Publisher Correction: An integrative framework for perceptual disturbances in psychosis.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

An integrative framework for perceptual disturbances in psychosis.

Perceptual disturbances in psychosis, such as auditory verbal hallucinations, are associated with increased baseline activity in the associative auditory cortex and increased dopamine transmission in the associative striatum. Perceptual disturbances are also associated with perceptual biases that suggest increased reliance on prior expectations. We review theoretical models of perceptual inference and key supporting physiological evidence, as well as the anatomy of associative cortico-striatal loops that may be relevant to auditory perceptual inference. Integrating recent findings, we outline a working framework that bridges neurobiology and the phenomenology of perceptual disturbances via theoretical models of perceptual inference.

Imaging synaptic dopamine availability in individuals at clinical high-risk for psychosis: a [11C]-(+)-PHNO PET with methylphenidate challenge study.

Patients at clinical high-risk (CHR) for psychosis show elevations in [18F]DOPA uptake, an estimate of dopamine (DA) synthesis capacity, in the striatum predictive of conversion to schizophrenia. Intrasynaptic DA levels can be inferred from imaging the change in radiotracer binding at D2 receptors due to a pharmacological challenge. Here, we used methylphenidate, a DA reuptake inhibitor, and [11C]-(+)-PHNO, to measure synaptic DA availability in CHR both in striatal and extra-striatal brain regions. Fourteen unmedicated, nonsubstance using CHR individuals and 14 matched control subjects participated in the study. Subjects underwent two [11C]-(+)-PHNO scans, one at baseline and one following administration of a single oral dose (60 mg) of methylphenidate. [11C]-(+)-PHNO BPND, the binding potential relative to the nondisplaceable compartment, was derived using the simplified reference tissue model with cerebellum as reference tissue. The percent change in BPND between scans, ΔBPND, was computed as an index of synaptic DA availability, and group comparisons were performed with a linear mixed model. An overall trend was found for greater synaptic DA availability (∆BPND) in CHR than controls (p = 0.06). This was driven entirely by ∆BPND in ventral striatum (-34 ± 14% in CHR, -20 ± 12% in HC; p = 0.023). There were no significant group differences in any other brain region. There were no significant differences in DA transmission in any striatal region between converters and nonconverters, although this finding is limited by the small sample size (N = 2). There was a strong and negative correlation between ΔBPND in VST and severity of negative symptoms at baseline in the CHR group (r = -0.66, p < 0.01). We show abnormally increased DA availability in the VST in CHR and an inverse relationship with negative symptoms. Our results suggest a potential early role for mesolimbic dopamine overactivity in CHR. Longitudinal studies are needed to ascertain the significance of the differential topography observed here with the [18F]DOPA literature.

Neuromelanin-sensitive MRI as a noninvasive proxy measure of dopamine function in the human brain.

Neuromelanin-sensitive MRI (NM-MRI) purports to detect the content of neuromelanin (NM), a product of dopamine metabolism that accumulates with age in dopamine neurons of the substantia nigra (SN). Interindividual variability in dopamine function may result in varying levels of NM accumulation in the SN; however, the ability of NM-MRI to measure dopamine function in nonneurodegenerative conditions has not been established. Here, we validated that NM-MRI signal intensity in postmortem midbrain specimens correlated with regional NM concentration even in the absence of neurodegeneration, a prerequisite for its use as a proxy for dopamine function. We then validated a voxelwise NM-MRI approach with sufficient anatomical sensitivity to resolve SN subregions. Using this approach and a multimodal dataset of molecular PET and fMRI data, we further showed the NM-MRI signal was related to both dopamine release in the dorsal striatum and resting blood flow within the SN. These results suggest that NM-MRI signal in the SN is a proxy for function of dopamine neurons in the nigrostriatal pathway. As a proof of concept for its clinical utility, we show that the NM-MRI signal correlated to severity of psychosis in schizophrenia and individuals at risk for schizophrenia, consistent with the well-established dysfunction of the nigrostriatal pathway in psychosis. Our results indicate that noninvasive NM-MRI is a promising tool that could have diverse research and clinical applications to investigate in vivo the role of dopamine in neuropsychiatric illness.

Cross-Scanner Harmonization of Neuromelanin-Sensitive MRI for Multisite Studies.

BACKGROUND: Neuromelanin-sensitive magnetic resonance imaging (NM-MRI) is a validated measure of neuromelanin concentration in the substantia nigra-ventral tegmental area (SN-VTA) complex and is a proxy measure of dopaminergic function with potential as a noninvasive biomarker. The development of generalizable biomarkers requires large-scale samples necessitating harmonization approaches to combine data collected across sites. PURPOSE: To develop a method to harmonize NM-MRI across scanners and sites. STUDY TYPE: Prospective. POPULATION: A total of 128 healthy subjects (18-73 years old; 45% female) from three sites and five MRI scanners. FIELD STRENGTH/SEQUENCE: 3.0 T; NM-MRI two-dimensional gradient-recalled echo with magnetization-transfer pulse and three-dimensional T1-weighted images. ASSESSMENT: NM-MRI contrast (contrast-to-noise ratio [CNR]) maps were calculated and CNR values within the SN-VTA (defined previously by manual tracing on a standardized NM-MRI template) were determined before harmonization (raw CNR) and after ComBat harmonization (harmonized CNR). Scanner differences were assessed by calculating the classification accuracy of a support vector machine (SVM). To assess the effect of harmonization on biological variability, support vector regression (SVR) was used to predict age and the difference in goodness-of-fit (Δr) was calculated as the correlation (between actual and predicted ages) for the harmonized CNR minus the correlation for the raw CNR. STATISTICAL TESTS: Permutation tests were used to determine if SVM classification accuracy was above chance level and if SVR Δr was significant. A P-value <0.05 was considered significant. RESULTS: In the raw CNR, SVM MRI scanner classification was above chance level (accuracy = 86.5%). In the harmonized CNR, the accuracy of the SVM was at chance level (accuracy = 29.5%; P = 0.8542). There was no significant difference in age prediction using the raw or harmonized CNR (Δr = -0.06; P = 0.7304). DATA CONCLUSION: ComBat harmonization removes differences in SN-VTA CNR across scanners while preserving biologically meaningful variability associated with age. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY: 1.

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.

Baseline reward processing and ventrostriatal dopamine function are associated with pramipexole response in depression.

The efficacy of dopamine agonists in treating major depressive disorder has been hypothesized to stem from effects on ventrostriatal dopamine and reward function. However, an important question is whether dopamine agonists are most beneficial for patients with reward-based deficits. This study evaluated whether measures of reward processing and ventrostriatal dopamine function predicted response to the dopamine agonist, pramipexole (ClinicalTrials.gov Identifier: NCT02033369). Individuals with major depressive disorder (n = 26) and healthy controls (n = 26) (mean ± SD age = 26.5 ± 5.9; 50% female) first underwent assessments of reward learning behaviour and ventrostriatal prediction error signalling (measured using functional MRI). 11C-(+)-PHNO PET before and after oral amphetamine was used to assess ventrostriatal dopamine release. The depressed group then received open-label pramipexole treatment for 6 weeks (0.5 mg/day titrated to a maximum daily dose of 2.5 mg). Symptoms were assessed weekly, and reward learning was reassessed post-treatment. At baseline, relative to controls, the depressed group showed lower reward learning (P = 0.02), a trend towards blunted reward-related prediction error signals (P = 0.07), and a trend towards increased amphetamine-induced dopamine release (P = 0.07). Despite symptom improvements following pramipexole (Cohen's d ranging from 0.51 to 2.16 across symptom subscales), reward learning did not change after treatment. At a group level, baseline reward learning (P = 0.001) and prediction error signalling (P = 0.004) were both associated with symptom improvement, albeit in a direction opposite to initial predictions: patients with stronger pretreatment reward learning and reward-related prediction error signalling improved most. Baseline D2/3 receptor availability (P = 0.02) and dopamine release (P = 0.05) also predicted improvements in clinical functioning, with lower D2/3 receptor availability and lower dopamine release predicting greater improvements. Although these findings await replication, they suggest that measures of reward-related mesolimbic dopamine function may hold promise for identifying depressed individuals likely to respond favourably to dopaminergic pharmacotherapy.

Reproducibility assessment of neuromelanin-sensitive magnetic resonance imaging protocols for region-of-interest and voxelwise analyses.

Neuromelanin-sensitive MRI (NM-MRI) provides a noninvasive measure of the content of neuromelanin (NM), a product of dopamine metabolism that accumulates with age in dopamine neurons of the substantia nigra (SN). NM-MRI has been validated as a measure of both dopamine neuron loss, with applications in neurodegenerative disease, and dopamine function, with applications in psychiatric disease. Furthermore, a voxelwise-analysis approach has been validated to resolve substructures, such as the ventral tegmental area (VTA), within midbrain dopaminergic nuclei thought to have distinct anatomical targets and functional roles. NM-MRI is thus a promising tool that could have diverse research and clinical applications to noninvasively interrogate in vivo the dopamine system in neuropsychiatric illness. Although a test-retest reliability study by Langley et al. using the standard NM-MRI protocol recently reported high reliability, a systematic and comprehensive investigation of the performance of the method for various acquisition parameters and preprocessing methods has not been conducted. In particular, most previous studies used relatively thick MRI slices (~3 â€‹mm), compared to the typical in-plane resolution (~0.5 â€‹mm) and to the height of the SN (~15 â€‹mm), to overcome technical limitations such as specific absorption rate and signal-to-noise ratio, at the cost of partial-volume effects. Here, we evaluated the effect of various acquisition and preprocessing parameters on the strength and test-retest reliability of the NM-MRI signal to determine optimized protocols for both region-of-interest (including whole SN-VTA complex and atlas-defined dopaminergic nuclei) and voxelwise measures. Namely, we determined a combination of parameters that optimizes the strength and reliability of the NM-MRI signal, including acquisition time, slice-thickness, spatial-normalization software, and degree of spatial smoothing. Using a newly developed, detailed acquisition protocol, across two scans separated by 13 days on average, we obtained intra-class correlation values indicating excellent reliability and high contrast, which could be achieved with a different set of parameters depending on the measures of interest and experimental constraints such as acquisition time. Based on this, we provide detailed guidelines covering acquisition through analysis and recommendations for performing NM-MRI experiments with high quality and reproducibility. This work provides a foundation for the optimization and standardization of NM-MRI, a promising MRI approach with growing applications throughout clinical and basic neuroscience.

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.

Deficits in predictive coding underlie hallucinations in schizophrenia.

The neural mechanisms that produce hallucinations and other psychotic symptoms remain unclear. Previous research suggests that deficits in predictive signals for learning, such as prediction error signals, may underlie psychotic symptoms, but the mechanism by which such deficits produce psychotic symptoms remains to be established. We used model-based fMRI to study sensory prediction errors in human patients with schizophrenia who report daily auditory verbal hallucinations (AVHs) and sociodemographically matched healthy control subjects. We manipulated participants' expectations for hearing speech at different periods within a speech decision-making task. Patients activated a voice-sensitive region of the auditory cortex while they experienced AVHs in the scanner and displayed a concomitant deficit in prediction error signals in a similar portion of auditory cortex. This prediction error deficit correlated strongly with increased activity during silence and with reduced volumes of the auditory cortex, two established neural phenotypes of AVHs. Furthermore, patients with more severe AVHs had more deficient prediction error signals and greater activity during silence within the region of auditory cortex where groups differed, regardless of the severity of psychotic symptoms other than AVHs. Our findings suggest that deficient predictive coding accounts for the resting hyperactivity in sensory cortex that leads to hallucinations.

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.

Alterations in cortical and extrastriatal subcortical dopamine function in schizophrenia: systematic review and meta-analysis of imaging studies.

BACKGROUND: The hypothesis that cortical dopaminergic alterations underlie aspects of schizophrenia has been highly influential. AIMS: To bring together and evaluate the imaging evidence for dopaminergic alterations in cortical and other extrastriatal regions in schizophrenia. METHOD: Electronic databases were searched for in vivo molecular studies of extrastriatal dopaminergic function in schizophrenia. Twenty-three studies (278 patients and 265 controls) were identified. Clinicodemographic and imaging variables were extracted and effect sizes determined for the dopaminergic measures. There were sufficient data to permit meta-analyses for the temporal cortex, thalamus and substantia nigra but not for other regions. RESULTS: The meta-analysis of dopamine D2/D3 receptor availability found summary effect sizes of d = -0.32 (95% CI -0.68 to 0.03) for the thalamus, d = -0.23 (95% CI -0.54 to 0.07) for the temporal cortex and d = 0.04 (95% CI -0.92 to 0.99) for the substantia nigra. Confidence intervals were wide and all included no difference between groups. Evidence for other measures/regions is limited because of the small number of studies and in some instances inconsistent findings, although significant differences were reported for D2/D3 receptors in the cingulate and uncus, for D1 receptors in the prefrontal cortex and for dopamine transporter availability in the thalamus. CONCLUSIONS: There is a relative paucity of direct evidence for cortical dopaminergic alterations in schizophrenia, and findings are inconclusive. This is surprising given the wide influence of the hypothesis. Large, well-controlled studies in drug-naive patients are warranted to definitively test this hypothesis.

Association of Neuromelanin-Sensitive MRI Signal With Lifetime Substance Use in Young Women.

OBJECTIVE: Midbrain dopamine function plays a key role in translational models of substance use disorders. Whether midbrain dopamine function is associated with substance use frequency and severity or reward function in 20-24 year-olds remains a critical gap in knowledge. The authors collected neuromelanin-sensitive magnetic resonance imaging (NM-MRI), a validated index of lifetime dopamine function in the substantia nigra/ventral tegmentum area (SN-VTA) complex, to characterize altered dopamine function. METHOD: Midbrain NM-MRI contrast-to-noise ratio (CNR) was acquired in 135 20-24 year-olds (105 women and 30 men). A composite measure of cumulative substance use was derived from factor analysis of lifetime alcohol intoxications, lifetime cannabis use, use of nicotine in heaviest month, number of classes of drugs used, and ever meeting DSM-5 criteria for a SUD. Trait reward function was assessed by self-report. RESULTS: Cumulative substance use was significantly positively associated with NM-MRI CNR in a large area of the bilateral SN-VTA complex, an effect which was driven by women (who comprised most of the sample) and by voxels with greater NM-MRI CNR, including the ventral tegmentum area. NM-MRI CNR was not associated with individual differences in trait reward function. CONCLUSIONS: History of substance use is associated with greater NM signal in NM-rich areas of the midbrain, especially in women. Future longitudinal studies with repeated NM-MRI assessments, especially in younger cohorts and while including more men, are warranted to evaluate whether aberrant dopamine function predates, follows, or is modulated by substance use.

Neural Correlates of Metacognition Impairment in Opioid Addiction.

BACKGROUND: Individuals with substance use disorder show impaired self-awareness of ongoing behavior. This deficit suggests problems with metacognition, which has been operationalized in the cognitive neuroscience literature as the ability to monitor and evaluate the success of one's own cognition and behavior. However, the neural mechanisms of metacognition have not been characterized in a population with drug addiction. METHODS: Community samples of participants with opioid use disorder (OUD) (n = 27) and healthy control participants (n = 29) performed a previously validated functional magnetic resonance imaging metacognition task (perceptual decision-making task along with confidence ratings of performance). Measures of recent drug use and addiction severity were also acquired. RESULTS: Individuals with OUD had lower metacognitive sensitivity (i.e., disconnection between task performance and task-related confidence) than control individuals. Trial-by-trial analyses showed that this overall group difference was driven by (suboptimally) low confidence in participants with OUD during correct trials. In functional magnetic resonance imaging analyses, the task engaged an expected network of brain regions (e.g., rostrolateral prefrontal cortex and dorsal anterior cingulate/supplementary motor area, both previously linked to metacognition); group differences emerged in a large ventral anterior cluster that included the medial and lateral orbitofrontal cortex and striatum (higher activation in OUD). Trial-by-trial functional magnetic resonance imaging analyses showed group differences in rostrolateral prefrontal cortex activation, which further correlated with metacognitive behavior across all participants. Exploratory analyses suggested that the behavioral and neural group differences were exacerbated by recent illicit opioid use and unexplained by general cognition. CONCLUSIONS: With confirmation and extension of these findings, metacognition and its associated neural circuits could become new, promising therapeutic targets in addiction.

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.

Functional Localization of the Human Auditory and Visual Thalamus Using a Thalamic Localizer Functional Magnetic Resonance Imaging Task.

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 benchmark 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.

Kappa opioid receptor availability predicts severity of anhedonia in schizophrenia.

The kappa opioid receptor (KOR) and its endogenous agonist dynorphin have been implicated in multiple psychiatric conditions including psychotic disorders. We tested the hypotheses that kappa expression is elevated and associated with psychotic symptoms in schizophrenia. We measured kappa expression in unmedicated patients with schizophrenia (7 female, 6 male) and matched controls (7 female, 6 male) with positron emission tomography (PET). We also acquired a measurement of cumulative dopamine activity over the life span in the same subjects using neuromelanin sensitive MRI. We hypothesized that neuromelanin accumulation would be higher in patients than controls and that in patients there would be a positive association between KOR availability and neuromelanin accumulation. Fourteen patients and thirteen controls were enrolled. Whole brain dynamic PET imaging data using the KOR selective tracer [18F]LY245998 were acquired. Distribution volume (VT) was measured with region of interest analysis in 14 brain regions. Neuromelanin accumulation in midbrain dopaminergic nuclei was assessed in the same subjects. Positive and negative symptoms were measured by a clinical psychologist. We did not observe group level differences in KOR expression, neuromelanin accumulation or relationships of these to positive symptoms. Unexpectedly, we did observe strong positive associations between KOR expression and symptoms of anhedonia in the patients (Pearson r > 0.7, uncorrected p < 0.01 in 8 cortical brain regions). We also observed moderate associations between KOR expression and neuromelanin levels in patients. In conclusion, we did not observe a relationship between kappa and symptoms of psychosis but the observed relationship to the negative symptom of anhedonia is in line with recent work testing kappa antagonism as a therapy for anhedonia in depression.

Reporting checklists in neuroimaging: promoting transparency, replicability, and reproducibility.

Neuroimaging plays a crucial role in understanding brain structure and function, but the lack of transparency, reproducibility, and reliability of findings is a significant obstacle for the field. To address these challenges, there are ongoing efforts to develop reporting checklists for neuroimaging studies to improve the reporting of fundamental aspects of study design and execution. In this review, we first define what we mean by a neuroimaging reporting checklist and then discuss how a reporting checklist can be developed and implemented. We consider the core values that should inform checklist design, including transparency, repeatability, data sharing, diversity, and supporting innovations. We then share experiences with currently available neuroimaging checklists. We review the motivation for creating checklists and whether checklists achieve their intended objectives, before proposing a development cycle for neuroimaging reporting checklists and describing each implementation step. We emphasize the importance of reporting checklists in enhancing the quality of data repositories and consortia, how they can support education and best practices, and how emerging computational methods, like artificial intelligence, can help checklist development and adherence. We also highlight the role that funding agencies and global collaborations can play in supporting the adoption of neuroimaging reporting checklists. We hope this review will encourage better adherence to available checklists and promote the development of new ones, and ultimately increase the quality, transparency, and reproducibility of neuroimaging research.

Candidate biomarkers in psychiatric disorders: state of the field.

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