Deficits in prefrontal cortical and extra-striatal dopamine release in schizophrenia: a PET fMRI study

Abstract

Importance

Multiple lines of evidence suggest a deficit in dopamine release in prefrontal cortex in schizophrenia. Despite the prevalence of the concept of prefrontal cortical hypodopaminergia in schizophrenia, in vivo imaging of dopamine release in prefrontal cortex has not been possible until recently, when the validity of using the PET D2/3 radiotracer [¹¹C]FLB457 in combination with the amphetamine paradigm was clearly established.

Objectives

1) To test amphetamine induced dopamine release in dorsolateral prefrontal cortex (DLPFC) in drug free or drug naïve patients with schizophrenia (SCZ) and healthy controls (HC) matched for age, gender, ethnicity and familial socioeconomic status, 2) to test BOLD fMRI activation during a working memory task in the same subjects and 3) to examine the relationship between PET and fMRI outcome measures.

Design, Setting and Participants

PET imaging with [11C]FLB457 before and following 0.5 mg/kg P.O. amphetamine. BOLD fMRI during the self-ordered working memory task (SOWT). 20 patients with schizophrenia and 21 healthy controls participated.

Main outcome measure

The percent change in binding potential (ΔBPND) in DLPFC following amphetamine, BOLD activation during the SOWT compared to the control task, and the correlation between these two outcome measures.

Results

We observed: 1) significant differences in the effect of amphetamine on DLPFC BPND (ΔBPND in HC: − 7.5 ± 11%, SCZ: +1.8 ± 11%, p = 0.013), 2) a generalized blunting in dopamine release in SCZ involving most extrastriatal regions and the midbrain, 3) a significant relationship between ΔBPND and BOLD activation in DLPFC in the overall sample including patients with SCZ and HC.

Conclusions and Relevance

These results provide the first in vivo evidence for a deficit in the capacity for dopamine release in DLPFC in schizophrenia, and suggest a more widespread deficit extending to many cortical and extrastriatal regions, including the midbrain. This contrasts with the well-replicated excess in dopamine release in the associative striatum in schizophrenia, and suggests a differential regulation of striatal dopamine release in associative striatum versus extrastriatal regions. Furthermore, dopamine release in the DLPFC relates to working memory-related activation of this region, suggesting that blunted release may affect frontal cortical function.

Introduction

The concept of cortical hypodopaminergia in schizophrenia¹ has emerged from converging lines of evidence showing that working memory (WM) is deficient in schizophrenia², that WM depends critically on optimal prefrontal dopamine (DA) transmission in non-human primates^3–10, that it is associated with abnormal prefrontal activation during functional brain imaging studies in schizophrenia¹¹, and that it can improve with DA agonists^12–15. Furthermore, post-mortem studies reported a decrease in tyrosine hydroxylase immunolabeling in prefrontal cortex in schizophrenia^16–18. While Positron Emission Tomography (PET) studies have investigated alterations in cortical D1 receptor availability^19–21, there have been no in vivo studies examining capacity for DA release in frontal cortex in schizophrenia, a gap that contrasts with the considerable body of evidence from in vivo PET imaging studies showing an increase in stimulant-induced DA release in the striatum of patients with schizophrenia^22–24.

One major impediment to PET studies of cortical DA release has been the lack of a suitable PET radiotracer. For reasons that are not completely understood, D1 radiotracers have not proven to be sensitive to stimulant-induced DA release ²⁵ whereas D2/D3 tracers have. While radiotracers such as [¹¹C]raclopride and [¹¹C]-(+)-PHNO are useful for detecting acute fluctuations in DA levels in the striatum, the very low density and limited anatomical distribution of DA D2/D3 receptors in cortex²⁶ precludes their use for quantitative imaging of D2/D3 receptors in the cortex. [¹¹C]FLB457 is a higher-affinity PET tracer that has been shown to provide reliable quantification of amphetamine-induced DA release in cortex^27,28 (test-retest reproducibility ≤ 15% using conventional compartment analysis methods), although it cannot be quantified in striatum due to its slow washout in this high D2/D3 receptor density region. However, there are challenges in working with this tracer. Most D2/D3 tracers show negligible specific binding in the cerebellum, allowing the use of the cerebellum as a reference region²⁹. This is not the case for [¹¹C]FLB457, as approximately 20% of [¹¹C]FLB457 cerebellum distribution volume V_T can be displaced by the D2 partial agonist aripiprazole³⁰.

In the current study, we measured amphetamine-induced DA release in the dorsolateral prefrontal cortex (DLPFC) in patients with schizophrenia (SCZ) and matched healthy controls (HC) using [¹¹C]FLB457 PET imaging. We implemented a kinetic model with shared parameters across 9 cortical regions, that addressed both the lack of a reference region and the low cortical signal, to quantify receptor availability and DA release. We hypothesized that cortical DA release capacity, especially in the dorsolateral prefrontal cortex (DLPFC) would be reduced in SCZ compared to HC. We also examined a number of brain regions where D2/D3 receptor availability is intermediate between striatal and cortical binding, including midbrain (substantia nigra, ventral tegmental area), thalamus, and medial temporal regions (amygdala, hippocampus). To test the functional significance of cortical DA release capacity, we used functional MRI (fMRI) to measure changes in the BOLD signal in the DLPFC during performance of the Self-Ordered Working Memory Task (SOWT), and examined associations between cortical DA release capacity and WM task-related DLPFC activation. Finally, we examined the relationships between [¹¹C]FLB457 PET and WM-sensitive performance in SCZ and HC subjects, and clinical symptomatology in patients.

Materials and Methods

Subjects

This study was approved by the Institutional Review Boards of the New York State Psychiatric Institute (NYSPI) of Columbia University Medical Center (CUMC) and the Yale University Human Investigation Committee. All participants provided written informed consent following an independent assessment of capacity by a psychiatrist who was not a member of the research team. Patients were recruited from the inpatient and outpatient research facilities at NYSPI. Healthy controls were recruited through advertisements. Medical screening procedures included a physical examination and history, blood and urine tests, an electrocardiogram and a structural magnetic resonance imaging scan of the brain.

Inclusion criteria for patients were: (1) lifetime DSM-IV (Diagnostic and Statistical Manual of Mental Disorders, 4th ed.) diagnosis of schizophrenia, schizoaffective or schizophreniform disorder; (2) no bipolar disorder; (3) no antipsychotics for 3 weeks prior to the PET scan; (5) no history of violent behavior. Inclusion criteria for healthy controls were: (1) absence of any current or past DSM-IV Axis-I diagnosis; and (2) no family history (first-degree) of psychotic illness.

Exclusion criteria for both groups: significant medical and neurological illnesses, current misuse of substances other than nicotine, positive urine drug screen, pregnancy and nursing. Groups were matched for age, gender, ethnicity, parental socioeconomic status, and nicotine smoking (Table 1).

Table 1.

Demographics.

	HC (n = 21)	SCZ patients (n = 20; 1 schizoaffective, 19 schizophrenia)	p*
Age	32.6 ± 8.1	33.1 ± 10.2	.887
Sex (F/M)	11/10	10/10	.879
Ethnicity (C/AA/Hisp/As/mixed)	2/8/6/2/3	1/9/7/1/2	.913
Parental SES	35.9 ± 11.3	42.6 ± 14.5	.133
Participant SES	37.4 ± 14.2	21.4 ± 8.0	<.001
Nicotine smoking (No/Yes)	18/3	15/5	.638
Drug-naïve/drug-free	-	6/14
Onset psychotic symptoms (yrs)	-	17.3 ± 7.8
Duration of psychotic illness (yrs)	-	13.2 ± 11.3
Drug-free interval (months)	-	38.4 ± 67.3, (n = 14)

^*2 group t tests for continuous variables, χ² for categorical.

Abbreviation: SES = socioeconomic status.

PET Imaging Study Design

Subjects underwent two PET scans on one day with [¹¹C]FLB457 at the Yale University PET Center. A 90 min baseline scan was acquired, followed immediately by oral administration of amphetamine (0.5 mg/kg) and a second 90 min scan 3 hr after amphetamine administration. Arterial plasma data were collected to form metabolite-corrected input functions. Data were acquired on an HR+ scanner (Siemens, Knoxville TN) and reconstructed by filtered back projection with correction for attenuation, randoms and scatter. Data were binned into a sequence of frames of increasing duration.

PET Data Analysis

Preprocessing

A high resolution T1-weighted MRI scan was acquired for each subject. Regions of interest (ROIs) were drawn on each subject’s MRI according to previously described criteria ^20,31,32 (See eMethods.1 for operational definitions of amygdala and hippocampus), and included, in addition to the DLPFC, our a priori ROI, the medial frontal cortex (MFC), orbito-frontal cortex (OFC), anterior cingulate (A. CING), occipital cortex (OCC), parietal cortex (PAR), temporal cortex (TEMP), sub-genu of the cingulate (GEN), insula, cerebellum, and 5 subcortical regions: amygdala (AMYG), hippocampus (HIPP), midbrain encompassing substantia nigra and ventral tegmental area (SN/VTA), thalamus (THAL) and uncus. PET data were coregistered to the MRI data using normalized maximization of mutual information (SPM8) and the ROIs were transferred to the coregistered PET using MEDx software (Medical Numerics, Germantown MD). Time activity curves were generated as the average activity in each frame for each ROI.

Kinetic Analysis

Data were analyzed with a two tissue compartment model ^29,33 (2TC) that additionally incorporated a set of shared parameter estimates across regions, in order to improve reliability of fits by estimating a reduced parameter set compared to conventional 2TC. For each subject, V_ND, the distribution volume of the nondisplaceable compartment, and k₄, the specific binding dissociation constant, were fitted to a single value across cortical regions for both baseline and post-amphetamine scans (See eMethods.2, eTable.1, eFigure.1). The parameters K₁, the brain delivery constant and k₃, proportional to receptor availability, were fitted in each region and condition. Data were weighted by frame duration; all regions were weighted equally. The same procedure was applied separately to the higher binding subcortical regions, in order to allow for the possibility that the fitting procedure might assign different k₄ values in those regions. Distribution volume (V_T) was estimated in each region and condition. Binding potential (BP_ND) was estimated directly from the ratio k₃/k₄. We report BP_ND, the relative change following amphetamine (ΔBP_ND), V_T and ΔV_T.

Statistics

In DLPFC, two group t tests were applied to baseline BP_ND, ΔBP_ND, baseline V_T and ΔV_T. Additionally, linear mixed modeling with ROI as repeated measure and group and ROI as fixed variables was applied across all 14 regions. Two-sided t tests were applied to scan parameters including injected activity, injected mass, plasma free fraction (f_p) and estimated V_ND and k₄. Parameter estimates are reported as mean ± standard deviation.

fMRI Data Analysis

A subset of 16 SCZ and 18 HC participated in a BOLD fMRI study in which they performed the SOWT (among the 20 SCZ and 21 HC subjects, 4 SCZ and 2 HC declined to participate in the fMRI procedures, and one HC’s data were unusable due to poor image quality). Complete details of the task as well as acquisition and analysis are in eMethods.3. Briefly, structural and BOLD images were acquired on a Philips 1.5 T Intera scanner. BOLD images during SOWT task performance were acquired at a 3 mm isotropic voxel size with a TR of 2 s, separated into 9 runs of 160 volumes each. BOLD images underwent slice-timing correction, motion realignment, and coregistration to the T1-weighted structural images. A separate set of BOLD images were normalized to the ICBM template for voxelwise statistical analysis.. The SOWT task consists of a presentation of 8 different geometric shapes on a projection screen. Subjects select one of the shapes; on each successive trial, the positions of shapes on the screen are randomly reordered, and subjects are instructed to select a shape they haven’t picked previously. Thus subjects are required to hold up to 7 distinct items in working memory. There was a monetary incentive for correct answers ($0.25 per correct response). Complete analysis of the BOLD response to the SOWT will be presented elsewhere (Van Snellenberg et al, in submission). Here, we report only the relationship between BOLD and PET data. We regressed DLPFC ΔBP_ND against overall BOLD activation in DLPFC voxels that were significantly activated during the SOWT. A second-level TASK - CONTROL contrast was calculated on the ICBM-normalized BOLD data to identify voxels showing significant activation during SOWT performance. This group-level map was transformed to the individual subjects’ T1 space and the intersection of the activated region with their DLPFC ROI was used to extract BOLD signal change values within DLPFC for each participant. We used this approach to restrict analysis to DLPFC voxels that showed evidence of involvement in the SOWT.

Neurocognitive and Clinical Measures

Diagnostic status was determined with the Diagnostic Interview for Genetic Studies³⁴ in patients with SCZ followed by a consensus diagnosis conference and an abbreviated version of the Structured Clinical Interview for DSM-IV Axis I disorders³⁵ in HC. Severity of symptoms was assessed with the Positive and Negative Symptoms Scale (PANSS)³⁶, obtained at the start of the PET day. Participant and parental socio-economic status were calculated according to the Hollingshead scale³⁷. Clinical assessments were administered by trained interviewers.

As additional measures of WM, we also assessed performance on the N-back task³⁸ and the Letter Number Span (LNS)³⁹. Both tasks were acquired once on the day preceding the PET scans and a second time after the second PET scan. The n-back task contained three levels of difficulty, including 1, 2 and 3 back. Adjusted hit rate (AHR, the percent of properly identified targets corrected for false positives, see eMethods.4) was assessed as in Abi-Dargham et al²⁰, and ranged from a maximum possible score of 1 for perfect performance to −1 if all true targets were missed and all non-targets were incorrectly identified as targets.

Results

PET Scan Parameters

Injected activity, injected mass, plasma free fraction f_p for baseline and amphetamine conditions and estimated V_ND and k₄ are shown in Table 3. There were no significant group differences in any of these.

Table 3.

Scan Parameters

	HC (n = 21)	SCZ (n = 20)	2 grp t (p)
Base ID (MBq)	175 ± 20	177 ± 21	0.77
Amph ID (MBq)	174 ± 30	179 ± 20	0.50
Base IM (µg)	0.26 ± 0.13	0.24 ± 0.15	0.61
Amph IM (µg)	0.28 ± 0.17	0.26 ± 0.17	0.68
Base SA (MBq/nmol)	400 ± 467	417 ± 229	0.88
Amph SA (MBq/nmol)	344 ± 259	367 ± 176	0.74
fp (Base)	0.30 ± 0.09	0.27 ± 0.08	0.21
fp (Amph)	0.29 ± 0.08	0.26 ± 0.08	0.25
Plasma amph (ng/mL)	82.6 ± 14.2	81.4 ± 24.6*	0.86
Estimated VND	3.27 ± 0.55	2.97 ± 0.86	0.20
Estimated k4	0.023 ± 0.008	0.022 ± 0.005	0.50
Cerebellum VT (base)	4.56 ± 1.08	4.19 ± 0.95	0.23
Cerebellum VT (amph)	4.43 ± 1.11	4.29 ± 1.11	0.68

^*Plasma amphetamine available in n = 19 SCZ.

Abbreviations: ID = injected dose of radioactivity, IM = injected mass of radiotracer, SA = Specific Activity, Base = baseline scan, amph = post-amphetamine scan, f_p, V_ND, k₄ and V_T as in Innis et al 2007²⁹

PET Results

Baseline DLPFC BP_ND did not differ significantly between groups but ΔBP_ND did (HC: −7.5 ± 11%, SCZ: +1.8 ± 11%, p = 0.013) (Table 4). Linear mixed modeling of ΔBP_ND showed a statistically significant effect of group (F(1,39) = 6.95, p = 0.012) but no significant effect of ROI or group by ROI interaction. While the interaction term was not significant, two regions reached trend level group differences including DLPFC (p = 0.087) and SN/VTA (p = 0.096). BP_ND was higher in drug-naïve than drug-free patients but there were no significant differences in any PET outcome measure when age was included as a covariate (see eResults.1). V_T results are shown in eTable2. Baseline DLPFC V_T did not differ significantly between groups. There was a significant difference in DLPFC ΔV_T (HC: - 5 ± 7%, SCZ +1 ± 7%, p = 0.013). Linear mixed modeling of ΔV_T showed a statistically significant effect of group (F(1,39) = 4.11, p = 0.049) but no significant effect of ROI or group by ROI interaction.

Table 4.

BP_ND

	HC			SCZ
	Baseline	Post-Amph	ΔBPND	Baseline	Post-Amph	ΔBPND
DLPFC	1.8 ± 0.5	1.6 ± 0.4	−7.5 ± 11.4%	1.8 ± 0.7	1.8 ± 0.6	1.8 ± 11.1%
OFC	1.8 ± 0.5	1.7 ± 0.4	−5.5 ± 13.9%	1.9 ± 0.7	1.9 ± 0.6	2.6 ± 11.3%
MFC	1.9 ± 0.5	1.8 ± 0.4	−4.7 ± 13.2%	2.0 ± 0.8	2.0 ± 0.7	0.9 ± 10.9%
A. CING	2.2 ± 0.6	2 ± 0.5	−4.9 ± 15.7%	2.3 ± 0.8	2.3 ± 0.7	3.6 ± 12.5%
OCC CTX	1.8 ± 0.6	1.6 ± 0.5	−6.5 ± 12.1%	1.8 ± 0.7	1.8 ± 0.6	2.1 ± 10.0%
PAR CTX	2.0 ± 0.6	1.8 ± 0.5	−6.1 ± 9.8%	2.0 ± 0.7	2.0 ± 0.6	1.0 ± 10.4%
TEMP CT	3.4 ± 0.8	3.2 ± 0.8	−5.3 ± 10.0%	3.4 ± 1.0	3.4 ± 0.9	0.2 ± 8.6%
SUB GEN	2.4 ± 0.7	2.3 ± 0.5	−1.5 ± 18.1%	2.6 ± 1.0	2.6 ± 0.8	5.4 ± 18.0%
INSULA	3.4 ± 0.8	3.2 ± 0.7	−4.1 ± 10.2%	3.6 ± 1.1	3.7 ± 1.0	2.3 ± 9.8%
AMYGDALA	5.6 ± 1.7	5.4 ± 1.5	−1.9 ± 16.5%	5.8 ± 2.4	5.9 ± 2.5	2.0 ± 11.0%
HIPPOCAMPUS	2.5 ± 0.9	2.3 ± 0.7	−5.1 ± 14.4%	2.6 ± 1.5	2.6 ± 1.5	0.5 ± 11.9%
SN/VTA	4.6 ± 1.3	4.4 ± 1.2	−4.5 ± 12.7%	5.0 ± 2.5	5.1 ± 2.5	2.7 ± 10.8%
THALAMUS	6.2 ± 1.9	5.9 ± 1.6	−3.0 ± 11.7%	6.3 ± 2.5	6.5 ± 2.6	2.8 ± 10.1%
UNCUS	3.8 ± 1.2	3.6 ± 1.2	−5.9 ± 17.5%	4.0 ± 1.8	4.0 ± 1.9	−1.1 ± 11.6%

See PET methods for abbreviations

Associations with fMRI Activation

A significant relationship between DLPFC ΔBP_ND and BOLD activation in DLPFC was observed in SCZ and HC. Regression of ΔBP_ND onto BOLD activation had a significant effect of group (β = −10.2, t₃₁ = −2.707; P = 0.0109) and a significant effect of BOLD (β = 52.9; t₃₁ = 2.211; P = 0.0345), but no group by BOLD interaction, thus the most parsimonious model contained the same slope for both groups but different intercepts: BP_ND% decrease (HC) = 53 * BOLD (HC) % increase +6%, BP_ND% decrease (SCZ) = 53 * BOLD (SCZ) % increase − 4% (Figure 1).

Figure 1.

Scatterplot of DLPFC ΔBP_ND (left) and regression of DLPFC ΔBP_ND onto fMRI BOLD increase during SOWT (right). HC data is in black circles, drug-free SCZ data in red triangles and drug-naïve SCZ in blue triangles. Left: Group means are given by horizontal lines. Right: the lines represent the best linear model fit of the data, with slope equal to 53% for both groups and intercepts of 6% in HC and −4% in SCZ.

Associations with Working Memory Performance and Symptoms

Patients performed significantly worse on the following WM measures: baseline 1-back and post-amphetamine 2-back task and on baseline and post-amphetamine LNS (Table 2).

Table 2.

Clinical and Neurocognitive assessments

Assessment	HC (n = 21)	SCZ (n = 20)	2 grp t (p)
PANSS positive symptoms	7.0 ± 0.2	15.1 ± 4.7	<.001
PANSS negative symptoms	9.0 ± 2.5	13.6 ± 5.59	.001
PANSS general symptoms	17.9 ± 2.5	30.4 ± 8.2	<.001
1-back baseline AHR	.998 ± .007	.953 ± .061	.005
1-back post-amphetamine AHR	.991 ± .025	.947 ± .092	.060
2-back baseline AHR	.863 ± .158	.777 ± .248	.209
2-back post-amphetamine AHR	.916 ± .146	.768 ± .212	.014
3-back baseline AHR	.747 ± .229	.654 ± .310	.296
3-back post-amphetamine AHR	.730 ± .200	.608 ± .238	.088
LNS baseline	16.7 ± 3.3	13.7 ± 3.5	.010
LNS post-amphetamine	17.6 ± 3.9	14.4 ± 3.0	.009

Abbreviations: PANSS = positive and negative symptom scale, AHR = adjusted hit rate, LNS = letter number sequencing task

In SCZ, there were no correlations between WM performance and DLPFC BP_ND, ΔBP_ND, V_T, or ΔV_T. In HC, WM performance correlated with DLPFC BP_ND (baseline 2-back, ρ = .50, p = .031, baseline 3-back, ρ = .79, p <.001, SOWT, ρ = .50, p = .031) and V_T (baseline 3-back, ρ = .68, p = 0.001) in the DLPFC. Exploratory analyses of correlations at each level of the SOWT revealed significant correlations between DLPFC BP_ND and WM performance when WM load was greatest (step 7: r = 0.48, p = 0.042, step 8: r = 0.66, p = 0.003). Exploratory analyses including all fourteen ROIs, using a linear mixed model with ROIs as repeated measures resulted in an overall positive association between BP_ND in the analyzed regions and baseline 2-back (F(1,16.9) = 4.99, p = 0.039), 3-back (F(1,16.9) = 14.18, p = 0.002) and SOWT (F(103.4) = 8.08, p = 0.005) in HC. The same design applied to V_T showed an overall positive association of V_T with baseline 3-back (F(1,16.6) = 14.18, p = 0.002) in HC. There were no significant correlations between WM performance and ΔBP_ND or ΔV_T in HC. There were no significant correlations between PANSS scores (Table 2) and BP_ND, ΔBP_ND V_T, or ΔV_T..

Discussion

In this study we observed that patients with schizophrenia show blunted amphetamine-induced DA release in the DLPFC in vivo. This deficit in DA release extended to other extrastriatal regions including midbrain. We also observed a correlation between this index of DA release capacity and WM-related activation of the DLPFC, as measured with BOLD fMRI.

Despite the prevalence of the concept of hypodopaminergia in schizophrenia, there had been no empirical evidence for decreased cortical DA release prior to this study. This was related to the difficulty in measuring DA release in the cortex due to the low level of cortical D2 receptors²⁶ and the small range of displacement of D2 radiotracers by DA. Here, we adopted and optimized an [¹¹C]FLB457 displacement paradigm shown to be a valid and reliable proxy for changes in extracellular DA following an amphetamine challenge^27,40,41.

Precise quantification of [¹¹C]FLB457 displacement is challenging both because the signal is quite small despite the high affinity of [¹¹C]FLB457, and because the cerebellum cannot be used as a suitable reference tissue. Considering these factors, we developed a kinetic approach that was sensitive enough to detect a small change within a small signal. The shared parameter method we’ve applied here took advantage of the fact that [¹¹C]FLB457 kinetics are similar in many cortical regions, and greater parsimony can be achieved through the dramatic reduction of the number of estimated parameters. In simulations (see eMethods.2), we extensively tested cases in which the underlying assumptions of the shared parameter method - uniform k₄ across cortical regions and between scans, uniform V_ND between scans - were intentionally violated, and found that the method performed more precisely than conventional 2TC. We also note that the average estimated V_ND is 70% of cerebellum V_T, in agreement with Narendran et al³⁰.

In the primate PFC, [¹¹C]FLB457 displacement correlates with changes in extracellular DA across doses of amphetamine⁴¹. Amphetamine increases synaptic and extracellular DA by reversing the DA transporter⁴². Microdialysis measures the summed effects of a given drug on synaptic release, extrasynaptic release, and uptake. D2/D3 tracer displacement on the other hand is considered an index of synaptic DA release. This interpretation comes from the fact that while the PET measure reliably correlates with microdialysis measurements of extracellular DA across doses of a given DA-releasing drug, the slopes of these correlations differ across drugs and across brain regions. This presumably reflects differences in the relative contributions of DA release and uptake. This may be important for our interpretation of regional differences in amphetamine-induced D2/D3 tracer displacement in SCZ. Regulation of DA release and reuptake in the cortex and other extra-striatal regions differs from regulation in the striatum^43–46. For example, in the cortex and other regions with noradrenergic inputs, but not in the striatum, the norepinephrine transporter (NET) is also a major regulator of extracellular DA levels^43,44,47,48. A decrease in amphetamine-induced release in the DLPFC observed in SCZ could reflect decreased synthesis and vesicular storage, altered metabolism, or altered regulation of synaptic DA by the DAT or NET.

Surprisingly, our PET study uncovered widespread lower DA release in SCZ compared to HC encompassing most cortical and extrastriatal regions, including the ventral midbrain. Notably, this observation appears to be inconsistent with [¹⁸F]FDOPA PET and post-mortem findings supporting an increase in DA synthesis and/or storage in the midbrain in SCZ⁴⁹. Further experiments are needed to confirm whether the increase in [¹⁸F]FDOPA uptake and decreased DA release capacity in the midbrain co-exist within the same patients or whether they reflect an uncoupling of DA synthesis and release in the midbrain.

The contrast between the generalized DA deficit in cortical and extrastriatal regions and the increase in DA release in the striatum in SCZ^24,50–52 is particularly intriguing. This dissociation may reflect abnormal local presynaptic regulation of DA specific to the striatum existing on a background of DA release deficits. Alternatively, a discrete DA neuron subpopulation within the midbrain, innervating the associative striatum (AST), may be over-active. Taken together, the apparently discordant abnormalities across the midbrain, striatum and cortex raises the possibility that SCZ involves a widespread DA release deficit, co-existing with abnormal local dysregulation of DA release or uptake in the striatum (particularly AST), and possible uncoupling of DA synthesis and storage from dendritic DA release in the midbrain. More basic research is required to clarify the mechanisms regulating DA synthesis, vesicular storage, release and reuptake - and their coupling - in midbrain, cortex and striatum^45,46.

Since DA is important for frontal cortex-dependent cognition including WM, we examined the relationship between DLPFC DA release capacity and fMRI BOLD activation within the DLPFC during WM performance. DA release correlated with BOLD activation, and did not differ between groups. The relationship between BOLD and DA release suggests that fluctuations in DA release in the DLPFC may modulate the strength of the hemodynamic (and presumably neuronal) response to cognitive “processing demands” placed on DLPFC circuitry. Interestingly, while release capacity correlated with the cortical response to the WM challenge, it did not predict performance, which was impaired in patients with SCZ. One potential explanation is that WM performance is tightly coupled to DA release dynamics during cognitive challenges⁵³, a measure not captured by amphetamine-induced release. Notably, we found a positive association between D2 BP_ND and WM performance in HC but not in SCZ. Consistent with reports that D2 stimulation can effectively gate synaptic plasticity in cortical projection neurons⁵⁴, this finding suggests that under normal conditions, D2 availability may be a rate-limiting factor for WM whereas in SCZ, WM capacity is limited by mechanisms upstream or independent of DLPFC D2 receptors.

In summary, our study established that in SCZ, amphetamine-induced DA release is deficient. This contrasts with the well-replicated increased DA storage and release in the striatum in SCZ. Moreover, the relationships between DA indices and prefrontal cortical function during WM are complex and may be modulated in part by the availability of DA receptors. These findings highlight the need to fully determine the molecular mechanisms regulating DA synthesis, storage, release and reuptake, and examine how these mechanisms operate in different DA projection fields. Such studies will lead to an understanding of the complex dopaminergic phenotype in schizophrenia, and advance the development of a coordinated treatment strategy for symptoms and cognitive disturbances in this disorder.