Advancing motion denoising of multiband resting-state functional connectivity fMRI data.

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.

Multitask Learning Based Three-Dimensional Striatal Segmentation of MRI: fMRI and PET Objective Assessments.

BACKGROUND: Recent 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. PURPOSE: To 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 TYPE: Retrospective. POPULATION: Eighty-seven total data sets from patients with schizophrenia and matched controls. FIELD STRENGTH/SEQUENCE: 1.5 T and 3.0 T, T1 -weighted (SPGR SENSE, 3D BRAVO). ASSESSMENT: MTL-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 [11 C]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 TESTS: Pearson correlation and paired t-test. RESULTS: MTL-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 CONCLUSION: Across 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 EVIDENCE: 2 TECHNICAL EFFICACY STAGE: 1.

Intrinsic neural circuitry of depression in adolescent females.

BACKGROUND: Adolescence is characterized by affective and cognitive changes that increase vulnerability to depression, especially in females. Neurodevelopmental models attribute adolescent depression to abnormal responses in amygdala, striatum, and prefrontal cortex (PFC). We examined whether the strength of functional brain networks involving these regions predicts depression symptoms in adolescent females. METHODS: In this longitudinal study, we recorded resting-state functional connectivity (RSFC) in 174 adolescent females. Using a cross-validation strategy, we related RSFC profiles that included (a) a network consisting of amygdala, striatum, and PFC (within-circuit model), (b) connectivity of this network to the whole brain (extended-circuit model), and (c) a network consisting of the entire brain (whole-brain model) to depression symptoms assessed concurrently and 18 months later. RESULTS: In testing subsets, the within-circuit RSFC profiles were associated with depression symptoms concurrently and 18 months later, while the extended-circuit and whole-brain model did not explain any additional variance in depression symptoms. Connectivity related to anterior cingulate and ventromedial prefrontal cortex contributed most to the association. CONCLUSIONS: Our results demonstrate that RSFC-based brain networks that include amygdala, striatum, and PFC are stable neural signatures of concurrent and future depression symptoms, representing a significant step toward identifying the neural mechanism of depression in adolescence.

Dynamic Connectivity between Brain Networks Supports Working Memory: Relationships to Dopamine Release and Schizophrenia.

Connectivity between brain networks may adapt flexibly to cognitive demand, a process that could underlie adaptive behaviors and cognitive deficits, such as those observed in neuropsychiatric conditions like schizophrenia. Dopamine signaling is critical for working memory but its influence on internetwork connectivity is relatively unknown. We addressed these questions in healthy humans using functional magnetic resonance imaging (during ann-back working-memory task) and positron emission tomography using the radiotracer [(11)C]FLB457 before and after amphetamine to measure the capacity for dopamine release in extrastriatal brain regions. Brain networks were defined by spatial independent component analysis (ICA) and working-memory-load-dependent connectivity between task-relevant pairs of networks was determined via a modified psychophysiological interaction analysis. For most pairs of task-relevant networks, connectivity significantly changed as a function of working-memory load. Moreover, load-dependent changes in connectivity between left and right frontoparietal networks (Δ connectivity lFPN-rFPN) predicted interindividual differences in task performance more accurately than other fMRI and PET imaging measures. Δ Connectivity lFPN-rFPN was not related to cortical dopamine release capacity. A second study in unmedicated patients with schizophrenia showed no abnormalities in load-dependent connectivity but showed a weaker relationship between Δ connectivity lFPN-rFPN and working memory performance in patients compared with matched healthy individuals. Poor working memory performance in patients was, in contrast, related to deficient cortical dopamine release. Our findings indicate that interactions between brain networks dynamically adapt to fluctuating environmental demands. These dynamic adaptations underlie successful working memory performance in healthy individuals and are not well predicted by amphetamine-induced dopamine release capacity. SIGNIFICANCE STATEMENT: It is unclear how communication between brain networks responds to changing environmental demands during complex cognitive processes. Also, unknown in regard to these network dynamics is the role of neuromodulators, such as dopamine, and whether their dysregulation could underlie cognitive deficits in neuropsychiatric illness. We found that connectivity between brain networks changes with working-memory load and greater increases predict better working memory performance; however, it was not related to capacity for dopamine release in the cortex. Patients with schizophrenia did show dynamic internetwork connectivity; however, this was more weakly associated with successful performance in patients compared with healthy individuals. Our findings indicate that dynamic interactions between brain networks may support the type of flexible adaptations essential to goal-directed behavior.

Dynamic shifts in brain network activation during supracapacity working memory task performance.

Despite significant advances in understanding how brain networks support working memory (WM) and cognitive control, relatively little is known about how these networks respond when cognitive capabilities are overtaxed. We used a fine-grained manipulation of memory load within a single trial to exceed WM capacity during functional magnetic resonance imaging to investigate how these networks respond to support task performance when WM capacity is exceeded. Analyzing correct trials only, we observed a nonmonotonic (inverted-U) response to WM load throughout the classic WM network (including bilateral dorsolateral prefrontal cortex, posterior parietal cortex, and presupplementary motor areas) that peaked later in individuals with greater WM capacity. We also observed a relative increase in activity in medial anterior prefrontal cortex, posterior cingulate/precuneus, and lateral temporal and parietal regions at the highest WM loads, and a set of predominantly subcortical and prefrontal regions whose activation was greatest at the lowest WM loads. At the individual subject level, the inverted-U pattern was associated with poorer performance while expression of the early and late activating patterns was predictive of better performance. In addition, greater activation in bilateral fusiform gyrus and right occipital lobe at the highest WM loads predicted better performance. These results demonstrate dynamic and behaviorally relevant changes in the level of activation of multiple brain networks in response to increasing WM load that are not well accounted for by present models of how the brain subserves the cognitive ability to hold and manipulate information on-line.

Capacity estimates in working memory: Reliability and interrelationships among tasks.

The concept of capacity has become increasingly important in discussions of working memory (WM), in so far as most models of WM conceptualize it as a limited-capacity mechanism for maintaining information in an active state, and as capacity estimates from at least one type of WM task-complex span-are valid predictors of real-world cognitive performance. However, the term capacity is also often used in the context of a distinct set of WM tasks, change detection, and may or may not refer to the same cognitive capability. We here develop maximum-likelihood models of capacity from each of these tasks-as well as from a third WM task that places heavy demands on cognitive control, the self-ordered WM task (SOT)-and show that the capacity estimates from change detection and complex span tasks are not correlated with each other, although capacity estimates from change detection tasks do correlate with those from the SOT. Furthermore, exploratory factor analysis confirmed that performance on the SOT and change detection load on the same factor, with performance on our complex span task loading on its own factor. These findings suggest that at least two distinct cognitive capabilities underlie the concept of WM capacity as it applies to each of these three tasks.

A tale of two n-backs: Diverging associations of dorsolateral prefrontal cortex activation with n-back task performance.

Background: 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. Methods: 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. Results: 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. Conclusions: 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.

Characterization and Mitigation of a Simultaneous Multi-Slice fMRI Artifact: Multiband Artifact Regression in Simultaneous Slices.

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 non-neural, appearing stronger in neurovasculature than grey 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.

Imaging the Vesicular Acetylcholine Transporter in Schizophrenia: A Positron Emission Tomography Study Using [18F]-VAT.

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.

Medial Prefrontal Cortex Dysfunction Mediates Working Memory Deficits in Patients With Schizophrenia.

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.

Illness Phase as a Key Assessment and Intervention Window for Psychosis.

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.

Deep rTMS of the insula and prefrontal cortex in smokers with schizophrenia: Proof-of-concept study.

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.

Opioid antagonists are associated with a reduction in the symptoms of schizophrenia: a meta-analysis of controlled trials.

Current treatments for the symptoms of schizophrenia are only effective for positive symptoms in some individuals, and have considerable side effects that impact compliance. Thus, there is a need to investigate the efficacy of other compounds in treating both positive and negative symptoms. We conducted a meta-analysis of English language placebo-controlled clinical trials of naloxone, naltrexone, nalmefene, and buprenorphine in patients with schizophrenia to determine whether opioid antagonists have therapeutic efficacy on positive, negative, total, or general symptoms. We searched online databases Ovid Medline and PsychINFO, PubMed, EMBASE, Scopus, Cochrane library/CENTRAL, Web of Science, and Google Scholar from 1970 through February 2019. Following PRISMA guidelines, Hedges g was calculated for each study. Primary study outcomes were the within-subject change on any symptom assessment scale for positive, negative, total, or general symptoms of schizophrenia between active drug and placebo conditions. Thirty studies were included with 434 total patients. We found a significant effect of all drugs on all scales combined with both a standard random effects model: (g = 0.26; P = 0.02; k = 22; CI = 0.03-0.49) and a more inclusive bootstrap model: (g = 0.26; P = 0.0002; k = 30; CI = 0.11-0.51) and a significant effect on total scales with the bootstrap model (g = 0.25288; P = 0.015; k = 19; CI = 0.04-0.35). We also observed a significant effect of all drugs on all positive scales combined with both the random effects (g = 0.33; P = 0.015; k = 17; CI = 0.07-0.60) and bootstrap models (g = 0.32; P < 0.0001; k = 21; CI = 0.13-1.38). This evidence provides support for further testing in randomized clinical trials of a new class of non-D2-receptor drugs, based on opioid mechanisms, for the treatment of positive and negative symptoms of schizophrenia.

Evaluating the consistency and specificity of neuroimaging data using meta-analysis.

Making sense of a neuroimaging literature that is growing in scope and complexity will require increasingly sophisticated tools for synthesizing findings across studies. Meta-analysis of neuroimaging studies fills a unique niche in this process: It can be used to evaluate the consistency of findings across different laboratories and task variants, and it can be used to evaluate the specificity of findings in brain regions or networks to particular task types. This review discusses examples, implementation, and considerations when choosing meta-analytic techniques. It focuses on the multilevel kernel density analysis (MKDA) framework, which has been used in recent studies to evaluate consistency and specificity of regional activation, identify distributed functional networks from patterns of co-activation, and test hypotheses about functional cortical-subcortical pathways in healthy individuals and patients with mental disorders. Several tests of consistency and specificity are described.

Functional neuroimaging of working memory in schizophrenia: task performance as a moderating variable.

Functional neuroimaging studies in schizophrenia have demonstrated abnormal activation of dorsolateral prefrontal cortex (DLPFC) during working memory (WM) performance. However, findings of increased and decreased activity have been reported. The authors used meta-analysis to investigate whether diverging results arise as a function of differential WM task performance between patients and control participants. Results indicate that the magnitude of the group difference in WM performance is a moderator of DLPFC activation differences, and concepts such as hypo- or hyperfrontality do not universally characterize WM findings in schizophrenia. Thus, the variability in the WM activation findings between participants with schizophrenia and control participants reflects the specific conditions under which WM functions are evaluated, not just the WM construct per se.

The impact of atypical antipsychotic medications on long-term memory dysfunction in schizophrenia spectrum disorder: a quantitative review.

This meta-analytic review examines the efficacy of antipsychotic medications in ameliorating schizophrenia-related long-term memory (LTM) impairments. Twenty-three studies were reviewed that compared schizophrenia spectrum patients treated (a) with atypical versus typical antipsychotic medications, or (b) with various atypical treatments. In 17 atypical versus typical trials aggregating 939 participants, superior overall (verbal and nonverbal) LTM was detected in patients assigned to atypical trials. However, this difference was small (effect size estimate (ES) 0.17; 95% Confidence Interval (CI) 0.04 to 0.31) and specific to certain atypical treatments. Relative to typical antipsychotic trials, LTM superiority was marginally significant for risperidone trials (ES 0.20; 95% CI -0.03 to 0.44) and significant for olanzapine trials (ES 0.29; 95% CI 0.08 to 0.49). In contrast, clozapine trials did not produce a LTM advantage over typical trials (ES -0.06; 95% CI -0.35 to 0.23). Due to the lack of available studies, the effect of quetiapine was indeterminate. Direct comparison between atypical trials revealed a similar effect pattern. A marginally significant superiority in overall LTM was detected for risperidone and olanzapine compared to clozapine (ES 0.28; 95% CI -0.04 to 0.59), which reached significance for verbal LTM (ES 0.36; 95% CI 0.04 to 0.67). Finally, the beneficial impact of antipsychotic medications emerged as a function of differences in the anticholinergic properties of the treatment arms being compared.

Motivational Context Modulates Prediction Error Response in Schizophrenia.

BACKGROUND: Recent findings demonstrate that patients with schizophrenia are worse at learning to predict rewards than losses, suggesting that motivational context modulates learning in this disease. However, these findings derive from studies in patients treated with antipsychotic medications, D2 receptor antagonists that may interfere with the neural systems that underlie motivation and learning. Thus, it remains unknown how motivational context affects learning in schizophrenia, separate from the effects of medication. METHODS: To examine the impact of motivational context on learning in schizophrenia, we tested 16 unmedicated patients with schizophrenia and 23 matched controls on a probabilistic learning task while they underwent functional magnetic resonance imaging (fMRI) under 2 conditions: one in which they pursued rewards, and one in which they avoided losses. Computational models were used to derive trial-by-trial prediction error responses to feedback. RESULTS: Patients performed worse than controls on the learning task overall, but there were no behavioral effects of condition. FMRI revealed an attenuated prediction error response in patients in the medial prefrontal cortex, striatum, and medial temporal lobe when learning to predict rewards, but not when learning to avoid losses. CONCLUSIONS: Patients with schizophrenia showed differences in learning-related brain activity when learning to predict rewards, but not when learning to avoid losses. Together with prior work, these results suggest that motivational deficits related to learning in schizophrenia are characteristic of the disease and not solely a result of antipsychotic treatment.

Dopamine-Related Disruption of Functional Topography of Striatal Connections in Unmedicated Patients With Schizophrenia.

IMPORTANCE: Despite the well-established role of striatal dopamine in psychosis, current views generally agree that cortical dysfunction is likely necessary for the emergence of psychotic symptoms. The topographic organization of striatal-cortical connections is central to gating and integration of higher-order information, so a disruption of such topography via dysregulated dopamine could lead to cortical dysfunction in schizophrenia. However, this hypothesis remains to be tested using multivariate methods ascertaining the global pattern of striatal connectivity and without the confounding effects of antidopaminergic medication. OBJECTIVES: To examine whether the pattern of brain connectivity across striatal subregions is abnormal in unmedicated patients with schizophrenia and whether this abnormality relates to psychotic symptoms and extrastriatal dopaminergic transmission. DESIGN, SETTING, AND PARTICIPANTS: In this multimodal, case-control study, we obtained resting-state functional magnetic resonance imaging data from 18 unmedicated patients with schizophrenia and 24 matched healthy controls from the New York State Psychiatric Institute. A subset of these (12 and 17, respectively) underwent positron emission tomography with the dopamine D2 receptor radiotracer carbon 11-labeled FLB457 before and after amphetamine administration. Data were acquired between June 16, 2011, and February 25, 2014. Data analysis was performed from September 1, 2014, to January 11, 2016. MAIN OUTCOMES AND MEASURES: Group differences in the striatal connectivity pattern (assessed via multivariable logistic regression) across striatal subregions, the association between the multivariate striatal connectivity pattern and extrastriatal baseline D2 receptor binding potential and its change after amphetamine administration, and the association between the multivariate connectivity pattern and the severity of positive symptoms evaluated with the Positive and Negative Syndrome Scale. RESULTS: Of the patients with schizophrenia (mean [SEM] age, 35.6 [11.8] years), 9 (50%) were male and 9 (50%) were female. Of the controls (mean [SEM] age, 33.7 [8.8] years), 10 (42%) were male and 14 (58%) were female. Patients had an abnormal pattern of striatal connectivity, which included abnormal caudate connections with a distributed set of associative cortex regions (χ229 = 53.55, P = .004). In patients, more deviation from the multivariate pattern of striatal connectivity found in controls correlated specifically with more severe positive symptoms (ρ = -0.77, P = .002). Striatal connectivity also correlated with baseline binding potential across cortical and extrastriatal subcortical regions (t25 = 3.01, P = .01, Bonferroni corrected) but not with its change after amphetamine administration. CONCLUSIONS AND RELEVANCE: Using a multimodal, circuit-level interrogation of striatal-cortical connections, it was demonstrated that the functional topography of these connections is globally disrupted in unmedicated patients with schizophrenia. These findings suggest that striatal-cortical dysconnectivity may underlie the effects of dopamine dysregulation on the pathophysiologic mechanism of psychotic symptoms.

Mechanisms of Working Memory Impairment in Schizophrenia.

BACKGROUND: The neural correlates of working memory (WM) impairment in schizophrenia remain a key puzzle in understanding the cognitive deficits and dysfunction of dorsolateral prefrontal cortex observed in this disorder. We sought to determine whether patients with schizophrenia exhibit an alteration in the inverted-U relationship between WM load and activation that we recently observed in healthy individuals and whether this could account for WM deficits in this population. METHODS: Medicated (n = 30) and unmedicated (n = 21) patients with schizophrenia and healthy control subjects (n = 45) performed the self-ordered WM task during functional magnetic resonance imaging. We identified regions exhibiting an altered fit to an inverted-U relationship between WM load and activation that were also predictive of WM performance. RESULTS: A blunted inverted-U response was observed in left dorsolateral prefrontal cortex in patients and was associated with behavioral deficits in WM capacity. In addition, suppression of medial prefrontal cortex during WM was reduced in patients and was associated with poorer WM capacity in patients. Finally, activation of visual cortex in the cuneus was elevated in patients and associated with improved WM capacity. Together, these findings explained 55% of the interindividual variance in WM capacity when combined with diagnostic and medication status, which alone accounted for only 22% of the variance in WM capacity. CONCLUSIONS: These findings identify a novel biomarker and putative mechanism of WM deficits in patients with schizophrenia, a reduction or flattening of the inverted-U relationship between activation and WM load observed in healthy individuals in left dorsolateral prefrontal cortex.