Altered Hierarchical Gradients of Intrinsic Neural Timescales in Mild Cognitive Impairment and Alzheimer's Disease.

Alzheimer's disease (AD) is a devastating neurodegenerative disease that affects millions of seniors in the United States. Resting-state functional magnetic resonance imaging (rs-fMRI) is widely used to study neurophysiology in AD and its prodromal condition, mild cognitive impairment (MCI). The intrinsic neural timescale (INT), which can be estimated through the magnitude of the autocorrelation of neural signals from rs-fMRI, is thought to quantify the duration that neural information is stored in a local circuit. Such heterogeneity of the timescales forms a basis of the brain functional hierarchy and captures an aspect of circuit dynamics relevant to excitation/inhibition balance, which is broadly relevant for cognitive functions. Given that, we applied rs-fMRI to test whether distinct changes of INT at different hierarchies are present in people with MCI, those progressing to AD (called Converter), and AD patients of both sexes. Linear mixed-effect model was implemented to detect altered hierarchical gradients across populations followed by pairwise comparisons to identify regional differences. High similarities between AD and Converter were observed. Specifically, the inferior temporal, caudate, and pallidum areas exhibit significant alterations in both AD and Converter. Distinct INT-related pathological changes in MCI and AD were found. For AD/Converter, neural information is stored for a longer time in lower hierarchical areas, while higher levels of hierarchy seem to be preferentially impaired in MCI leading to a less pronounced hierarchical gradient. These results inform that the INT holds great potential as an additional measure for AD prediction, even a stable biomarker for clinical diagnosis.

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.

Replication of a neuroimaging biomarker for striatal dysfunction in psychosis.

To bring biomarkers closer to clinical application, they should be generalizable, reliable, and maintain performance within the constraints of routine clinical conditions. The functional striatal abnormalities (FSA), is among the most advanced neuroimaging biomarkers in schizophrenia, trained to discriminate diagnosis, with post-hoc analyses indicating prognostic properties. Here, we attempt to replicate its diagnostic capabilities measured by the area under the curve (AUC) in receiver operator characteristic curves discriminating individuals with psychosis (n = 101) from healthy controls (n = 51) in the Human Connectome Project for Early Psychosis. We also measured the test-retest (run 1 vs 2) and phase encoding direction (i.e., AP vs PA) reliability with intraclass correlation coefficients (ICC). Additionally, we measured effects of scan length on classification accuracy (i.e., AUCs) and reliability (i.e., ICCs). Finally, we tested the prognostic capability of the FSA by the correlation between baseline scores and symptom improvement over 12 weeks of antipsychotic treatment in a separate cohort (n = 97). Similar analyses were conducted for the Yeo networks intrinsic connectivity as a reference. The FSA had good/excellent diagnostic discrimination (AUC = 75.4%, 95% CI = 67.0-83.3%; in non-affective psychosis AUC = 80.5%, 95% CI = 72.1-88.0%, and in affective psychosis AUC = 58.7%, 95% CI = 44.2-72.0%). Test-retest reliability ranged between ICC = 0.48 (95% CI = 0.35-0.59) and ICC = 0.22 (95% CI = 0.06-0.36), which was comparable to that of networks intrinsic connectivity. Phase encoding direction reliability for the FSA was ICC = 0.51 (95% CI = 0.42-0.59), generally lower than for networks intrinsic connectivity. By increasing scan length from 2 to 10 min, diagnostic classification of the FSA increased from AUC = 71.7% (95% CI = 63.1-80.3%) to 75.4% (95% CI = 67.0-83.3%) and phase encoding direction reliability from ICC = 0.29 (95% CI = 0.14-0.43) to ICC = 0.51 (95% CI = 0.42-0.59). FSA scores did not correlate with symptom improvement. These results reassure that the FSA is a generalizable diagnostic - but not prognostic - biomarker. Given the replicable results of the FSA as a diagnostic biomarker trained on case-control datasets, next the development of prognostic biomarkers should be on treatment-response data.

Effects of Hallucination Proneness and Sensory Resolution on Prior Biases in Human Perceptual Inference of Time Intervals.

Bayesian models of perception posit that percepts result from the optimal integration of new sensory information and prior expectations. In turn, prominent models of perceptual disturbances in psychosis frame hallucination-like phenomena as percepts excessively biased toward perceptual prior expectations. Despite mounting support for this notion, whether this hallucination-related prior bias results secondarily from imprecise sensory representations at early processing stages or directly from alterations in perceptual priors-both suggested candidates potentially consistent with Bayesian models-remains to be tested. Using modified interval timing paradigms designed to arbitrate between these alternative hypotheses, we show in human participants (16 females and 24 males) from a nonclinical population that hallucination proneness correlates with a circumscribed form of prior bias that reflects selective differences in weighting of contextual prior variance, a prior bias that is unrelated to the effect of sensory noise and to a separate index of sensory resolution. Our results thus suggest distinct mechanisms underlying prior biases in perceptual inference and favor the notion that hallucination proneness could reflect direct alterations in the representation or use of perceptual priors independent of sensory noise.SIGNIFICANCE STATEMENT Current theories of psychosis posit that hallucination proneness results from excessive influence of prior expectations on perception. It is not clear whether this prior bias represents a primary top-down process related to the representation or use of prior beliefs or instead a secondary bottom-up process stemming from imprecise sensory representations at early processing stages. To address this question, we examined interval timing behaviors captured by Bayesian perceptual-inference models. Our data support the notion that excessive influence of prior expectations associated with hallucination propensity is not directly secondary to sensory imprecision and is instead more consistent with a primary top-down process. These results help refine computational theories of psychosis and may contribute to the development of improved intervention targets.

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.

Probing midbrain dopamine function in pediatric obsessive-compulsive disorder via neuromelanin-sensitive magnetic resonance imaging.

Obsessive-compulsive disorder (OCD) is an impairing psychiatric condition, which often onsets in childhood. Growing research highlights dopaminergic alterations in adult OCD, yet pediatric studies are limited by methodological constraints. This is the first study to utilize neuromelanin-sensitive MRI as a proxy for dopaminergic function among children with OCD. N = 135 youth (6-14-year-olds) completed high-resolution neuromelanin-sensitive MRI across two sites; n = 64 had an OCD diagnosis. N = 47 children with OCD completed a second scan after cognitive-behavioral therapy. Voxel-wise analyses identified that neuromelanin-MRI signal was higher among children with OCD compared to those without (483 voxels, permutation-corrected p = 0.018). Effects were significant within both the substania nigra pars compacta (p = 0.004, Cohen's d = 0.51) and ventral tegmental area (p = 0.006, d = 0.50). Follow-up analyses indicated that more severe lifetime symptoms (t = -2.72, p = 0.009) and longer illness duration (t = -2.22, p = 0.03) related to lower neuromelanin-MRI signal. Despite significant symptom reduction with therapy (p < 0.001, d = 1.44), neither baseline nor change in neuromelanin-MRI signal associated with symptom improvement. Current results provide the first demonstration of the utility of neuromelanin-MRI in pediatric psychiatry, specifically highlighting in vivo evidence for midbrain dopamine alterations in treatment-seeking youth with OCD. Neuromelanin-MRI likely indexes accumulating alterations over time, herein, implicating dopamine hyperactivity in OCD. Given evidence of increased neuromelanin signal in pediatric OCD but negative association with symptom severity, additional work is needed to parse potential longitudinal or compensatory mechanisms. Future studies should explore the utility of neuromelanin-MRI biomarkers to identify early risk prior to onset, parse OCD subtypes or symptom heterogeneity, and explore prediction of pharmacotherapy response.

Normative Values of Neuromelanin-Sensitive MRI Signal in Older Adults Obtained Using a Turbo Spin Echo Sequence.

BACKGROUND: The integrity and function of catecholamine neurotransmitter systems can be assessed using neuromelanin-sensitive MRI (NM-MRI). The relevance of this method to neurodegenerative and psychiatric disorders is becoming increasingly evident, and it has potential as a clinical biomarker. PURPOSE: To support future application of NM-MRI as a clinical biomarker by defining the normative range of NM-MRI signal and volume metrics in cognitively normal older adults. STUDY TYPE: Prospective. POPULATION: A total of 152 cognitively normal older adults aged 53-86 years old, including 41 participants who had follow-up NM-MRI data collected 9-16 months later. FIELD STRENGTH/SEQUENCE: A 3.0 T; NM-MRI turbo spin echo and T1-weighted magnetization-prepared rapid acquisition with gradient echo sequences. ASSESSMENT: NM-MRI images were processed to yield summary measures of volume and signal (contrast-to-noise ratio, CNR) for the substantia nigra (SN) and locus coeruleus (LC) using a recently developed software employing a fully automated algorithm. Change in these metrics over time was also assessed. STATISTICAL TESTS: Mean and standard deviation of NM-MRI metrics were calculated; change over time was tested for significance using 1-sample t-tests. P values < 0.05 were considered statistically significant. RESULTS: At baseline SN signal (CNR) was 10.02% (left) and 10.28% (right) and LC signal was 24.71% (left) and 20.42% (right). Baseline SN volume was 576 mm3 (left) and 540 mm3 (right) and LC volume was 6.31 mm3 (left) and 6.30 mm3 (right). The only NM-MRI metric showing significant change was a decrease in left SN volume (t40  = -2.57, P = 0.014). DATA CONCLUSION: We report normative values for NM-MRI signal and volume in the SN and LC of cognitively normal older adults and explore their change over time. These values may help future efforts to use NM-MRI as a clinical biomarker by facilitating identification of patients with extreme NM-MRI values. TECHNICAL EFFICACY STAGE: 1.

Influences of dopaminergic system dysfunction on late-life depression.

Deficits in cognition, reward processing, and motor function are clinical features relevant to both aging and depression. Individuals with late-life depression often show impairment across these domains, all of which are moderated by the functioning of dopaminergic circuits. As dopaminergic function declines with normal aging and increased inflammatory burden, the role of dopamine may be particularly salient for late-life depression. We review the literature examining the role of dopamine in the pathogenesis of depression, as well as how dopamine function changes with aging and is influenced by inflammation. Applying a Research Domain Criteria (RDoC) Initiative perspective, we then review work examining how dopaminergic signaling affects these domains, specifically focusing on Cognitive, Positive Valence, and Sensorimotor Systems. We propose a unified model incorporating the effects of aging and low-grade inflammation on dopaminergic functioning, with a resulting negative effect on cognition, reward processing, and motor function. Interplay between these systems may influence development of a depressive phenotype, with an initial deficit in one domain reinforcing decline in others. This model extends RDoC concepts into late-life depression while also providing opportunities for novel and personalized interventions.

The effects of base rate neglect on sequential belief updating and real-world beliefs.

Base-rate neglect is a pervasive bias in judgment that is conceptualized as underweighting of prior information and can have serious consequences in real-world scenarios. This bias is thought to reflect variability in inferential processes but empirical support for a cohesive theory of base-rate neglect with sufficient explanatory power to account for longer-term and real-world beliefs is lacking. A Bayesian formalization of base-rate neglect in the context of sequential belief updating predicts that belief trajectories should exhibit dynamic patterns of dependence on the order in which evidence is presented and its consistency with prior beliefs. To test this, we developed a novel 'urn-and-beads' task that systematically manipulated the order of colored bead sequences and elicited beliefs via an incentive-compatible procedure. Our results in two independent online studies confirmed the predictions of the sequential base-rate neglect model: people exhibited beliefs that are more influenced by recent evidence and by evidence inconsistent with prior beliefs. We further found support for a noisy-sampling inference model whereby base-rate neglect results from rational discounting of noisy internal representations of prior beliefs. Finally, we found that model-derived indices of base-rate neglect-including noisier prior representation-correlated with propensity for unusual beliefs outside the laboratory. Our work supports the relevance of Bayesian accounts of sequential base-rate neglect to real-world beliefs and hints at strategies to minimize deleterious consequences of this pervasive bias.

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.

Reliability and Reproducibility of Neuromelanin-Sensitive Imaging of the Substantia Nigra: A Comparison of Three Different Sequences.

BACKGROUND: Neuromelanin-sensitive MRI (NM-MRI) of the substantia nigra provides a noninvasive way to acquire an indirect measure of dopamine functioning. Despite the potential of NM-MRI as a candidate biomarker for dopaminergic pathology, studies about its reproducibility are sparse. PURPOSE: To assess the test-retest reproducibility of three commonly used NM-MRI sequences and evaluate three analysis methods. STUDY TYPE: Prospective study. POPULATION: A total of 11 healthy participants age between 20-27 years. FIELD STRENGTH/SEQUENCE: 3.0T; NM-MRI gradient recalled echo (GRE) with magnetization transfer (MT) pulse; NM-MRI turbo spin echo (TSE) with MT pulse; NM-MRI TSE without MT pulse. ASSESSMENT: Participants were scanned twice with a 3-week interval. Manual analysis, threshold analysis, and voxelwise analysis were performed for volume and contrast ratio (CR) measurements. STATISTICAL TESTS: Intraclass correlation coefficients (ICCs) were calculated for test-retest and inter- and intrarater variability. RESULTS: The GRE sequence achieved the highest contrast and lowest variability (4.9-5.7%) and showed substantial to almost perfect test-retest ICC (0.72-0.90) for CR measurements. For volume measurements, the manual analysis showed a higher variability (10.7-17.9%) and scored lower test-retest ICCs (-0.13-0.73) than the other analysis methods. The threshold analysis showed higher test-retest ICC (0.77) than the manual analysis for the volume measurements. DATA CONCLUSION: NM-MRI is a highly reproducible measure, especially when using the GRE sequence and CR measurements. Volume measurements appear to be more sensitive to inter/intrarater variability and variability in placement and orientation of the NM-MRI slab. The threshold analysis appears to be the best alternative for volume analysis. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY STAGE: 1.

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.

A distinct inferential mechanism for delusions in schizophrenia.

Delusions, a core symptom of psychosis, are false beliefs that are rigidly held with strong conviction despite contradictory evidence. Alterations in inferential processes have long been proposed to underlie delusional pathology, but previous attempts to show this have failed to yield compelling evidence for a specific relationship between inferential abnormalities and delusional severity in schizophrenia. Using a novel, incentivized information-sampling task (a modified version of the beads task), alongside well-characterized decision-making tasks, we sought a mechanistic understanding of delusions in a sample of medicated and unmedicated patients with schizophrenia who exhibited a wide range of delusion severity. In this novel task, participants chose whether to draw beads from one of two hidden jars or to guess the identity of the hidden jar, in order to minimize financial loss from a monetary endowment, and concurrently reported their probability estimates for the hidden jar. We found that patients with higher delusion severity exhibited increased information seeking (i.e. increased draws-to-decision behaviour). This increase was highly specific to delusion severity as compared to the severity of other psychotic symptoms, working-memory capacity, and other clinical and socio-demographic characteristics. Delusion-related increases in information seeking were present in unmedicated patients, indicating that they were unlikely due to antipsychotic medication. In addition, after adjusting for delusion severity, patients as a whole exhibited decreased information seeking relative to healthy individuals, a decrease that correlated with lower socioeconomic status. Computational analyses of reported probability estimates further showed that more delusional patients exhibited abnormal belief updating characterized by stronger reliance on prior beliefs formed early in the inferential process, a feature that correlated with increased information seeking in patients. Other decision-making parameters that could have theoretically explained the delusion effects, such as those related to subjective valuation, were uncorrelated with both delusional severity and information seeking among the patients. In turn, we found some preliminary evidence that subjective valuation (rather than belief updating) may explain group differences in information seeking unrelated to delusions. Together, these results suggest that abnormalities in belief updating, characterized by stronger reliance on prior beliefs formed by incorporating information presented earlier in the inferential process, may be a core computational mechanism of delusional ideation in psychosis. Our results thus provide direct empirical support for an inferential mechanism that naturally captures the characteristic rigidity associated with delusional beliefs.

A predictive model for conversion to psychosis in clinical high-risk patients.

BACKGROUND: The authors developed a practical and clinically useful model to predict the risk of psychosis that utilizes clinical characteristics empirically demonstrated to be strong predictors of conversion to psychosis in clinical high-risk (CHR) individuals. The model is based upon the Structured Interview for Psychosis Risk Syndromes (SIPS) and accompanying clinical interview, and yields scores indicating one's risk of conversion. METHODS: Baseline data, including demographic and clinical characteristics measured by the SIPS, were obtained on 199 CHR individuals seeking evaluation in the early detection and intervention for mental disorders program at the New York State Psychiatric Institute at Columbia University Medical Center. Each patient was followed for up to 2 years or until they developed a syndromal DSM-4 disorder. A LASSO logistic fitting procedure was used to construct a model for conversion specifically to a psychotic disorder. RESULTS: At 2 years, 64 patients (32.2%) converted to a psychotic disorder. The top five variables with relatively large standardized effect sizes included SIPS subscales of visual perceptual abnormalities, dysphoric mood, unusual thought content, disorganized communication, and violent ideation. The concordance index (c-index) was 0.73, indicating a moderately strong ability to discriminate between converters and non-converters. CONCLUSIONS: The prediction model performed well in classifying converters and non-converters and revealed SIPS measures that are relatively strong predictors of conversion, comparable with the risk calculator published by NAPLS (c-index = 0.71), but requiring only a structured clinical interview. Future work will seek to externally validate the model and enhance its performance with the incorporation of relevant biomarkers.

Abnormal fronto-striatal activation as a marker of threshold and subthreshold Bulimia Nervosa.

This study aimed to determine whether functional disturbances in fronto-striatal control circuits characterize adolescents with Bulimia Nervosa (BN) spectrum eating disorders regardless of clinical severity. FMRI was used to assess conflict-related brain activations during performance of a Simon task in two samples of adolescents with BN symptoms compared with healthy adolescents. The BN samples differed in the severity of their clinical presentation, illness duration and age. Multi-voxel pattern analyses (MVPAs) based on machine learning were used to determine whether patterns of fronto-striatal activation characterized adolescents with BN spectrum disorders regardless of clinical severity, and whether accurate classification of less symptomatic adolescents (subthreshold BN; SBN) could be achieved based on patterns of activation in adolescents who met DSM5 criteria for BN. MVPA classification analyses revealed that both BN and SBN adolescents could be accurately discriminated from healthy adolescents based on fronto-striatal activation. Notably, the patterns detected in more severely ill BN compared with healthy adolescents accurately discriminated less symptomatic SBN from healthy adolescents. Deficient activation of fronto-striatal circuits can characterize BN early in its course, when clinical presentations are less severe, perhaps pointing to circuit-based disturbances as useful biomarker or risk factor for the disorder, and a tool for understanding its developmental trajectory, as well as the development of early interventions.

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.

Changes in corticostriatal connectivity during reinforcement learning in humans.

Many computational models assume that reinforcement learning relies on changes in synaptic efficacy between cortical regions representing stimuli and striatal regions involved in response selection, but this assumption has thus far lacked empirical support in humans. We recorded hemodynamic signals with fMRI while participants navigated a virtual maze to find hidden rewards. We fitted a reinforcement-learning algorithm to participants' choice behavior and evaluated the neural activity and the changes in functional connectivity related to trial-by-trial learning variables. Activity in the posterior putamen during choice periods increased progressively during learning. Furthermore, the functional connections between the sensorimotor cortex and the posterior putamen strengthened progressively as participants learned the task. These changes in corticostriatal connectivity differentiated participants who learned the task from those who did not. These findings provide a direct link between changes in corticostriatal connectivity and learning, thereby supporting a central assumption common to several computational models of reinforcement learning.

Annual research review: Current limitations and future directions in MRI studies of child- and adult-onset developmental psychopathologies.

BACKGROUND: The widespread use of Magnetic Resonance Imaging (MRI) in the study of child- and adult-onset developmental psychopathologies has generated many investigations that have measured brain structure and function in vivo throughout development, often generating great excitement over our ability to visualize the living, developing brain using the attractive, even seductive images that these studies produce. Often lost in this excitement is the recognition that brain imaging generally, and MRI in particular, is simply a technology, one that does not fundamentally differ from any other technology, be it a blood test, a genotyping assay, a biochemical assay, or behavioral test. No technology alone can generate valid scientific findings. Rather, it is only technology coupled with a strong experimental design that can generate valid and reproducible findings that lead to new insights into the mechanisms of disease and therapeutic response. METHODS: In this review we discuss selected studies to illustrate the most common and important limitations of MRI study designs as most commonly implemented thus far, as well as the misunderstanding that the interpretations of findings from those studies can create for our theories of developmental psychopathologies. RESULTS: Common limitations of MRI study designs are in large part responsible thus far for the generally poor reproducibility of findings across studies, poor generalizability to the larger population, failure to identify developmental trajectories, inability to distinguish causes from effects of illness, and poor ability to infer causal mechanisms in most MRI studies of developmental psychopathologies. For each of these limitations in study design and the difficulties they entail for the interpretation of findings, we discuss various approaches that numerous laboratories are now taking to address those difficulties, which have in common the yoking of brain imaging technologies to studies with inherently stronger designs that permit more valid and more powerful causal inferences. Those study designs include epidemiological, longitudinal, high-risk, clinical trials, and multimodal imaging studies. CONCLUSIONS: We highlight several studies that have yoked brain imaging technologies to these stronger designs to illustrate how doing so can aid our understanding of disease mechanisms and in the foreseeable future can improve clinical diagnosis, prevention, and treatment planning for developmental psychopathologies.