A potential biomarker for treatment stratification in psychosis: evaluation of an [(18)F] FDOPA PET imaging approach

A Brief History and the Use of PET in the Diagnosis and Management of Schizophrenia: An Educational Review

This article explores the role of PET in the diagnosis and treatment of schizophrenia. PET imaging can reveal neurobiologic aspects such as cerebral blood flow, glucose metabolism, receptor function, and neuroinflammation in schizophrenia. It has supported the dopaminergic hypothesis and helped distinguish symptom types and severity. Diagnostic biomarkers from PET could differentiate schizophrenia from other disorders, while predictive biomarkers might allow earlier targeted treatments. Despite significant promises, the application of PET imaging in schizophrenia is still in its nascent stage, requiring well-designed multicenter studies with large sample sizes to fully realize its clinical potential.

Predictive utility of artificial intelligence on schizophrenia treatment outcomes: A systematic review and meta-analysis

Identifying optimal treatment approaches for schizophrenia is challenging due to varying symptomatology and treatment responses. Artificial intelligence (AI) shows promise in predicting outcomes, prompting this systematic review and meta-analysis to evaluate various AI models' predictive utilities in schizophrenia treatment. A systematic search was conducted, and the risk of bias was evaluated. The pooled sensitivity, specificity, and diagnostic odds ratio with 95 % confidence intervals between AI models and the reference standard for response to treatment were assessed. Diagnostic accuracy measures were calculated, and subgroup analysis was performed based on the input data of AI models. Out of the 21 included studies, AI models achieved a pooled sensitivity of 70 % and specificity of 76 % in predicting schizophrenia treatment response with substantial predictive capacity and a near-to-high level of test accuracy. Subgroup analysis revealed EEG-based models to have the highest sensitivity (89 %) and specificity (94 %), followed by imaging-based models (76 % and 80 %, respectively). However, significant heterogeneity was observed across studies in treatment response definitions, participant characteristics, and therapeutic interventions. Despite methodological variations and small sample sizes in some modalities, this study underscores AI's predictive utility in schizophrenia treatment, offering insights for tailored approaches, improving adherence, and reducing relapse risk.

EANM perspective on clinical PET and SPECT imaging in schizophrenia-spectrum disorders: a systematic review of longitudinal studies

Purpose

There is a need for biomarkers in psychiatry to improve diagnosis, prognosis and management, and with confirmed value in follow-up care. Radionuclide imaging, given its molecular imaging characteristics, is well-positioned for translation to the clinic. This systematic review lays the groundwork for integrating PET and SPECT imaging in the clinical management of schizophrenia-spectrum disorders.

Methods

Systematic search of PubMed, Embase, Web of Science and Cochrane library databases was conducted from the earliest date available until February 2024. The focus was on longitudinal studies evaluating PET or SPECT imaging in individuals with a schizophrenia-spectrum or another psychotic disorders. Quality assessment was done using the Newcastle-Ottawa Scale (NOS), NIH scale for before-after studies and Cochrane Risk of Bias tool version 2 (Cochrane RoB2). Studies were further categorised into three groups: preclinical and diagnosis, predicting disease course or personalising treatment.

Results

Fifty-six studies were included in the systematic review investigating in total 1329 patients over a median of 3 months. Over two-thirds used PET tracers, whereas the remaining studies employed SPECT tracers. The most frequently investigated system was dopaminergic transmission, followed by cerebral metabolism and blood flow. [18F]FDOPA demonstrated large effect size in predicting conversion of subjects at risk and treatment response. Additionally, treatment dosage could be optimised to reduce side effects using [123I]IBZM or [11C]raclopride.

Conclusion

Molecular imaging holds significant promise for real-life application in schizophrenia, with two particularly encouraging avenues being the prediction of conversion/response to antipsychotic medication and the improved management of antipsychotic dosage. Further longitudinal studies and clinical trials will be essential for validating both the clinical effectiveness and economic sustainability, as well as for exploring new applications.

Mesostriatal Dopaminergic Circuit Dysfunction in Schizophrenia: A Multimodal Neuromelanin-Sensitive Magnetic Resonance Imaging and [18F]-DOPA Positron Emission Tomography Study

BACKGROUND

Striatal hyperdopaminergia is implicated in the pathoetiology of schizophrenia, but how this relates to dopaminergic midbrain activity is unclear. Neuromelanin (NM)-sensitive magnetic resonance imaging provides a marker of long-term dopamine function. We examined whether midbrain NM-sensitive magnetic resonance imaging contrast-to-noise ratio (NM-CNR) was higher in people with schizophrenia than in healthy control (HC) participants and whether this correlated with dopamine synthesis capacity.

METHODS

One hundred fifty-four participants (schizophrenia group: n = 74, HC group: n = 80) underwent NM-sensitive magnetic resonance imaging of the substantia nigra and ventral tegmental area (SN-VTA). A subset of the schizophrenia group (n = 38) also received [18F]-DOPA positron emission tomography to measure dopamine synthesis capacity (Kicer) in the SN-VTA and striatum.

RESULTS

SN-VTA NM-CNR was significantly higher in patients with schizophrenia than in HC participants (effect size = 0.38, p = .019). This effect was greatest for voxels in the medial and ventral SN-VTA. In patients, SN-VTA Kicer positively correlated with SN-VTA NM-CNR (r = 0.44, p = .005) and striatal Kicer (r = 0.71, p < .001). Voxelwise analysis demonstrated that SN-VTA NM-CNR was positively associated with striatal Kicer (r = 0.53, p = .005) and that this relationship seemed strongest between the ventral SN-VTA and associative striatum in schizophrenia.

CONCLUSIONS

Our results suggest that NM levels are higher in patients with schizophrenia than in HC individuals, particularly in midbrain regions that project to parts of the striatum that receive innervation from the limbic and association cortices. The direct relationship between measures of NM and dopamine synthesis suggests that these aspects of schizophrenia pathophysiology are linked. Our findings highlight specific mesostriatal circuits as the loci of dopamine dysfunction in schizophrenia and thus as potential therapeutic targets.

Complex Diagnostic Challenges in Glioblastoma: The Role of 18F-FDOPA PET Imaging

Glioblastoma multiforme (GBM) remains one of the most aggressive and lethal forms of brain cancer, characterized by rapid proliferation and diffuse infiltration into the surrounding brain tissues. Despite advancements in therapeutic approaches, the prognosis for GBM patients is poor, with median survival times rarely exceeding 15 months post-diagnosis. An accurate diagnosis, treatment planning, and monitoring are crucial for improving patient outcomes. Core imaging modalities such as Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) are indispensable in the initial diagnosis and ongoing management of GBM. Histopathology remains the gold standard for definitive diagnoses, guiding treatment by providing molecular and genetic insights into the tumor. Advanced imaging modalities, particularly positron emission tomography (PET), play a pivotal role in the management of GBM. Among these, 3,4-dihydroxy-6-[18F]-fluoro-L-phenylalanine (18F-FDOPA) PET has emerged as a powerful tool due to its superior specificity and sensitivity in detecting GBM and monitoring treatment responses. This introduction provides a comprehensive overview of the multifaceted role of 18F-FDOPA PET in GBM, covering its diagnostic accuracy, potential as a biomarker, integration into clinical workflows, impact on patient outcomes, technological and methodological advancements, comparative effectiveness with other PET tracers, and its cost-effectiveness in clinical practice. Through these perspectives, we aim to underscore the significant contributions of 18F-FDOPA PET to the evolving landscape of GBM management and its potential to enhance both clinical and economic outcomes for patients afflicted with this formidable disease.

Evaluation of Neurodegenerative Disorders with Amyloid-β, Tau, and Dopaminergic PET Imaging: Interpretation Pitfalls

Antiamyloid therapies for Alzheimer disease recently entered clinical practice, making imaging biomarkers for Alzheimer disease even more relevant to guiding patient management. Amyloid and tau PET are valuable tools that can provide objective evidence of Alzheimer pathophysiology in living patients and will increasingly be used to complement 18F-FDG PET in the diagnostic evaluation of cognitive impairment and dementia. Parkinsonian syndromes, also common causes of dementia, can likewise be evaluated with a PET imaging biomarker,18F-DOPA, allowing in vivo assessment of the presynaptic dopaminergic neurons. Understanding the role of these PET biomarkers will help the nuclear medicine physician contribute to the appropriate diagnosis and management of patients with cognitive impairment and dementia. To successfully evaluate brain PET examinations for neurodegenerative diseases, knowledge of the necessary protocol details for obtaining a reliable imaging study, inherent limitations for each PET radiopharmaceutical, and pitfalls in image interpretation is critical. This review will focus on underlying concepts for interpreting PET examinations, important procedural details, and guidance for avoiding potential interpretive pitfalls for amyloid, tau, and dopaminergic PET examinations.

Neuromelanin-Sensitive MRI as Candidate Marker for Treatment Resistance in First-Episode Schizophrenia

OBJECTIVE

Markers for treatment resistance in schizophrenia are needed to reduce delays in effective treatment. Nigrostriatal hyperdopaminergic function plays a critical role in the pathology of schizophrenia, yet antipsychotic nonresponders do not show increased dopamine function. Neuromelanin-sensitive MRI (NM-MRI), which indirectly measures dopamine function in the substantia nigra, has potential as a noninvasive marker for nonresponders. Increased NM-MRI signal has been shown in psychosis, but has not yet been assessed in nonresponders. In this study, the authors investigated whether nonresponders show lower NM-MRI signal than responders.

METHODS

NM-MRI scans were acquired in 79 patients with first-episode psychosis and 20 matched healthy control subjects. Treatment response was assessed at a 6-month follow-up. An a priori voxel-wise analysis within the substantia nigra tested the relation between NM-MRI signal and treatment response in patients.

RESULTS

Fifteen patients were classified as nonresponders and 47 patients as responders. Seventeen patients were excluded, primarily because of medication nonadherence or change in diagnosis. Voxel-wise analysis revealed 297 significant voxels in the ventral tier of the substantia nigra that were negatively associated with treatment response. Nonresponders and healthy control subjects had significantly lower NM-MRI signal than responders. Receiver operating characteristic curve analysis showed that NM-MRI signal separated nonresponders with areas under the curve between 0.62 and 0.85. In addition, NM-MRI signal in patients did not change over 6 months.

CONCLUSIONS

These findings provide further evidence for dopaminergic differences between medication responders and nonresponders and support the potential of NM-MRI as a clinically applicable marker for treatment resistance in schizophrenia.

On the proportion of patients who experience a prodrome prior to psychosis onset: A systematic review and meta-analysis

BACKGROUND

Preventing or delaying the onset of psychosis requires identification of those at risk for developing psychosis. For predictive purposes, the prodrome - a constellation of symptoms which may occur before the onset of psychosis - has been increasingly recognized as having utility. However, it is unclear what proportion of patients experience a prodrome or how this varies based on the multiple definitions used.

METHODS

We conducted a systematic review and meta-analysis of studies of patients with psychosis with the objective of determining the proportion of patients who experienced a prodrome prior to psychosis onset. Inclusion criteria included a consistent prodrome definition and reporting the proportion of patients who experienced a prodrome. We excluded studies of only patients with a prodrome or solely substance-induced psychosis, qualitative studies without prevalence data, conference abstracts, and case reports/case series. We searched Ovid MEDLINE, Embase (Ovid), APA PsycInfo (Ovid), Web of Science Core Collection (Clarivate), Cochrane Database of Systematic Reviews, Cochrane Central Register of Controlled Trials, APA PsycBooks (Ovid), ProQuest Dissertation & Thesis, on March 3, 2021. Studies were assessed for quality using the Critical Appraisal Checklist for Prevalence Studies. Narrative synthesis and proportion meta-analysis were used to estimate prodrome prevalence. I2 and predictive interval were used to assess heterogeneity. Subgroup analyses were used to probe sources of heterogeneity. (PROSPERO ID: CRD42021239797).

RESULTS

Seventy-one articles were included, representing 13,774 patients. Studies varied significantly in terms of methodology and prodrome definition used. The random effects proportion meta-analysis estimate for prodrome prevalence was 78.3% (95% CI = 72.8-83.2); heterogeneity was high (I2 97.98% [95% CI = 97.71-98.22]); and the prediction interval was wide (95% PI = 0.411-0.936). There were no meaningful differences in prevalence between grouped prodrome definitions, and subgroup analyses failed to reveal a consistent source of heterogeneity.

CONCLUSIONS

This is the first meta-analysis on the prevalence of a prodrome prior to the onset of first episode psychosis. The majority of patients (78.3%) were found to have experienced a prodrome prior to psychosis onset. However, findings are highly heterogenous across study and no definitive source of heterogeneity was found despite extensive subgroup analyses. As most studies were retrospective in nature, recall bias likely affects these results. While the large majority of patients with psychosis experience a prodrome in some form, it is unclear if the remainder of patients experience no prodrome, or if ascertainment methods employed in the studies were not sensitive to their experiences. Given widespread investment in indicated prevention of psychosis through prospective identification and intervention during the prodrome, a resolution of this question as well as a consensus definition of the prodrome is much needed in order to effectively direct and organize services, and may be accomplished through novel, densely sampled and phenotyped prospective cohort studies that aim for representative sampling across multiple settings.

Generalizability and Out-of-Sample Predictive Ability of Associations Between Neuromelanin-Sensitive Magnetic Resonance Imaging and Psychosis in Antipsychotic-Free Individuals

Importance

The link between psychosis and dopaminergic dysfunction is established, but no generalizable biomarkers with clear potential for clinical adoption exist.

Objective

To replicate previous findings relating neuromelanin-sensitive magnetic resonance imaging (NM-MRI), a proxy measure of dopamine function, to psychosis severity in antipsychotic-free individuals in the psychosis spectrum and to evaluate the out-of-sample predictive ability of NM-MRI for psychosis severity.

Design, Setting, and Participants

This cross-sectional study recruited participants from 2019 to 2023 in the New York City area (main samples) and Mexico City area (external validation sample). The main samples consisted of 42 antipsychotic-free patients with schizophrenia, 53 antipsychotic-free individuals at clinical high risk for psychosis (CHR), and 52 matched healthy controls. An external validation sample consisted of 16 antipsychotic-naive patients with schizophrenia.

Main Outcomes and Measures

NM-MRI contrast within a subregion of the substantia nigra previously linked to psychosis severity (a priori psychosis region of interest [ROI]) and psychosis severity measured using the Positive and Negative Syndrome Scale (PANSS) in schizophrenia and the Structured Interview for Psychosis-Risk Syndromes (SIPS) in CHR. The cross-validated performance of linear support vector regression to predict psychosis severity across schizophrenia and CHR was assessed, and a final trained model was tested on the external validation sample.

Results

Of the 163 included participants, 76 (46.6%) were female, and the mean (SD) age was 29.2 (10.4) years. In the schizophrenia sample, higher PANSS positive total scores correlated with higher mean NM-MRI contrast in the psychosis ROI (t37 = 2.24, P = .03; partial r = 0.35; 95% CI, 0.05 to 0.55). In the CHR sample, no significant association was found between higher SIPS positive total score and NM-MRI contrast in the psychosis ROI (t48 = -0.55, P = .68; partial r = -0.08; 95% CI, -0.36 to 0.23). The 10-fold cross-validated prediction accuracy of psychosis severity was above chance in held-out test data (mean r = 0.305, P = .01; mean root-mean-square error [RMSE] = 1.001, P = .005). External validation prediction accuracy was also above chance (r = 0.422, P = .046; RMSE = 0.882, P = .047).

Conclusions and Relevance

This study provided a direct ROI-based replication of the in-sample association between NM-MRI contrast and psychosis severity in antipsychotic-free patients with schizophrenia. In turn, it failed to replicate such association in CHR individuals. Most critically, cross-validated machine-learning analyses provided a proof-of-concept demonstration that NM-MRI patterns can be used to predict psychosis severity in new data, suggesting potential for developing clinically useful tools.

The association between clinical, sociodemographic, familial, and environmental factors and treatment resistance in schizophrenia: A machine-learning-based approach

BACKGROUND

Prediction of treatment resistance in schizophrenia (TRS) would be helpful to reduce the duration of ineffective treatment and avoid delays in clozapine initiation. We applied machine learning to identify clinical, sociodemographic, familial, and environmental variables that are associated with TRS and could potentially predict TRS in the future.

STUDY DESIGN

Baseline and follow-up data on trait(-like) variables from the Genetic Risk and Outcome of Psychosis (GROUP) study were used. For the main analysis, we selected patients with non-affective psychotic disorders who met TRS (n = 200) or antipsychotic-responsive criteria (n = 423) throughout the study. For a sensitivity analysis, we only selected patients who met TRS (n = 76) or antipsychotic-responsive criteria (n = 123) at follow-up but not at baseline. Random forest models were trained to predict TRS in both datasets. SHapley Additive exPlanation values were used to examine the variables' contributions to the prediction.

STUDY RESULTS

Premorbid functioning, age at onset, and educational degree were most consistently associated with TRS across both analyses. Marital status, current household, intelligence quotient, number of moves, and family loading score for substance abuse also consistently contributed to the prediction of TRS in the main or sensitivity analysis. The diagnostic performance of our models was modest (area under the curve: 0.66-0.69).

CONCLUSIONS

We demonstrate that various clinical, sociodemographic, familial, and environmental variables are associated with TRS. Our models only showed modest performance in predicting TRS. Prospective large multi-centre studies are needed to validate our findings and investigate whether the model's performance can be improved by adding data from different modalities.

Evidence for functional improvement in reward anticipation in recent onset schizophrenia after one year of coordinated specialty care

BACKGROUND

Motivational impairment associated with deficits in processing the anticipation of future reward is hypothesized to be a cardinal feature of schizophrenia spectrum disorders (SZ). Evidence from short-term follow-up (6-week post-treatment) studies suggests that these deficits may improve or be reversed with treatment, although longer-term outcomes are unknown. Here we examined the one-year trajectory of functional activation in brain circuitry associated with reward anticipation in people with recent onset SZ who participated in coordinated specialty care (CSC) treatment, hypothesizing normalization of brain response mirroring previous short-term findings in first-episode individuals.

METHOD

Blood oxygen level-dependent (BOLD) response in the dorsal anterior cingulate cortex, anterior insula, and ventral striatum (VS) associated with reward anticipation during the Incentivized Control Engagement Task (ICE-T) was analyzed in a baseline sample of 49 healthy controls (HCs) and 52 demographically matched people with SZ, with follow-up data available for 35 HCs and 17 people with SZ.

RESULTS

In agreement with our hypothesis, significant time × diagnosis interactions were observed across all regions, in which reward anticipation-associated BOLD response increased in SZ to above baseline HC levels at follow-up. Increased VS activation was associated with decreased reality distortion symptoms over the follow-up period. Baseline reward anticipation-associated BOLD response in the right anterior insula was associated with improvement in reality distortion symptoms.

CONCLUSIONS

These findings suggest that functional deficits in reward anticipation may be reversed after one year of CSC in recent onset participants with SZ, and that this improvement is associated with reduced positive symptoms in the illness.

An automatic analysis framework for FDOPA PET neuroimaging

In this study we evaluate the performance of a fully automated analytical framework for FDOPA PET neuroimaging data, and its sensitivity to demographic and experimental variables and processing parameters. An instance of XNAT imaging platform was used to store the King's College London institutional brain FDOPA PET imaging archive, alongside individual demographics and clinical information. By re-engineering the historical Matlab-based scripts for FDOPA PET analysis, a fully automated analysis pipeline for imaging processing and data quantification was implemented in Python and integrated in XNAT. The final data repository includes 892 FDOPA PET scans organized from 23 different studies. We found good reproducibility of the data analysis by the automated pipeline (in the striatum for the Kicer: for the controls ICC = 0.71, for the psychotic patients ICC = 0.88). From the demographic and experimental variables assessed, gender was found to most influence striatal dopamine synthesis capacity (F = 10.7, p < 0.001), with women showing greater dopamine synthesis capacity than men. Our automated analysis pipeline represents a valid resourse for standardised and robust quantification of dopamine synthesis capacity using FDOPA PET data. Combining information from different neuroimaging studies has allowed us to test it comprehensively and to validate its replicability and reproducibility performances on a large sample size.

Reduced cortical cerebral blood flow in antipsychotic-free first-episode psychosis and relationship to treatment response

BACKGROUND

Altered cerebral blood flow (CBF) has been found in people at risk for psychosis, with first-episode psychosis (FEP) and with chronic schizophrenia (SCZ). Studies using arterial spin labelling (ASL) have shown reduction of cortical CBF and increased subcortical CBF in SCZ. Previous studies have investigated CBF using ASL in FEP, reporting increased CBF in striatum and reduced CBF in frontal cortex. However, as these people were taking antipsychotics, it is unclear whether these changes are related to the disorder or antipsychotic treatment and how they relate to treatment response.

METHODS

We examined CBF in FEP free from antipsychotic medication (N = 21), compared to healthy controls (N = 22). Both absolute and relative-to-global CBF were assessed. We also investigated the association between baseline CBF and treatment response in a partially nested follow-up study (N = 14).

RESULTS

There was significantly lower absolute CBF in frontal cortex (Cohen's d = 0.84, p = 0.009) and no differences in striatum or hippocampus. Whole brain voxel-wise analysis revealed widespread cortical reductions in absolute CBF in large cortical clusters that encompassed occipital, parietal and frontal cortices (Threshold-Free Cluster Enhancement (TFCE)-corrected <0.05). No differences were found in relative-to-global CBF in the selected region of interests and in voxel-wise analysis. Relative-to-global frontal CBF was correlated with percentage change in total Positive and Negative Syndrome Scale after antipsychotic treatment (r = 0.67, p = 0.008).

CONCLUSIONS

These results show lower cortical absolute perfusion in FEP prior to starting antipsychotic treatment and suggest relative-to-global frontal CBF as assessed with magnetic resonance imaging could potentially serve as a biomarker for antipsychotic response.

Dopamine Synthesis Capacity and GABA and Glutamate Levels Separate Antipsychotic-Naïve Patients With First-Episode Psychosis From Healthy Control Subjects in a Multimodal Prediction Model

Background

Disturbances in presynaptic dopamine activity and levels of GABA (gamma-aminobutyric acid) and glutamate plus glutamine collectively may have a role in the pathophysiology of psychosis, although separately they are poor diagnostic markers. We tested whether these neurotransmitters in combination improve the distinction of antipsychotic-naïve patients with first-episode psychosis from healthy control subjects.

Methods

We included 23 patients (mean age 22.3 years, 9 male) and 20 control subjects (mean age 22.4 years, 8 male). We determined dopamine metabolism in the nucleus accumbens and striatum from 18F-fluorodopa (18F-FDOPA) positron emission tomography. We measured GABA levels in the anterior cingulate cortex (ACC) and glutamate plus glutamine levels in the ACC and left thalamus with 3T proton magnetic resonance spectroscopy. We used binominal logistic regression for unimodal prediction when we modeled neurotransmitters individually and for multimodal prediction when we combined the 3 neurotransmitters. We selected the best combination based on Akaike information criterion.

Results

Individual neurotransmitters failed to predict group. Three triple neurotransmitter combinations significantly predicted group after Benjamini-Hochberg correction. The best model (Akaike information criterion 48.5) carried 93.5% of the cumulative model weight. It reached a classification accuracy of 83.7% (p = .003) and included dopamine synthesis capacity (Ki4p) in the nucleus accumbens (p = .664), GABA levels in the ACC (p = .019), glutamate plus glutamine levels in the thalamus (p = .678), and the interaction term Ki4p × GABA (p = .016).

Conclusions

Our multimodal approach proved superior classification accuracy, implying that the pathophysiology of patients represents a combination of neurotransmitter disturbances rather than aberrations in a single neurotransmitter. Particularly aberrant interrelations between Ki4p in the nucleus accumbens and GABA values in the ACC appeared to contribute diagnostic information.

Candidate biomarkers in psychiatric disorders: state of the field

The field of psychiatry is hampered by a lack of robust, reliable and valid biomarkers that can aid in objectively diagnosing patients and providing individualized treatment recommendations. Here we review and critically evaluate the evidence for the most promising biomarkers in the psychiatric neuroscience literature for autism spectrum disorder, schizophrenia, anxiety disorders and post-traumatic stress disorder, major depression and bipolar disorder, and substance use disorders. Candidate biomarkers reviewed include various neuroimaging, genetic, molecular and peripheral assays, for the purposes of determining susceptibility or presence of illness, and predicting treatment response or safety. This review highlights a critical gap in the biomarker validation process. An enormous societal investment over the past 50 years has identified numerous candidate biomarkers. However, to date, the overwhelming majority of these measures have not been proven sufficiently reliable, valid and useful to be adopted clinically. It is time to consider whether strategic investments might break this impasse, focusing on a limited number of promising candidates to advance through a process of definitive testing for a specific indication. Some promising candidates for definitive testing include the N170 signal, an event-related brain potential measured using electroencephalography, for subgroup identification within autism spectrum disorder; striatal resting-state functional magnetic resonance imaging (fMRI) measures, such as the striatal connectivity index (SCI) and the functional striatal abnormalities (FSA) index, for prediction of treatment response in schizophrenia; error-related negativity (ERN), an electrophysiological index, for prediction of first onset of generalized anxiety disorder, and resting-state and structural brain connectomic measures for prediction of treatment response in social anxiety disorder. Alternate forms of classification may be useful for conceptualizing and testing potential biomarkers. Collaborative efforts allowing the inclusion of biosystems beyond genetics and neuroimaging are needed, and online remote acquisition of selected measures in a naturalistic setting using mobile health tools may significantly advance the field. Setting specific benchmarks for well-defined target application, along with development of appropriate funding and partnership mechanisms, would also be crucial. Finally, it should never be forgotten that, for a biomarker to be actionable, it will need to be clinically predictive at the individual level and viable in clinical settings.

The relationship between striatal dopamine and anterior cingulate glutamate in first episode psychosis changes with antipsychotic treatment

The neuromodulator dopamine and excitatory neurotransmitter glutamate have both been implicated in the pathogenesis of psychosis, and dopamine antagonists remain the predominant treatment for psychotic disorders. To date no study has measured the effect of antipsychotics on both of these indices together, in the same population of people with psychosis. Striatal dopamine synthesis capacity (Kicer) and anterior cingulate glutamate were measured using 18F-DOPA positron emission tomography and proton magnetic resonance spectroscopy respectively, before and after at least 5 weeks' naturalistic antipsychotic treatment in people with first episode psychosis (n = 18) and matched healthy controls (n = 20). The relationship between both measures at baseline and follow-up, and the change in this relationship was analyzed using a mixed linear model. Neither anterior cingulate glutamate concentrations (p = 0.75) nor striatal Kicer (p = 0.79) showed significant change following antipsychotic treatment. The change in relationship between whole striatal Kicer and anterior cingulate glutamate, however, was statistically significant (p = 0.017). This was reflected in a significant difference in relationship between both measures for patients and controls at baseline (t = 2.1, p = 0.04), that was not present at follow-up (t = 0.06, p = 0.96). Although we did not find any effect of antipsychotic treatment on absolute measures of dopamine synthesis capacity and anterior cingulate glutamate, the relationship between anterior cingluate glutamate and striatal dopamine synthesis capacity did change, suggesting that antipsychotic treatment affects the relationship between glutamate and dopamine.

The synaptic hypothesis of schizophrenia version III: a master mechanism

The synaptic hypothesis of schizophrenia has been highly influential. However, new approaches mean there has been a step-change in the evidence available, and some tenets of earlier versions are not supported by recent findings. Here, we review normal synaptic development and evidence from structural and functional imaging and post-mortem studies that this is abnormal in people at risk and with schizophrenia. We then consider the mechanism that could underlie synaptic changes and update the hypothesis. Genome-wide association studies have identified a number of schizophrenia risk variants converging on pathways regulating synaptic elimination, formation and plasticity, including complement factors and microglial-mediated synaptic pruning. Induced pluripotent stem cell studies have demonstrated that patient-derived neurons show pre- and post-synaptic deficits, synaptic signalling alterations, and elevated, complement-dependent elimination of synaptic structures compared to control-derived lines. Preclinical data show that environmental risk factors linked to schizophrenia, such as stress and immune activation, can lead to synapse loss. Longitudinal MRI studies in patients, including in the prodrome, show divergent trajectories in grey matter volume and cortical thickness compared to controls, and PET imaging shows in vivo evidence for lower synaptic density in patients with schizophrenia. Based on this evidence, we propose version III of the synaptic hypothesis. This is a multi-hit model, whereby genetic and/or environmental risk factors render synapses vulnerable to excessive glia-mediated elimination triggered by stress during later neurodevelopment. We propose the loss of synapses disrupts pyramidal neuron function in the cortex to contribute to negative and cognitive symptoms and disinhibits projections to mesostriatal regions to contribute to dopamine overactivity and psychosis. It accounts for the typical onset of schizophrenia in adolescence/early adulthood, its major risk factors, and symptoms, and identifies potential synaptic, microglial and immune targets for treatment.

Machine learning methods to predict outcomes of pharmacological treatment in psychosis

In recent years, machine learning (ML) has been a promising approach in the research of treatment outcome prediction in psychosis. In this study, we reviewed ML studies using different neuroimaging, neurophysiological, genetic, and clinical features to predict antipsychotic treatment outcomes in patients at different stages of schizophrenia. Literature available on PubMed until March 2022 was reviewed. Overall, 28 studies were included, among them 23 using a single-modality approach and 5 combining data from multiple modalities. The majority of included studies considered structural and functional neuroimaging biomarkers as predictive features used in ML models. Specifically, functional magnetic resonance imaging (fMRI) features contributed to antipsychotic treatment response prediction of psychosis with good accuracies. Additionally, several studies found that ML models based on clinical features might present adequate predictive ability. Importantly, by examining the additive effects of combining features, the predictive value might be improved by applying multimodal ML approaches. However, most of the included studies presented several limitations, such as small sample sizes and a lack of replication tests. Moreover, considerable clinical and analytical heterogeneity among included studies posed a challenge in synthesizing findings and generating robust overall conclusions. Despite the complexity and heterogeneity of methodology, prognostic features, clinical presentation, and treatment approaches, studies included in this review suggest that ML tools may have the potential to predict treatment outcomes of psychosis accurately. Future studies need to focus on refining feature characterization, validating prediction models, and evaluate their translation in real-world clinical practice.

Cognitive Impairment Associated with Schizophrenia: From Pathophysiology to Treatment

Cognitive impairment is a core feature of schizophrenia and a major contributor to poor functional outcomes. Methods for assessment of cognitive dysfunction in schizophrenia are now well established. In addition, there has been increasing appreciation in recent years of the additional role of social cognitive impairment in driving functional outcomes and of the contributions of sensory-level dysfunction to higher-order impairments. At the neurochemical level, acute administration of N-methyl-d-aspartate receptor (NMDAR) antagonists reproduces the pattern of neurocognitive dysfunction associated with schizophrenia, encouraging the development of treatments targeted at both NMDAR and its interactome. At the local-circuit level, an auditory neurophysiological measure, mismatch negativity, has emerged both as a veridical index of NMDAR dysfunction and excitatory/inhibitory imbalance in schizophrenia and as a critical biomarker for early-stage translational drug development. Although no compounds have yet been approved for treatment of cognitive impairment associated with schizophrenia, several candidates are showing promise in early-phase testing.

Machine Learning and Non-Affective Psychosis: Identification, Differential Diagnosis, and Treatment

PURPOSE OF REVIEW

This review will cover the most relevant findings on the use of machine learning (ML) techniques in the field of non-affective psychosis, by summarizing the studies published in the last three years focusing on illness detection and treatment.

RECENT FINDINGS

Multiple ML tools that include mostly supervised approaches such as support vector machine, gradient boosting, and random forest showed promising results by applying these algorithms to various sources of data: socio-demographic information, EEG, language, digital content, blood biomarkers, neuroimaging, and electronic health records. However, the overall performance, in the binary classification case, varied from 0.49, which is to be considered very low (i.e., noise), to over 0.90. These results are fully justified by different factors, some of which may be attributable to the preprocessing of the data, the wide variety of the data, and the a-priori setting of hyperparameters. One of the main limitations of the field is the lack of stratification of results based on biological sex, given that psychosis presents differently in men and women; hence, the necessity to tailor identification tools and data analytic strategies. Timely identification and appropriate treatment are key factors in reducing the consequences of psychotic disorders. In recent years, the emergence of new analytical tools based on artificial intelligence such as supervised ML approaches showed promises as a potential breakthrough in this field. However, ML applications in everyday practice are still in its infancy.

Evaluation of Intraperitoneal [18F]-FDOPA Administration for Micro-PET Imaging in Mice and Assessment of the Effect of Subchronic Ketamine Dosing on Dopamine Synthesis Capacity

Positron emission tomography (PET) using the radiotracer [18F]-FDOPA provides a tool for studying brain dopamine synthesis capacity in animals and humans. We have previously standardised a micro-PET methodology in mice by intravenously administering [18F]-FDOPA via jugular vein cannulation and assessment of striatal dopamine synthesis capacity, indexed as the influx rate constant K i Mod of [18F]-FDOPA, using an extended graphical Patlak analysis with the cerebellum as a reference region. This enables a direct comparison between preclinical and clinical output values. However, chronic intravenous catheters are technically difficult to maintain for longitudinal studies. Hence, in this study, intraperitoneal administration of [18F]-FDOPA was evaluated as a less-invasive alternative that facilitates longitudinal imaging. Our experiments comprised the following assessments: (i) comparison of [18F]-FDOPA uptake between intravenous and intraperitoneal radiotracer administration and optimisation of the time window used for extended Patlak analysis, (ii) comparison of Ki Mod in a within-subject design of both administration routes, (iii) test-retest evaluation of Ki Mod in a within-subject design of intraperitoneal radiotracer administration, and (iv) validation of Ki Mod estimates by comparing the two administration routes in a mouse model of hyperdopaminergia induced by subchronic ketamine. Our results demonstrate that intraperitoneal [18F]-FDOPA administration resulted in good brain uptake, with no significant effect of administration route on Ki Mod estimates (intraperitoneal: 0.024 ± 0.0047 min-1, intravenous: 0.022 ± 0.0041 min-1, p = 0.42) and similar coefficient of variation (intraperitoneal: 19.6%; intravenous: 18.4%). The technique had a moderate test-retest validity (intraclass correlation coefficient (ICC) = 0.52, N = 6) and thus supports longitudinal studies. Following subchronic ketamine administration, elevated K i Mod as compared to control condition was measured with a large effect size for both methods (intraperitoneal: Cohen's d = 1.3; intravenous: Cohen's d = 0.9), providing further evidence that ketamine has lasting effects on the dopamine system, which could contribute to its therapeutic actions and/or abuse liability.

Integrating the Neurodevelopmental and Dopamine Hypotheses of Schizophrenia and the Role of Cortical Excitation-Inhibition Balance

The neurodevelopmental and dopamine hypotheses are leading theories of the pathoetiology of schizophrenia, but they were developed in isolation. However, since they were originally proposed, there have been considerable advances in our understanding of the normal neurodevelopmental refinement of synapses and cortical excitation-inhibition (E/I) balance, as well as preclinical findings on the interrelationship between cortical and subcortical systems and new in vivo imaging and induced pluripotent stem cell evidence for lower synaptic density markers in patients with schizophrenia. Genetic advances show that schizophrenia is associated with variants linked to genes affecting GABA (gamma-aminobutyric acid) and glutamatergic signaling as well as neurodevelopmental processes. Moreover, in vivo studies on the effects of stress, particularly during later development, show that it leads to synaptic elimination. We review these lines of evidence as well as in vivo evidence for altered cortical E/I balance and dopaminergic dysfunction in schizophrenia. We discuss mechanisms through which frontal cortex circuitry may regulate striatal dopamine and consider how frontal E/I imbalance may cause dopaminergic dysregulation to result in psychotic symptoms. This integrated neurodevelopmental and dopamine hypothesis suggests that overpruning of synapses, potentially including glutamatergic inputs onto frontal cortical interneurons, disrupts the E/I balance and thus underlies cognitive and negative symptoms. It could also lead to disinhibition of excitatory projections from the frontal cortex and possibly other regions that regulate mesostriatal dopamine neurons, resulting in dopamine dysregulation and psychotic symptoms. Together, this explains a number of aspects of the epidemiology and clinical presentation of schizophrenia and identifies new targets for treatment and prevention.

A polygenic score indexing a DRD2-related co-expression network is associated with striatal dopamine function

The D2 dopamine receptor (D2R) is the primary site of the therapeutic action of antipsychotics and is involved in essential brain functions relevant to schizophrenia, such as attention, memory, motivation, and emotion processing. Moreover, the gene coding for D2R (DRD2) has been associated with schizophrenia at a genome-wide level. Recent studies have shown that a polygenic co-expression index (PCI) predicting the brain-specific expression of a network of genes co-expressed with DRD2 was associated with response to antipsychotics, brain function during working memory in patients with schizophrenia, and with the modulation of prefrontal cortex activity after pharmacological stimulation of D2 receptors. We aimed to investigate the relationship between the DRD2 gene network and in vivo striatal dopaminergic function, which is a phenotype robustly associated with psychosis and schizophrenia. To this aim, a sample of 92 healthy subjects underwent 18F-DOPA PET and was genotyped for genetic variations indexing the co-expression of the DRD2-related genetic network in order to calculate the PCI for each subject. The PCI was significantly associated with whole striatal dopamine synthesis capacity (p = 0.038). Exploratory analyses on the striatal subdivisions revealed a numerically larger effect size of the PCI on dopamine function for the associative striatum, although this was not significantly different than effects in other sub-divisions. These results are in line with a possible relationship between the DRD2-related co-expression network and schizophrenia and extend it by identifying a potential mechanism involving the regulation of dopamine synthesis. Future studies are needed to clarify the molecular mechanisms implicated in this relationship.

Striatal dopamine synthesis capacity and its association with negative symptoms upon resolution of positive symptoms in first-episode schizophrenia and delusional disorder

RATIONALE

How striatal dopamine synthesis capacity (DSC) contributes to the pathogenesis of negative symptoms in first-episode schizophrenia (SZ) and delusional disorder (DD) has seldom been explored. As negative symptoms during active psychotic episodes can be complicated by secondary influences, such as positive symptoms, longitudinal investigations may help to clarify the relationship between striatal DSC and negative symptoms and differentiate between primary and secondary negative symptoms.

OBJECTIVE

A longitudinal study was conducted to examine whether baseline striatal DSC would be related to negative symptoms at 3 months in first-episode SZ and DD patients.

METHODS

Twenty-three first-episode age- and gender-matched patients (11 DD and 12 SZ) were consecutively recruited through an early intervention service for psychosis in Hong Kong. Among them, 19 (82.6%) patients (9 DD and 10 SZ) were followed up at 3 months. All patients received an ¹⁸F-DOPA PET/MR scan at baseline.

RESULTS

Baseline striatal DSC (K^occ;30-60) was inversely associated with negative symptoms at 3 months in first-episode SZ patients (r_s =  - 0.80, p = 0.010). This association remained in SZ patients even when controlling for baseline negative, positive, and depressive symptoms, as well as cumulative antipsychotic dosage (β =  - 0.69, p = 0.012). Such associations were not observed in first-episode DD patients. Meanwhile, the severity of negative symptoms at 3 months was associated with more positive symptoms in DD patients (r_s = 0.74, p = 0.010) and not in SZ patients.

CONCLUSIONS

These findings highlight the role of striatal DSC in negative symptoms upon resolution of active psychotic episodes among first-episode SZ patients. Baseline striatal dopamine activity may inform future symptom expression with important treatment implications.

A Guideline and Checklist for Initiating and Managing Clozapine Treatment in Patients with Treatment-Resistant Schizophrenia

Treatment-resistant schizophrenia (TRS) will affect about one in three patients with schizophrenia. Clozapine is the only treatment approved for TRS, and patients should be treated as soon as possible to improve their chances of achieving remission. Despite its effectiveness, concern over side effects, monitoring requirements, and inexperience with prescribing often result in long delays that can expose patients to unnecessary risks and compromise their chances of achieving favorable long-term outcomes. We critically reviewed the literature on clozapine use in TRS, focusing on guidelines, systematic reviews, and algorithms to identify strategies for improving clozapine safety and tolerability. Based on this, we have provided an overview of strategies to support early initiation of clozapine in patients with TRS based on the latest evidence and our clinical experience, and have summarized the key elements in a practical, evidence-based checklist for identifying and managing patients with TRS, with the aim of increasing confidence in prescribing and monitoring clozapine therapy.

Novel PET Imaging of Inflammatory Targets and Cells for the Diagnosis and Monitoring of Giant Cell Arteritis and Polymyalgia Rheumatica

Giant cell arteritis (GCA) and polymyalgia rheumatica (PMR) are two interrelated inflammatory diseases affecting patients above 50 years of age. Patients with GCA suffer from granulomatous inflammation of medium- to large-sized arteries. This inflammation can lead to severe ischemic complications (e.g., irreversible vision loss and stroke) and aneurysm-related complications (such as aortic dissection). On the other hand, patients suffering from PMR present with proximal stiffness and pain due to inflammation of the shoulder and pelvic girdles. PMR is observed in 40-60% of patients with GCA, while up to 21% of patients suffering from PMR are also affected by GCA. Due to the risk of ischemic complications, GCA has to be promptly treated upon clinical suspicion. The treatment of both GCA and PMR still heavily relies on glucocorticoids (GCs), although novel targeted therapies are emerging. Imaging has a central position in the diagnosis of GCA and PMR. While [18F]fluorodeoxyglucose (FDG)-positron emission tomography (PET) has proven to be a valuable tool for diagnosis of GCA and PMR, it possesses major drawbacks such as unspecific uptake in cells with high glucose metabolism, high background activity in several non-target organs and a decrease of diagnostic accuracy already after a short course of GC treatment. In recent years, our understanding of the immunopathogenesis of GCA and, to some extent, PMR has advanced. In this review, we summarize the current knowledge on the cellular heterogeneity in the immunopathology of GCA/PMR and discuss how recent advances in specific tissue infiltrating leukocyte and stromal cell profiles may be exploited as a source of novel targets for imaging. Finally, we discuss prospective novel PET radiotracers that may be useful for the diagnosis and treatment monitoring in GCA and PMR.

Effects of average reward rate on vigor as a function of individual variation in striatal dopamine

RATIONALE

We constantly need to decide not only which actions to perform, but also how vigorously to perform them. In agreement with an earlier theoretical model, it has been shown that a significant portion of the variance in our action vigor can be explained by the average rate of rewards received for that action. Moreover, this invigorating effect of average reward rate was shown to vary with within-subject changes in dopamine, both in human individuals and experimental rodents.

OBJECTIVES

Here, we assessed whether individual differences in the effect of average reward rate on vigor are related to individual variation in a stable measure of striatal dopamine function in healthy, unmedicated participants.

METHODS

Forty-four participants performed a discrimination task to test the effect of average reward rate on response times to index vigor and completed an [¹⁸F]-DOPA PET scan to index striatal dopamine synthesis capacity.

RESULTS

We did not find an interaction between dopamine synthesis capacity and average reward rate across the entire group. However, a post hoc analysis revealed that participants with higher striatal dopamine synthesis capacity, particularly in the nucleus accumbens, exhibited a stronger invigorating effect of average reward rate among the 30 slowest participants.

CONCLUSIONS

Our findings provide converging evidence for a role of striatal dopamine in average reward rate signaling, thereby extending the current literature on the mechanistic link between average reward rate, vigor, and dopamine.

Markers of muscarinic deficit for individualized treatment in schizophrenia

Recent clinical studies have shown that agonists at muscarinic acetylcholine receptors effectively reduce schizophrenia symptoms. It is thus conceivable that, for the first time, a second substance class of procholinergic antipsychotics could become established alongside the usual antidopaminergic antipsychotics. In addition, various basic science studies suggest that there may be a subgroup of schizophrenia in which hypofunction of muscarinic acetylcholine receptors is of etiological importance. This could represent a major opportunity for individualized treatment of schizophrenia if markers can be identified that predict response to procholinergic vs. antidopaminergic interventions. In this perspective, non-response to antidopaminergic antipsychotics, specific symptom patterns like visual hallucinations and strong disorganization, the presence of antimuscarinic antibodies, ERP markers such as mismatch negativity, and radiotracers are presented as possible in vivo markers of muscarinic deficit and thus potentially of response to procholinergic therapeutics. Finally, open questions and further research steps are outlined.

The utility of PET imaging in the diagnosis and management of psychosis: a brief review

Purpose

Advances in the pathophysiological characterization of psychosis has led to a newfound role of biomarkers in diagnostic and prognostic contexts. Further, advances in the accuracy and sensitivity of nuclear medicine imaging techniques, and specifically positron emission tomography (PET), have improved the ability to diagnose and manage individuals experiencing first-episode psychosis or those at greater risk for developing psychosis.

Methods

Literature searches were performed in PubMed, Google Scholar, and Web of Science to identify papers related to the use of PET imaging in the diagnosis or management of psychosis. Search terms used included “positron emission tomography”, “PET imaging”, “psychosis”, “disorders of psychosis”, “schizophrenia”, “biomarkers”, “diagnostic biomarkers”, “prognostic biomarker”, “monitoring biomarker”, “outcome biomarker”, and “predictive biomarker.”

Results

Studies included fell into three categories: those examining microglia, those studying dopamine synthesis capacity, and those examining acetylcholine receptor activity. Microglial imaging has been shown to be ineffective in all patients with psychosis, but some believe it shows promise in a subset of patients with psychosis, although no defining characteristics of said subset have been postulated. Studies of dopamine synthesis capacity suggest that presynaptic dopamine is reliably elevated in patients with psychosis, but levels of dopamine active transporter are not. Further, positron emission tomography (PET) with [18F]fluoro-l-dihydroxyphenylalanine ([18F]FDOPA)-PET has been recently used successfully as a predictive biomarker of dopaminergic treatment response, although more work is needed to validate such findings. Finally, existing studies have also documented lower levels of binding to the α7 nicotinic cholinergic receptor (α7-nAChR) via [18F]-ASEM PET in patients with psychosis, however there is a dearth of prospective, randomized studies evaluating the efficacy of [18F]-ASEM as a diagnostic or monitoring biomarker of any kind.

Conclusion

Molecular imaging has become a useful tool in the diagnosis and management of psychosis. Further work must be done to improve the comparative prognostic value and diagnostic accuracy of different radiotracers.

Automated Data Quality Control in FDOPA brain PET Imaging using Deep Learning

INTRODUCTION

With biomedical imaging research increasingly using large datasets, it becomes critical to find operator-free methods to quality control the data collected and the associated analysis. Attempts to use artificial intelligence (AI) to perform automated quality control (QC) for both single-site and multi-site datasets have been explored in some neuroimaging techniques (e.g. EEG or MRI), although these methods struggle to find replication in other domains. The aim of this study is to test the feasibility of an automated QC pipeline for brain [¹⁸F]-FDOPA PET imaging as a biomarker for the dopamine system.

METHODS

Two different Convolutional Neural Networks (CNNs) were used and combined to assess spatial misalignment to a standard template and the signal-to-noise ratio (SNR) relative to 200 static [¹⁸F]-FDOPA PET images that had been manually quality controlled from three different PET/CT scanners. The scans were combined with an additional 400 scans, in which misalignment (200 scans) and low SNR (200 scans) were simulated. A cross-validation was performed, where 80% of the data were used for training and 20% for validation. Two additional datasets of [¹⁸F]-FDOPA PET images (50 and 100 scans respectively with at least 80% of good quality images) were used for out-of-sample validation.

RESULTS

The CNN performance was excellent in the training dataset (accuracy for motion: 0.86 ± 0.01, accuracy for SNR: 0.69 ± 0.01), leading to 100% accurate QC classification when applied to the two out-of-sample datasets. Data dimensionality reduction affected the generalizability of the CNNs, especially when the classifiers were applied to the out-of-sample data from 3D to 1D datasets.

CONCLUSIONS

This feasibility study shows that it is possible to perform automatic QC of [¹⁸F]-FDOPA PET imaging with CNNs. The approach has the potential to be extended to other PET tracers in both brain and non-brain applications, but it is dependent on the availability of large datasets necessary for the algorithm training.

HPLC-free and cassette-based nucleophilic production of [18F]FDOPA for clinical use

Radiotracer 3,4-dihydroxy-6-[¹⁸F]fluoro-L-phenylalanine (L-6-[¹⁸F]fluorodopa or [¹⁸F]FDOPA) is widely used for PET imaging of dopamine metabolism in several diseases including Parkinson's Disease, brain tumor, neuroendocrine tumors, and focal hyperinsulinism of infancy. In 2019, [¹⁸F]FDOPA was approved by US FDA for detection of dopaminergic nerve terminals in the striatum of adult patients with suspected Parkinsonian Syndromes. A convenient and reliable method is desired for fully automated production of [¹⁸F]FDOPA under cGMP compliance to meet the increasing clinical need. In this study, we reported a cassette-based automated production of [¹⁸F]FDOPA using a GE Fastlab 2 module and the quality control (QC) under fully cGMP compliant environment. Briefly, automated radiosynthesis of [¹⁸F]FDOPA was processed via nucleophilic radio-fluorination using FDOPA Fastlab cassette and solid phase extraction (SPE) purification. The QC tests of [¹⁸F]FDOPA, including appearance, pH, half-life, radiochemical purity and identity, enantiomeric purity, chemical impurities, molecular activity, radioactive concentration, filter integrity, endotoxin, and sterility, were conducted at the end of synthesis (EOS) and 8 h after EOS during the validation runs. Three consecutive productions of [¹⁸F]FDOPA were reliably achieved with desired radiochemical yield and high radiochemical/enantiomeric purities and molar activity. The uncorrected radiochemical yields of [¹⁸F]FDOPA were 9.3-9.8% with a total synthesis time of ~140 min. Both radiochemical and enantiomeric purities of [¹⁸F]FDOPA were >99.9% and the molar activities were 2.1-3.9 Ci/μmole at EOS. The full QC results at EOS and 8 h after EOS showed that the produced [¹⁸F]FDOPA met all release criteria for clinical use within 8 hours of expiration time. Three consecutive validation runs and QC results demonstrated the efficacy of cassette-based production of [¹⁸F]FDOPA for routine clinical use.

The Emerging Role of SPECT Functional Neuroimaging in Schizophrenia and Depression

Over the last three decades, the brain's functional and structural imaging has become more prevalent in psychiatric research and clinical application. A substantial amount of psychiatric research is based on neuroimaging studies that aim to illuminate neural mechanisms underlying psychiatric disorders. Single-photon emission computed tomography (SPECT) is one of those developing brain imaging techniques among various neuroimaging technologies. Compared to PET, SPECT imaging is easy, less expensive, and practical for radioligand use. Current technologies increased the spatial accuracy of SPECT findings by combining the functional SPECT images with CT images. The radioligands bind to receptors such as 5-hydroxytryptamine 2A, and dopamine transporters can help us comprehend neural mechanisms of psychiatric disorders based on neurochemicals. This mini-review focuses on the SPECT-based neuroimaging approach to psychiatric disorders such as schizophrenia and major depressive disorder (MDD). Research-based SPECT findings of psychiatric disorders indicate that there are notable changes in biochemical components in certain disorders. Even though many studies support that SPECT can be used in psychiatric clinical practice, we still only use subjective diagnostic criteria such as the Diagnostic Statistical Manual of Mental Disorders (DSM-5). Glimpsing into the brain's biochemical world via SPECT in psychiatric disorders provides more information about the pathophysiology and future implication of neuroimaging techniques.

Molecular imaging of schizophrenia: Neurochemical findings in a heterogeneous and evolving disorder

The past four decades have seen enormous efforts placed on a search for molecular markers of schizophrenia using positron emission tomography (PET) and single photon emission computed tomography (SPECT). In this narrative review, we cast a broad net to define and summarize what researchers have learned about schizophrenia from molecular imaging studies. Some PET studies of brain energy metabolism with the glucose analogue FDGhave have shown a hypofrontality defect in patients with schizophrenia, but more generally indicate a loss of metabolic coherence between different brain regions. An early finding of significantly increased striatal trapping of the dopamine synthesis tracer FDOPA has survived a meta-analysis of many replications, but the increase is not pathognomonic of the disorder, since one half of patients have entirely normal dopamine synthesis capacity. Similarly, competition SPECT studies show greater basal and amphetamine-evoked dopamine occupancy at post-synaptic dopamine D_2/3 receptors in patients with schizophrenia, but the difference is likewise not pathognomonic. We thus propose that molecular imaging studies of brain dopamine indicate neurochemical heterogeneity within the diagnostic entity of schizophrenia. Occupancy studies have established the relevant target engagement by antipsychotic medications at dopamine D_2/3 receptors in living brain. There is evidence for elevated frontal cortical dopamine D₁ receptors, especially in relation to cognitive deficits in schizophrenia. There is a general lack of consistent findings of abnormalities in serotonin markers, but some evidence for decreased levels of nicotinic receptors in patients. There are sparse and somewhat inconsistent findings of reduced binding of muscarinic, glutamate, and opioid receptors ligands, inconsistent findings of microglial activation, and very recently, evidence of globally reduced levels of synaptic proteins in brain of patients. One study reports a decline in histone acetylase binding that is confined to the dorsolateral prefrontal cortex. In most contexts, the phase of the disease and effects of past or present medication can obscure or confound PET and SPECT findings in schizophrenia.