Extrastriatal dopamine D2/3 receptors and cortical grey matter volumes in antipsychotic-naïve schizophrenia patients before and after initial antipsychotic treatment

A machine-learning framework for robust and reliable prediction of short- and long-term treatment response in initially antipsychotic-naïve schizophrenia patients based on multimodal neuropsychiatric data

The reproducibility of machine-learning analyses in computational psychiatry is a growing concern. In a multimodal neuropsychiatric dataset of antipsychotic-naïve, first-episode schizophrenia patients, we discuss a workflow aimed at reducing bias and overfitting by invoking simulated data in the design process and analysis in two independent machine-learning approaches, one based on a single algorithm and the other incorporating an ensemble of algorithms. We aimed to (1) classify patients from controls to establish the framework, (2) predict short- and long-term treatment response, and (3) validate the methodological framework. We included 138 antipsychotic-naïve, first-episode schizophrenia patients with data on psychopathology, cognition, electrophysiology, and structural magnetic resonance imaging (MRI). Perinatal data and long-term outcome measures were obtained from Danish registers. Short-term treatment response was defined as change in Positive And Negative Syndrome Score (PANSS) after the initial antipsychotic treatment period. Baseline diagnostic classification algorithms also included data from 151 matched controls. Both approaches significantly classified patients from healthy controls with a balanced accuracy of 63.8% and 64.2%, respectively. Post-hoc analyses showed that the classification primarily was driven by the cognitive data. Neither approach predicted short- nor long-term treatment response. Validation of the framework showed that choice of algorithm and parameter settings in the real data was successfully guided by results from the simulated data. In conclusion, this novel approach holds promise as an important step to minimize bias and obtain reliable results with modest sample sizes when independent replication samples are not available.

In vivo absolute quantification of striatal and extrastriatal D2/3 receptors with [123I]epidepride SPECT

Background

[¹²³I]epidepride is a high-affinity radiotracer used in single-photon emission computed tomography (SPECT) imaging of the D_2/3 receptors. It binds with high affinity to striatal and extrastriatal receptors. Nevertheless, its slow kinetics in the striatum impedes quantification in this region. Thus, an approach that would allow a simultaneous quantification of both striatal and extrastriatal D_2/3 receptors would be of interest for preclinical and clinical SPECT neuroimaging. We describe a partial saturation protocol that allows us to produce an in vivo Scatchard plot and thus estimate B_avail and appK_d separately in both striatal and extrastriatal regions, through a single dynamic SPECT session. To validate this approach, a multi-injection protocol is used for the full kinetic modeling of [¹²³I]epidepride using a two-tissue compartment, 5-parameter model (2T-5k).

Methods

Eighteen male rats were used. Binding parameters were estimated using the multi-injection protocol. Various simulations were performed to estimate the optimal conditions for the partial saturation protocol, which was applied at the region and voxel level. The results of the partial saturation study were compared to those obtained with the 2T-5k model. To illustrate the interest of the partial saturation approach, we performed a preliminary study of the effect of a chronic, subcutaneous administration of haloperidol (1 mg/kg/day), a D₂ receptor antagonist, on the B_avail of [¹²³I]epidepride in the rat striatum.

Results

A series of simulations demonstrated that a mass of 3 ug/kg of unlabeled epidepride allows the formation of an in vivo Scatchard plot. The partial saturation study led to robust estimations of B_avail in all brain regions that highly correlated (r = 0.99) with the corresponding values from the multi-injection study. A chronic haloperidol treatment resulted in a 17.9% increase in the B_avail values in the left Caudate Putamen nucleus (CP) (p = 0.07) and a 13.8% increase in the right CP (p = 0.12).

Conclusion

A partial saturation method allowed the robust quantification of D_2/3 receptors in striatal and extrastriatal D_2/3 receptors with a single-scan approach. This approach may be applied in the mapping of the D_2/3 receptor in translational biological studies and potentially, in clinical SPECT imaging.

Cortical structures and their clinical correlates in antipsychotic-naïve schizophrenia patients before and after 6 weeks of dopamine D2/3 receptor antagonist treatment

BACKGROUND

Schizophrenia has been associated with changes in both cortical thickness and surface area, but antipsychotic exposure, illness progression and substance use may confound observations. In antipsychotic-naïve schizophrenia patients, we investigated cortical thickness and surface area as well as mean curvature before and after monotherapy with amisulpride, a relatively selective dopamine D2/3 receptor antagonist.

METHODS

Fifty-six patients and 59 matched healthy controls (HCs) underwent T1-weighted 3T magnetic resonance imaging. Forty-one patients and 51 HCs were re-scanned. FreeSurfer-processed baseline, follow-up values and symmetrized percentage changes (SPC) in cortical structures were analysed using univariate analysis of variance. Clinical measures comprised psychopathology ratings, assessment of functioning and tests of premorbid and current intelligence. We applied false discovery rate correction to account for multiple comparisons.

RESULTS

At baseline, groups did not differ in cortical thickness or surface area; however, curvature in the left hemisphere was higher in patients (p = 0.015). In both patients and HCs, higher curvature was associated with lower premorbid (p = 0.009) and current intelligence (p 0.43). Cortical thickness SPC was negatively associated with symptom improvement (p = 0.002).

CONCLUSIONS

Schizophrenia appears associated with subtle, yet clinically relevant aberrations in cortical structures. Mean curvature holds promise as a sensitive supplement to cortical thickness and surface area to detect complex structural brain abnormalities.

Short-term mechanisms influencing volumetric brain dynamics

With the use of magnetic resonance imaging (MRI) and brain analysis tools, it has become possible to measure brain volume changes up to around 0.5%. Besides long-term brain changes caused by atrophy in aging or neurodegenerative disease, short-term mechanisms that influence brain volume may exist. When we focus on short-term changes of the brain, changes may be either physiological or pathological. As such determining the cause of volumetric dynamics of the brain is essential. Additionally for an accurate interpretation of longitudinal brain volume measures by means of neurodegeneration, knowledge about the short-term changes is needed. Therefore, in this review, we discuss the possible mechanisms influencing brain volumes on a short-term basis and set-out a framework of MRI techniques to be used for volumetric changes as well as the used analysis tools. 3D T₁-weighted images are the images of choice when it comes to MRI of brain volume. These images are excellent to determine brain volume and can be used together with an analysis tool to determine the degree of volume change. Mechanisms that decrease global brain volume are: fluid restriction, evening MRI measurements, corticosteroids, antipsychotics and short-term effects of pathological processes like Alzheimer's disease, hypertension and Diabetes mellitus type II. Mechanisms increasing the brain volume include fluid intake, morning MRI measurements, surgical revascularization and probably medications like anti-inflammatory drugs and anti-hypertensive medication. Exercise was found to have no effect on brain volume on a short-term basis, which may imply that dehydration caused by exercise differs from dehydration by fluid restriction. In the upcoming years, attention should be directed towards studies investigating physiological short-term changes within the light of long-term pathological changes. Ultimately this may lead to a better understanding of the physiological short-term effects of pathological processes and may aid in early detection of these diseases.

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Fluid-restriction, evening MRI, corticosteroids, & antipsychotics decrease volume

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Fluid-intake, morning MRI, surgical revascularization & medications increase volume

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Short-term changes within the light of long-term pathological changes should be investigated

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Short-term changes may introduce bias in longitudinal data