate metabolites in male cannabis users (glutamate: F1,14 = 25.94, p=.00016; Cohen's f=1.32, large effect; glutamate+glutamine: F1,14 = 23.24, p=.00027, Cohen's f=1.24, large effect) but not in female cannabis users (glutamate: F1,6 = 1.37, p=0.78; glutamate+glutamine: F1,6 = 0.001, p=.97). The present results extend existing evidence of altered glial function and glutamate metabolism with cannabis use by providing evidence linking problematic drug use behaviors with glial function as measured with myo-inositol and recent chronic cannabis exposure to alterations in glutamate metabolism. This provides novel directions for the interrogation of the impact of cannabis use on brain neurochemistry.

Imaging Brain Fatty Acid Amide Hydrolase in Untreated Patients With Psychosis.

BACKGROUND: The brain's endocannabinoid system, the primary target of cannabis, has been implicated in psychosis. The endocannabinoid anandamide is elevated in cerebrospinal fluid of patients with schizophrenia. Fatty acid amide hydrolase (FAAH) controls brain anandamide levels; however, it is unknown if FAAH is altered in vivo in psychosis or related to positive psychotic symptoms. METHODS: Twenty-seven patients with schizophrenia spectrum disorders and 36 healthy control subjects completed high-resolution positron emission tomography scans with the novel FAAH radioligand [11C]CURB and structural magnetic resonance imaging. Data were analyzed using the validated irreversible 2-tissue compartment model with a metabolite-corrected arterial input function. RESULTS: FAAH did not differ significantly between patients with psychotic disorders and healthy control subjects (F1,62.85 = 0.48, p = .49). In contrast, lower FAAH predicted greater positive psychotic symptom severity, with the strongest effect observed for the positive symptom dimension, which includes suspiciousness, delusions, unusual thought content, and hallucinations (F1,26.69 = 12.42, p = .002; Cohen's f = 0.42, large effect). Shorter duration of illness (F1,26.95 = 13.78, p = .001; Cohen's f = 0.39, medium to large effect) and duration of untreated psychosis predicted lower FAAH (F1,26.95 = 6.03, p = .021, Cohen's f = 0.27, medium effect). These results were not explained by past cannabis exposure or current intake of antipsychotic medications. FAAH exhibited marked differences across brain regions (F7,112.62 = 175.85, p < 1 × 10-56; Cohen's f > 1). Overall, FAAH was higher in female subjects than in male subjects (F1,62.84 = 10.05, p = .002; Cohen's f = 0.37). CONCLUSIONS: This first study of brain FAAH in psychosis indicates that FAAH may represent a biomarker of disease state of potential utility for clinical studies targeting psychotic symptoms or as a novel target for interventions to treat psychotic symptoms.

Application of CRISPR genetic screens to investigate neurological diseases.

The adoption of CRISPR-Cas9 technology for functional genetic screens has been a transformative advance. Due to its modular nature, this technology can be customized to address a myriad of questions. To date, pooled, genome-scale studies have uncovered genes responsible for survival, proliferation, drug resistance, viral susceptibility, and many other functions. The technology has even been applied to the functional interrogation of the non-coding genome. However, applications of this technology to neurological diseases remain scarce. This shortfall motivated the assembly of a review that will hopefully help researchers moving in this direction find their footing. The emphasis here will be on design considerations and concepts underlying this methodology. We will highlight groundbreaking studies in the CRISPR-Cas9 functional genetics field and discuss strengths and limitations of this technology for neurological disease applications. Finally, we will provide practical guidance on navigating the many choices that need to be made when implementing a CRISPR-Cas9 functional genetic screen for the study of neurological diseases.

In Vivo Imaging of Translocator Protein in Long-term Cannabis Users.

Importance: Cannabis is the most commonly used illicit drug in the world. Cannabinoids have been shown to modulate immune responses; however, the association of cannabis with neuroimmune function has never been investigated in vivo in the human brain. Objective: To investigate neuroimmune activation or 18-kDa translocator protein (TSPO) levels in long-term cannabis users, and to evaluate the association of brain TSPO levels with behavioral measures and inflammatory blood biomarkers. Design, Setting, and Participants: This cross-sectional study based in Toronto, Ontario, recruited individuals from January 1, 2015, to October 30, 2018. Participants included long-term cannabis users (n = 24) and non-cannabis-using controls (n = 27). Cannabis users were included if they had a positive urine drug screen for only cannabis and if they used cannabis at least 4 times per week for the past 12 months and/or met the criteria for cannabis use disorder. All participants underwent a positron emission tomography scan with [18F]FEPPA, or fluorine F 18-labeled N-(2-(2-fluoroethoxy)benzyl)-N-(4-phenoxypyridin-3-yl)acetamide. Main Outcomes and Measures: Total distribution volume was quantified across regions of interest. Stress and anxiety as well as peripheral measures of inflammatory cytokines and C-reactive protein levels were also measured. Results: In total, 24 long-term cannabis users (mean [SD] age, 23.1 [3.8] years; 15 men [63%]) and 27 non-cannabis-using controls (mean [SD] age, 23.6 [4.2] years; 18 women [67%]) were included and completed all study procedures. Compared with the controls, cannabis users had higher [18F]FEPPA total distribution volume (main group effect: F1,48 = 6.5 [P = .01]; ROI effect: F1,200 = 28.4 [P < .001]; Cohen d = 0.6; 23.3% higher), with a more prominent implication for the cannabis use disorder subgroup (n = 15; main group effect: F1,39 = 8.5 [P = .006]; ROI effect: F1,164 = 19.3 [P < .001]; Cohen d = 0.8; 31.5% higher). Greater TSPO levels in the brain were associated with stress and anxiety and with higher circulating C-reactive protein levels in cannabis users. Conclusions and Relevance: The results of this study suggest that TSPO levels in cannabis users, particularly in those with cannabis use disorder, are higher than those in non-cannabis-using controls. The findings emphasize the need for more complementary preclinical systems for a better understanding of the role of cannabinoids and TSPO in neuroimmune signaling.

A systematic review of phytocannabinoid exposure on the endocannabinoid system: Implications for psychosis.

Cannabis, the most widely used illicit drug worldwide, produces psychoactive effects through its component cannabinoids, which act on the endocannabinoid system. Research on how cannabinoid exposure affects the endocannabinoid system is limited. Substantial evidence indicates cannabis use as a risk factor for psychosis, and the mechanism(s) by which this is occurring is/are currently unknown. Here, we conduct the first review of the effects of exogenous cannabinoids on the endocannabinoid system in humans with and without psychotic disorders. The most well established finding is the down-regulation of cannabinoid CB1 receptors (CB1R) after chronic and recent cannabis exposure, but it remains uncertain whether this effect is present in cannabis users with schizophrenia. We highlight where cannabis exposure affects the endocannabinoid system in a pattern that may mirror what is seen in psychosis, and how further research can push this field forward. In these times of changing cannabis legislation, research highlighting the biological effects of cannabinoids is greatly needed.

Hippocampal glutamate metabolites and glial activation in clinical high risk and first episode psychosis.

Alterations in glutamate neurotransmission have been implicated in the pathophysiology of schizophrenia, as well as in symptom severity and cognitive deficits. The hippocampus, in particular, is a site of key functional and structural abnormalities in schizophrenia. Yet few studies have investigated hippocampal glutamate in antipsychotic-naïve first episode psychosis patients or in individuals at clinical high risk (CHR) of developing psychosis. Using proton magnetic resonance spectroscopy (1H-MRS), we investigated glutamate metabolite levels in the left hippocampus of 25 CHR (19 antipsychotic-naïve), 16 patients with first-episode psychosis (13 antipsychotic-naïve) and 31 healthy volunteers. We also explored associations between hippocampal glutamate metabolites and glial activation, as indexed by [18F]FEPPA positron emission tomography (PET); symptom severity; and cognitive function. Groups differed significantly in glutamate plus glutamine (Glx) levels (F(2, 69) = 6.39, p = 0.003). Post-hoc analysis revealed that CHR had significantly lower Glx levels than both healthy volunteers (p = 0.003) and first-episode psychosis patients (p = 0.050). No associations were found between glutamate metabolites and glial activation. Our findings suggest that glutamate metabolites are altered in CHR.

Nigral Stress-Induced Dopamine Release in Clinical High Risk and Antipsychotic-Naïve Schizophrenia.

Background: Striatal dopamine (DA) synthesis capacity and release are elevated in schizophrenia (SCZ) and its putative prodrome, the clinical high risk (CHR) state. Striatal DA function results from the activity of midbrain DA neurons projecting mainly from the substantia nigra (SN). Elevated stress-induced DA release in SCZ and CHR was observed in the striatum; however, whether it is also elevated in the SN is unclear. The current study aims to determine whether nigral DA release in response to a validated stress task is altered in CHR and in antipsychotic-naïve SCZ. Further, we explore how DA release in the SN and striatum might be related. Methods: 24 CHR subjects, 9 antipsychotic-naïve SCZ and 25 healthy volunteers (HV) underwent 2 positron emission tomography (PET) scans using the DA D2/3 agonist radiotracer, [11C]-(+)-PHNO, which allows simultaneous investigations of DA in the SN and striatum. Psychosocial stress-induced DA release was estimated as the percentage differences in BPND (%[11C]-(+)-PHNO displacement) between stress and sensory-motor control sessions. Results: We observed a significant diagnostic group by session interaction, such that SCZ exhibited greater stress-induced [11C]-(+)-PHNO % displacement (25.90% ± 32.2%; mean ± SD), as compared to HVs (-10.94% ± 27.1%). Displacement in CHRs (-1.13% ± 32.2%) did not differ significantly from either HV or SCZ. Conclusion: Our findings suggest that elevated nigral DA responsiveness to stress is observed in antipsychotic-naïve SCZ.