Importance:In prior work, a transgenic mouse model of the striatal dopamine dysfunction observed in persons with schizophrenia (PSZ) exhibited dopamine-related neuroplasticity in the basal ganglia. This phenotype has never been demonstrated in human PSZ. Objective:To identify a specific dopamine-related alteration of basal ganglia connectivity via task-based and resting-state functional magnetic resonance imaging (fMRI), neuromelanin-sensitive MRI (NM-MRI), and positron emission tomography (PET), in unmedicated PSZ. Design:This case-control study of unmedicated PSZ and healthy controls (HC) occurred between November 2014 and June 2018, with analyses performed between April 2023 and February 2025. Setting:fMRI and NM-MRI were collected at New York State Psychiatric Institute. [11C]-(+)-PHNO PET was collected at Yale University. Participants:Participants were aged 18-55, and demographically matched. PSZ were antipsychotic drug-naïve or drug-free for at least three weeks prior to recruitment. Main Outcomes and Measures:1) task-state and resting-state functional connectivity (FC) between dorsal caudate (DCa) and globus pallidus externus (GPe), 2) NM-MRI contrast ratio in substantia nigra voxels associated with psychotic symptom severity, and 3) baseline and amphetamine-induced change in [11C]-(+)-PHNO binding potential in DCa. Results:37 PSZ (mean±SD age, 32.7±12.7 years, 29.7% female) and 30 HC (32.5±9.7 years, 26.7% female) underwent resting-state fMRI; 29 PSZ (33.4±12.7 years, 31% female) and 29 HC (32.4±9.7 years, 31% female) underwent working memory task-based fMRI. 22 PSZ (35.1±13.9 years, 36.4% female) and 20 HC (29.4±8.5 years, 35% female) underwent NM-MRI. 7 PSZ (23.1±6.3 years, 57.1% female) and 4 HC (31.5±11.9 years, 25% female) underwent [11C]-(+)-PHNO PET with amphetamine challenge. PSZ displayed elevated task-state FC (0.11±0.10 versus 0.05±0.09 in HC; P=0.0252), which was associated with increased NM-MRI contrast ratio (β* [SE] = 0.40 [0.17]; P=0.023), decreased baseline D2 receptor availability (β* [SE] = -0.45 [0.17]; P=0.039), greater amphetamine-induced dopamine release (β* [SE] = -0.82 [0.27]; P=0.021), and worse task performance (β* [SE] = -0.31 [0.13]; P=0.020). Conclusions and Relevance:This study provides in-vivo evidence of a dopamine-associated neural abnormality of DCa and GPe connectivity in unmedicated PSZ. This phenotype suggests a potential neurodevelopmental mechanism of working memory deficits in schizophrenia, representing a critical step towards developing treatments for cognitive deficits.

ABSTRACT In studying the neural correlates of working memory (WM) ability via functional magnetic resonance imaging (fMRI) in health and disease, it is relatively uncommon for investigators to report associations between brain activation and measures of task performance. Additionally, how the choice of WM task impacts observed activation–performance relationships is poorly understood. We sought to illustrate the impact of WM task on brain–behavior correlations using two large, publicly available datasets. We conducted between‐participants analyses of task‐based fMRI data from two publicly available datasets: The Human Connectome Project (HCP; n = 866) and the Queensland Twin Imaging (QTIM) Study ( n = 459). Participants performed two distinct variations of the n‐ back WM task with different stimuli, timings, and response paradigms. Associations between brain activation ([2‐back − 0‐back] contrast) and task performance (2‐back % correct) were investigated separately in each dataset, as well as across datasets, within the dorsolateral prefrontal cortex (dlPFC), medial prefrontal cortex, and whole cortex. Global patterns of activation to task were similar in both datasets. However, opposite associations between activation and task performance were observed in bilateral pre‐supplementary motor area and left middle frontal gyrus. Within the dlPFC, HCP participants exhibited a significantly greater activation–performance relationship in bilateral middle frontal gyrus relative to QTIM Study participants. The observation of diverging activation–performance relationships between two large datasets performing variations of the n ‐back task serves as a critical reminder for investigators to exercise caution when selecting WM tasks and interpreting neural activation in response to a WM task.

Working memory (WM) is an essential system of cognitive processes for a wide range of cognitive activities and is associated with diverse real-world outcomes. Despite extensive research in cognitive psychology, the complex multifaceted nature of WM is often overlooked in applied settings, such as clinical and neuroimaging research. This study investigated the latent structure of WM by examining a comprehensive set of WM tasks commonly used in both theoretical and applied research in cognitive psychology and psychiatric neuroimaging. A large sample of healthy, young adults (N = 608) completed a battery of WM tasks and other cognitive measures. Factor analyses and structural equation models revealed a three-factor structure: Storage, Executive Attention, and Updating. These factors were moderately correlated but contributed uniquely to explaining variance in intelligence measures. Furthermore, when the three factors were considered in a single model, only the Updating and Executive Attention factors had unique shared variance with intelligence. The findings support that WM is a multifaceted construct, with complex span and n-back tasks capturing important and distinct components related to real-world cognitive performance. This highlights the need for precise selection of measurement tools for WM in both theoretical and applied research contexts.

BACKGROUND:Converging evidence from clinical neuroimaging and animal models has strongly implicated dysfunction of thalamocortical circuits in the pathophysiology of schizophrenia (SZ). Preclinical models of genetic risk for SZ have shown reduced synaptic transmission from the auditory thalamus to the primary auditory cortex, which may represent a correlate of auditory disturbances such as hallucinations. However, human neuroimaging studies have found a generalized increase in resting-state functional connectivity (RSFC) between whole thalamus and sensorimotor cortex in people with SZ (PSZ). We aimed to more directly translate preclinical findings by specifically localizing auditory and visual thalamic nuclei in unmedicated PSZ and measuring RSFC to primary sensory cortices. METHODS:In this case-control study, 82 unmedicated PSZ and 55 matched healthy control participants (HCs) completed RSFC functional magnetic resonance imaging (fMRI) (N = 137). Auditory and visual thalamic nuclei were localized for 55 unmedicated PSZ and 46 HCs who also completed a sensory thalamic nuclei localizer fMRI task (n = 101). Using localized nuclei as RSFC seeds, we assessed group differences in auditory and visual thalamocortical connectivity and associations with positive symptom severity. RESULTS:Auditory thalamocortical connectivity was not significantly different between PSZ and HCs, but hyperconnectivity was associated with greater positive symptom severity in the bilateral superior temporal gyrus. Visual thalamocortical connectivity was significantly greater in PSZ relative to HCs in the secondary and higher-order visual cortex but was not predictive of positive symptom severity. CONCLUSIONS:These results indicate that visual thalamocortical hyperconnectivity is a generalized marker of SZ, while hyperconnectivity in auditory thalamocortical circuits relates more specifically to positive symptom severity.

Despite decades of research, cognitive impairment remains a critical untreated symptom for many patients with schizophrenia. One way to accelerate the development of pro-cognitive therapies for schizophrenia is to evaluate compounds using biomarker approaches tailored to relevant neural mechanisms. While D1/D5 receptor (D1R/D5R) agonism has been extensively studied in neuroscience, its therapeutic potential for cognitive impairment in schizophrenia remains untapped. The Translational Neuroscience & Computational Evaluation of a D1R Partial Agonist for Schizophrenia (TRANSCENDS) clinical trial tests this mechanism using a 'target engagement' approach. Multiple, double-blind doses of a D1/D5R partial agonist were administered in advance of a functional neuroimaging (fMRI) session that deployed a cognitive paradigm explicitly designed to capture a translational micro-circuit mechanism underlying spatial working memory in patients with schizophrenia. Specifically, this study will assess whether the D1R/D5R partial agonist CVL-562 induces a dose-dependent engagement of spatial working memory circuits in schizophrenia using fMRI. This design, and the use of spatial working memory neural circuits as a dependent measure, was selected on the basis of a translational and computational understanding of prefrontal micro-circuitry and a mechanistic understanding of the role of D1R/D5Rs in schizophrenia. To enhance data integration and scalability, TRANSCENDS employs an automated informatics framework for seamless neuroimaging data sharing and electronic clinical data capture. This ensures high-standards for regulatory compliance, data quality, and data sharing across sites, improving aspects of current clinical trial data management. We share the study design and approach with the goal of advancing future pro-cognitive drug development and strategies for developing mechanistically-driven biomarkers in psychiatry.

Functional magnetic resonance imaging (fMRI) of the auditory and visual sensory systems of the human brain is an active area of investigation in the study of human health and disease. The medial geniculate nucleus (MGN) and lateral geniculate nucleus (LGN) are key thalamic nuclei involved in the processing and relay of auditory and visual information, respectively, and are the subject of blood-oxygen-level-dependent (BOLD) fMRI studies of neural activation and functional connectivity in human participants. However, localization of BOLD fMRI signal originating from neural activity in MGN and LGN remains a technical challenge, due, in part, to the poor definition of boundaries of these thalamic nuclei in standard T1-weighted and T2-weighted magnetic resonance imaging sequences. Here, we report the development and evaluation of an auditory and visual sensory thalamic localizer (TL) fMRI task that produces participant-specific functionally-defined regions of interest (fROIs) of both MGN and LGN, using 3 Tesla multiband fMRI and a clustered-sparse temporal acquisition sequence, in less than 16 minutes of scan time. We demonstrate the use of MGN and LGN fROIs obtained from the TL fMRI task in standard resting-state functional connectivity (RSFC) fMRI analyses in the same participants. In RSFC analyses, we validated the specificity of MGN and LGN fROIs for signals obtained from primary auditory and visual cortex, respectively, and benchmarked their performance against alternative atlas- and segmentation-based localization methods. The TL fMRI task and analysis code (written in Presentation and MATLAB, respectively) have been made freely available to the wider research community.

Simultaneous multi-slice (multiband) acceleration in fMRI has become widespread, but may be affected by novel forms of signal artifact. Here, we demonstrate a previously unreported artifact manifesting as a shared signal between simultaneously acquired slices in all resting-state and task-based multiband fMRI datasets we investigated, including publicly available consortium data from the Human Connectome Project (HCP) and Adolescent Brain Cognitive Development (ABCD) Study. We propose Multiband Artifact Regression in Simultaneous Slices (MARSS), a regression-based detection and correction technique that successfully mitigates this shared signal in unprocessed data. We demonstrate that the signal isolated by MARSS correction is likely nonneural, appearing stronger in neurovasculature than gray matter. Additionally, we evaluate MARSS both against and in tandem with sICA+FIX denoising, which is implemented in HCP resting-state data, to show that MARSS mitigates residual artifact signal that is not modeled by sICA+FIX. MARSS correction leads to study-wide increases in signal-to-noise ratio, decreases in cortical coefficient of variation, and mitigation of systematic artefactual spatial patterns in participant-level task betas. Finally, MARSS correction has substantive effects on second-level t-statistics in analyses of task-evoked activation. We recommend that investigators apply MARSS to multiband fMRI datasets with moderate or higher acceleration factors, in combination with established denoising methods.

Background Despite longstanding interest in the central cholinergic system in schizophrenia (SCZ), cholinergic imaging studies with patients have been limited to receptors. Here, we conducted a proof-of-concept positron emission tomography study using [18F]-VAT, a new radiotracer that targets the vesicular acetylcholine transporter as a proxy measure of acetylcholine transmission capacity, in patients with SCZ and explored relationships of vesicular acetylcholine transporter with clinical symptoms and cognition. Methods A total of 18 adult patients with SCZ or schizoaffective disorder (the SCZ group) and 14 healthy control participants underwent a positron emission tomography scan with [18F]-VAT. Distribution volume (VT) for [18F]-VAT was derived for each region of interest, and group differences in VT were assessed with 2-sample t tests. Functional significance was explored through correlations between VT and scores on the Positive and Negative Syndrome Scale and a computerized neurocognitive battery (PennCNB). Results No group differences in [18F]-VAT VT were observed. However, within the SCZ group, psychosis symptom severity was positively associated with VT in multiple regions of interest, with the strongest effects in the hippocampus, thalamus, midbrain, cerebellum, and cortex. In addition, in the SCZ group, working memory performance was negatively associated with VT in the substantia innominata and several cortical regions of interest including the dorsolateral prefrontal cortex. Conclusions In this initial study, the severity of 2 important features of SCZ—psychosis and working memory deficit—was strongly associated with [18F]-VAT VT in several cortical and subcortical regions. These correlations provide preliminary evidence of cholinergic activity involvement in SCZ and, if replicated in larger samples, could lead to a more complete mechanistic understanding of psychosis and cognitive deficits in SCZ and the development of therapeutic targets.

BACKGROUND: Schizophrenia (SCZ) is marked by working memory (WM) deficits, which predict poor functional outcome. While most functional magnetic resonance imaging studies of WM in SCZ have focused on the dorsolateral prefrontal cortex (PFC), some recent work suggests that the medial PFC (mPFC) may play a role. We investigated whether task-evoked mPFC deactivation is associated with WM performance and whether it mediates deficits in SCZ. In addition, we investigated associations between mPFC deactivation and cortical dopamine release.METHODS: Patients with SCZ (n = 41) and healthy control participants (HCs) (n = 40) performed a visual object n-back task during functional magnetic resonance imaging. Dopamine release capacity in mPFC was quantified with [11C]FLB457 in a subset of participants (9 SCZ, 14 HCs) using an amphetamine challenge. Correlations between task-evoked deactivation and performance were assessed in mPFC and dorsolateral PFC masks and were further examined for relationships with diagnosis and dopamine release.RESULTS: mPFC deactivation was associated with WM task performance, but dorsolateral PFC activation was not. Deactivation in the mPFC was reduced in patients with SCZ relative to HCs and mediated the relationship between diagnosis and WM performance. In addition, mPFC deactivation was significantly and inversely associated with dopamine release capacity across groups and in HCs alone, but not in patients.CONCLUSIONS: Reduced WM task-evoked mPFC deactivation is a mediator of, and potential substrate for, WM impairment in SCZ, although our study design does not rule out the possibility that these findings could relate to cognition in general rather than WM specifically. We further present preliminary evidence of an inverse association between deactivation during WM tasks and dopamine release capacity in the mPFC.

Simultaneous multi-slice (multiband) accelerated functional magnetic resonance imaging (fMRI) provides dramatically improved temporal and spatial resolution for resting-state functional connectivity (RSFC) studies of the human brain in health and disease. However, multiband acceleration also poses unique challenges for denoising of subject motion induced data artifacts, the presence of which is a major confound in RSFC research that substantively diminishes reliability and reproducibility. We comprehensively evaluated existing and novel approaches to volume censoring-based motion denoising in the Human Connectome Project (HCP) dataset. We show that assumptions underlying common metrics for evaluating motion denoising pipelines, especially those based on quality control-functional connectivity (QC-FC) correlations and differences between high- and low-motion participants, are problematic, and appear to be inappropriate in their current widespread use as indicators of comparative pipeline performance and as targets for investigators to use when tuning pipelines for their own datasets. We further develop two new quantitative metrics that are instead agnostic to QC-FC correlations and other measures that rely upon the null assumption that no true relationships exist between trait measures of subject motion and functional connectivity, and demonstrate their use as benchmarks for comparing volume censoring methods. Finally, we develop and validate quantitative methods for determining dataset-specific optimal volume censoring parameters prior to the final analysis of a dataset, and provide straightforward recommendations and code for all investigators to apply this optimized approach to their own RSFC datasets.

The phenotype of schizophrenia, regardless of etiology, represents the most studied psychotic disorder with respect to neurobiology and distinct phases of illness. The early phase of illness represents a unique opportunity to provide effective and individualized interventions that can alter illness trajectories. Developmental age and illness stage, including temporal variation in neurobiology, can be targeted to develop phase-specific clinical assessment, biomarkers, and interventions. We review an earlier model whereby an initial glutamate signaling deficit progresses through different phases of allostatic adaptation, moving from potentially reversible functional abnormalities associated with early psychosis and working memory dysfunction, and ending with difficult-to-reverse structural changes after chronic illness. We integrate this model with evidence of dopaminergic abnormalities, including cortical D1 dysfunction, which develop during adolescence. We discuss how this model and a focus on a potential critical window of intervention in the early stages of schizophrenia impact the approach to research design and clinical care. This impact includes stage-specific considerations for symptom assessment as well as genetic, cognitive, and neurophysiological biomarkers. We examine how phase-specific biomarkers of illness phase and brain development can be incorporated into current strategies for large-scale research and clinical programs implementing coordinated specialty care. We highlight working memory and D1 dysfunction as early treatment targets that can substantially affect functional outcome.

Patients with schizophrenia have a high prevalence of cigarette smoking and respond poorly to conventional treatments, highlighting the need for new therapies. We conducted a mechanistic, proof-of-concept study using bilateral deep repetitive transcranial magnetic stimulation (dTMS) of insular and prefrontal cortices at high frequency, using the specialized H4 coil. Feasibility of dTMS was tested for disruption of tobacco self-administration, insula target engagement, and insula circuit modulation, all of which were a priori outcomes of interest. Twenty patients completed the study, consisting of weekday dTMS sessions (randomization to active dTMS or sham; double-blind; 10 patients per group), a laboratory tobacco self-administration paradigm (pre/post assessments), and multimodal imaging (three MRI total sessions). Results showed that participants assigned to active dTMS were slower to initiate smoking their first cigarette compared with sham, consistent with smoking disruption. The imaging analyses did not reveal significant Time × Group interactions, but effects were in the anticipated directions. In arterial spin labeling analyses testing for target engagement, an overall decrease in insula blood flow, measured during a post-treatment MRI versus baseline, was numerically more pronounced in the active dTMS group than sham. In fMRI analyses, resting-state connectivity between the insula and default mode network showed a numerically greater change from baseline in the active dTMS group than sham, consistent with a functional change to insula circuits. Exploratory analyses further suggested a therapeutic effect of dTMS on symptoms of psychosis. These initial observations pave the way for future confirmatory studies of dTMS in smoking patients with schizophrenia.

Decades of research have highlighted the importance of optimal stimulation of cortical dopaminergic receptors, particularly the D1R receptor (D1R), for prefrontal-mediated cognition. This mechanism is particularly relevant to the cognitive deficits in schizophrenia, given the abnormalities in cortical dopamine (DA) neurotransmission and in the expression of D1R. Despite the critical need for D1R-based therapeutics, many factors have complicated their development and prevented this important therapeutic target from being adequately interrogated. Challenges include determination of the optimal level of D1R stimulation needed to improve cognitive performance, especially when D1R expression levels, affinity states, DA levels, and the resulting D1R occupancy by DA, are not clearly known in schizophrenia, and may display great interindividual and intraindividual variability related to cognitive states and other physiological variables. These directly affect the selection of the level of stimulation necessary to correct the underlying neurobiology. The optimal mechanism for stimulation is also unknown and could include partial or full agonism, biased agonism, or positive allosteric modulation. Furthermore, the development of D1R targeting drugs has been complicated by complexities in extrapolating from in vitro affinity determinations to in vivo use. Prior D1R-targeted drugs have been unsuccessful due to poor bioavailability, pharmacokinetics, and insufficient target engagement at tolerable doses. Newer drugs have recently become available, and these must be tested in the context of carefully designed paradigms that address methodological challenges. In this paper, we discuss how a better understanding of these challenges has shaped our proposed experimental design for testing a new D1R/D5R partial agonist, PF-06412562, renamed CVL-562.

BackgroundRecent studies have established a clear topographical and functional organization of projections to and from complex subdivisions of the striatum. Manual segmentation of these functional subdivisions is labor‐intensive and time‐consuming, and automated methods are not as reliable as manual segmentation.PurposeTo utilize multitask learning (MTL) as a method to segment subregions of the striatum consisting of pre‐commissural putamen (prePU), pre‐commissural caudate (preCA), post‐commissural putamen (postPU), post‐commissural caudate (postCA), and ventral striatum (VST).Study TypeRetrospective.PopulationEighty‐seven total data sets from patients with schizophrenia and matched controls.Field Strength/Sequence1.5 T and 3.0 T, T1‐weighted (SPGR SENSE, 3D BRAVO).AssessmentMTL‐generated segmentations were compared to the Imperial College London Clinical Imaging Center (CIC) atlas. Dice similarity coefficient (DSC) was used to compare the automated methods to manual segmentations. Positron emission tomography (PET) imaging: 60 minutes of emission data were acquired using [11C]raclopride. Data were reconstructed by filtered back projection (FBP) with computed tomography (CT) used for attenuation correction. Binding potential values, BPND, and region of interest (ROI) time series and whole‐brain connectivity using functional magnetic resonance imaging (fMRI) images were compared between manual and both automated segmentations.Statistical TestsPearson correlation and paired t‐test.ResultsMTL‐generated segmentations showed excellent spatial agreement with manual (DSC ≥0.72 across all striatal subregions). BPND values from MTL‐generated segmentations were shown to correlate well with manual segmentations with R2 ≥ 0.91 in all caudate and putamen subregions, and R2 = 0.69 in VST. Mean Pearson correlation coefficients of the fMRI data between MTL‐generated and manual segmentations were also high in time series (≥0.86) and whole‐brain connectivity (≥0.89) across all subregions.Data ConclusionAcross both PET and fMRI task‐based assessments, results from MTL‐generated segmentations more closely corresponded to results from manually drawn ROIs than CIC‐generated segmentations did. Therefore, the proposed MTL approach is a fast and reliable method for three‐dimensional striatal subregion segmentation with results comparable to manually segmented ROIs.Level of Evidence2Technical Efficacy Stage1

Background: While cigarette smoking has plummeted in the general population, rates have stagnated in patients with schizophrenia. We tested a 3-week regimen of deep repetitive transcranial magnetic stimulation (dTMS) capable of reaching the insula, which plays an important role in maintaining smoking.

1175 Introduction: Segmenting the human striatum to assess functionality has proven to be critical in studying neurological and neuropsychiatric diseases. Studies in nonhuman primates have established a clear functional organization of projections to and from complex striatal subregions1. . Imaging researchers, particularly in positron emission tomography (PET), have translated this functional organization into operationally defined substructures that can be manually traced on T1-weighted MR images2,3. Nevertheless, this manual segmentation is time-consuming. More rapid atlas-based methods have been developed such as the Imperial College London Clinical Imaging Center (CIC). Although easy to implement, atlas-based methods typically nonlinear warp the input volume which may affect smaller ROI’s such as striatal subregions4. We have developed a deep learning technique using Multi-Task Learning (MTL) that jointly learns how to segment striatal subregions consisting of pre-commissural putamen (prePU), pre-commissural caudate (preCA), post-commissural putamen (postPU), post-commissural caudate (postCA), and ventral striatum (VST) using a convolutional neural network (CNN). In this work, we employ a 3-D U-Net architecture with single MRI input and multiple segmentation outputs and compare it to CIC segmentations using PET and fMRI objective assessment. Methods: 68 3D T1-weighted images and their corresponding manually drawn striatal subregion segmentations were used to train the network. The datasets consisted of patients with schizophrenia and matched controls. As shown in Figure 1, this MTL network consisted of a 3D U-Net architecture. This model was trained to minimize the sparse softmax cross entropy. An independent dataset of 19 volunteers imaged with [11C]raclopride PET and MRI were used for the testing set. For additional comparison, striatal substructure ROIs from the CIC atlas5 were retrieved from the MIAKAT (Imanova, Ltd; London, UK) software package, and applied to the test data. Dice Similarity Coefficients (DSC) were utilized to initially evaluate the performance of the automated methods. Multi-modal assessment consisted of PET and fMRI analysis. 60 min of dynamic emission data were acquired on an Siemens mCT scanner, following a bolus injection of 349 +/- 109 MBq of [11C]raclopride. Data were reconstructed by FBP with CT used for attenuation correction. Binding Potential (BPND) was derived in each ROI using simplified reference tissue model (SRTM)6 with cerebellum as reference tissue. Linear Regression was performed with BPND obtained from manual and MTL-generated ROIs and R2 was reported. fMRI test dataset consisted of multiband blood oxygen level dependent (BOLD) MR sequences. Raw timeseries and whole-brain RSFC were extracted from both hand-drawn and automated ROIs following preprocessing. Finally, the correlation across voxels in RSFC was calculated between hand-drawn and automated ROIs. Results: Striatal segmentations from manual and automated methods can be seen in Figure 2. When comparing DSC, MTL-generated segmentations were more comparable to manual segmentations than CIC across all ROI’s (Table 1). When comparing PET quantification, R-square show that MTL is more comparable to manual segmentations than CIC across all ROI’s (Table 2). In terms of fMRI connectivity, MTL segmentations also had closer correlation to manually drawn ROI’s (rMTL > 0.87 ; rCIC > 0.76) across all ROI9s. Conclusion: The developed MTL framework for striatal subregion segmentation shown here provides reliable segmentations with more comparable PET and fMRI results that more closely match those obtained with manually drawn ROIs than atlas-based segmentations.