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.

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.

Neuromelanin-Sensitive MRI as Candidate Marker for Treatment Resistance in First-Episode Schizophrenia.

OBJECTIVE: Markers for treatment resistance in schizophrenia are needed to reduce delays in effective treatment. Nigrostriatal hyperdopaminergic function plays a critical role in the pathology of schizophrenia, yet antipsychotic nonresponders do not show increased dopamine function. Neuromelanin-sensitive MRI (NM-MRI), which indirectly measures dopamine function in the substantia nigra, has potential as a noninvasive marker for nonresponders. Increased NM-MRI signal has been shown in psychosis, but has not yet been assessed in nonresponders. In this study, the authors investigated whether nonresponders show lower NM-MRI signal than responders. METHODS: NM-MRI scans were acquired in 79 patients with first-episode psychosis and 20 matched healthy control subjects. Treatment response was assessed at a 6-month follow-up. An a priori voxel-wise analysis within the substantia nigra tested the relation between NM-MRI signal and treatment response in patients. RESULTS: Fifteen patients were classified as nonresponders and 47 patients as responders. Seventeen patients were excluded, primarily because of medication nonadherence or change in diagnosis. Voxel-wise analysis revealed 297 significant voxels in the ventral tier of the substantia nigra that were negatively associated with treatment response. Nonresponders and healthy control subjects had significantly lower NM-MRI signal than responders. Receiver operating characteristic curve analysis showed that NM-MRI signal separated nonresponders with areas under the curve between 0.62 and 0.85. In addition, NM-MRI signal in patients did not change over 6 months. CONCLUSIONS: These findings provide further evidence for dopaminergic differences between medication responders and nonresponders and support the potential of NM-MRI as a clinically applicable marker for treatment resistance in schizophrenia.

The parieto-occipital cortex is a candidate neural substrate for the human ability to approximate Bayesian inference.

Adaptive decision-making often requires one to infer unobservable states based on incomplete information. Bayesian logic prescribes that individuals should do so by estimating the posterior probability by integrating the prior probability with new information, but the neural basis of this integration is incompletely understood. We record fMRI during a task in which participants infer the posterior probability of a hidden state while we independently modulate the prior probability and likelihood of evidence regarding the state; the task incentivizes participants to make accurate inferences and dissociates expected value from posterior probability. Here we show that activation in a region of left parieto-occipital cortex independently tracks the subjective posterior probability, combining its subcomponents of prior probability and evidence likelihood, and reflecting the individual participants' systematic deviations from objective probabilities. The parieto-occipital cortex is thus a candidate neural substrate for humans' ability to approximate Bayesian inference by integrating prior beliefs with new information.

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.

Hallucination-Proneness is Associated With a Decrease in Robust Averaging of Perceptual Evidence.

BACKGROUND AND HYPOTHESIS: Hallucinations are characterized by disturbances in perceptual decision-making about environmental stimuli. When integrating across multiple stimuli to form a perceptual decision, typical observers engage in "robust averaging" by down-weighting extreme perceptual evidence, akin to a statistician excluding outlying data. Furthermore, observers adapt to contexts with more unreliable evidence by increasing this down-weighting strategy. Here, we test the hypothesis that hallucination-prone individuals (n = 38 high vs n = 91 low) would show a decrease in this robust averaging and diminished sensitivity to changes in evidence variance. STUDY DESIGN: We used a multielement perceptual averaging task to elicit dichotomous judgments about the "average color" (red/blue) of an array of stimuli in trials with varied strength (mean) and reliability (variance) of decision-relevant perceptual evidence. We fitted computational models to task behavior, with a focus on a log-posterior-ratio (LPR) model which integrates evidence as a function of the log odds of each perceptual option and produces a robust averaging effect. STUDY RESULTS: Hallucination-prone individuals demonstrated less robust averaging, seeming to weigh inlying and outlying extreme or untrustworthy evidence more equally. Furthermore, the model that integrated evidence as a function of the LPR of the two perceptual options and produced robust averaging showed poorer fit for the group prone to hallucinations. Finally, the weighting strategy in hallucination-prone individuals remained insensitive to evidence variance. CONCLUSIONS: Our findings provide empirical support for theoretical proposals regarding evidence integration aberrations in psychosis and alterations in the perceptual systems that track statistical regularities in environmental stimuli.

Generalizability and Out-of-Sample Predictive Ability of Associations Between Neuromelanin-Sensitive Magnetic Resonance Imaging and Psychosis in Antipsychotic-Free Individuals.

Importance: The link between psychosis and dopaminergic dysfunction is established, but no generalizable biomarkers with clear potential for clinical adoption exist. Objective: To replicate previous findings relating neuromelanin-sensitive magnetic resonance imaging (NM-MRI), a proxy measure of dopamine function, to psychosis severity in antipsychotic-free individuals in the psychosis spectrum and to evaluate the out-of-sample predictive ability of NM-MRI for psychosis severity. Design, Setting, and Participants: This cross-sectional study recruited participants from 2019 to 2023 in the New York City area (main samples) and Mexico City area (external validation sample). The main samples consisted of 42 antipsychotic-free patients with schizophrenia, 53 antipsychotic-free individuals at clinical high risk for psychosis (CHR), and 52 matched healthy controls. An external validation sample consisted of 16 antipsychotic-naive patients with schizophrenia. Main Outcomes and Measures: NM-MRI contrast within a subregion of the substantia nigra previously linked to psychosis severity (a priori psychosis region of interest [ROI]) and psychosis severity measured using the Positive and Negative Syndrome Scale (PANSS) in schizophrenia and the Structured Interview for Psychosis-Risk Syndromes (SIPS) in CHR. The cross-validated performance of linear support vector regression to predict psychosis severity across schizophrenia and CHR was assessed, and a final trained model was tested on the external validation sample. Results: Of the 163 included participants, 76 (46.6%) were female, and the mean (SD) age was 29.2 (10.4) years. In the schizophrenia sample, higher PANSS positive total scores correlated with higher mean NM-MRI contrast in the psychosis ROI (t37 = 2.24, P = .03; partial r = 0.35; 95% CI, 0.05 to 0.55). In the CHR sample, no significant association was found between higher SIPS positive total score and NM-MRI contrast in the psychosis ROI (t48 = -0.55, P = .68; partial r = -0.08; 95% CI, -0.36 to 0.23). The 10-fold cross-validated prediction accuracy of psychosis severity was above chance in held-out test data (mean r = 0.305, P = .01; mean root-mean-square error [RMSE] = 1.001, P = .005). External validation prediction accuracy was also above chance (r = 0.422, P = .046; RMSE = 0.882, P = .047). Conclusions and Relevance: This study provided a direct ROI-based replication of the in-sample association between NM-MRI contrast and psychosis severity in antipsychotic-free patients with schizophrenia. In turn, it failed to replicate such association in CHR individuals. Most critically, cross-validated machine-learning analyses provided a proof-of-concept demonstration that NM-MRI patterns can be used to predict psychosis severity in new data, suggesting potential for developing clinically useful tools.

A Distinct Neural Code Supports Prospection of Future Probabilities During Instrumental Information-Seeking.

To make adaptive decisions, we must actively demand information, but relatively little is known about the mechanisms of active information gathering. An open question is how the brain estimates expected information gains (EIG) when comparing the current decision uncertainty with the uncertainty that is expected after gathering information. We examined this question using fMRI in a task in which people placed bids to obtain information in conditions that varied independently by prior decision uncertainty, information diagnosticity, and the penalty for an erroneous choice. Consistent with value of information theory, bids were sensitive to EIG and its components of prior certainty and expected posterior certainty. Expected posterior certainty was decoded above chance from multivoxel activation patterns in the posterior parietal and extrastriate cortices. This representation was independent of instrumental rewards and overlapped with distinct representations of EIG and prior certainty. Thus, posterior parietal and extrastriate cortices are candidates for mediating the prospection of posterior probabilities as a key step to estimate EIG during active information gathering.

Introspective inference counteracts perceptual distortion.

Introspective agents can recognize the extent to which their internal perceptual experiences deviate from the actual states of the external world. This ability, also known as insight, is critically required for reality testing and is impaired in psychosis, yet little is known about its cognitive underpinnings. We develop a Bayesian modeling framework and a psychophysics paradigm to quantitatively characterize this type of insight while people experience a motion after-effect illusion. People can incorporate knowledge about the illusion into their decisions when judging the actual direction of a motion stimulus, compensating for the illusion (and often overcompensating). Furthermore, confidence, reaction-time, and pupil-dilation data all show signatures consistent with inferential adjustments in the Bayesian insight model. Our results suggest that people can question the veracity of what they see by making insightful inferences that incorporate introspective knowledge about internal distortions.

Reduced neural encoding of utility prediction errors in cocaine addiction.

Influential accounts of addiction posit alterations in adaptive behavior driven by deficient dopaminergic prediction errors (PEs), signaling the discrepancy between actual and expected reward. Dopamine neurons encode these error signals in subjective terms, calibrated by individual risk preferences, as "utility" PEs. It remains unclear, however, whether people with drug addiction have PE deficits or their computational source. Here, using an analogous task to prior single-unit studies with known expectancies, we show that fMRI-measured PEs similarly reflect utility PEs. Relative to control participants, people with chronic cocaine addiction demonstrate reduced utility PEs in the dopaminoceptive ventral striatum, with similar trends in orbitofrontal cortex. Dissecting this PE signal into its subcomponent terms attributed these reductions to weaker striatal responses to received reward/utility, whereas suppression of activity with reward expectation was unchanged. These findings support that addiction may fundamentally disrupt PE signaling and reveal an underappreciated role for perceived reward value in this mechanism.

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 older adults in the US and worldwide. Resting-state functional magnetic resonance imaging (rs-fMRI) has become a widely used neuroimaging tool 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 intrinsic neural signals using rs-fMRI, is thought to quantify the duration that neural information is stored in a local cortical circuit. The heterogeneity of the timescales is considered to be a basis of the functional hierarchy in the brain. In addition, INT captures an aspect of circuit dynamics relevant to excitation/inhibition (E/I) balance, which is thought to be broadly relevant for cognitive functions. Here we examined its relevance to AD. We used rs-fMRI data of 904 individuals from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. The subjects were divided into 4 groups based on their baseline and end-visit clinical status, which were cognitively normal (CN), stable MCI, Converter, and AD groups. Linear mixed effect model and pairwise comparison were implemented to investigate the large-scale hierarchical organization and local differences. We observed high similarities between AD and Converter groups. Specifically, among the eight identified ROIs with distinct INT alterations in AD, three ROIs (inferior temporal, caudate, pallidum areas) exhibit stable and significant alteration in AD converter. In addition, distinct INT related pathological changes in stable MCI and AD/Converter were found. For AD and Converter groups, neural information is stored for a longer time in lower hierarchical order areas, while higher levels of hierarchy seem to be preferentially impaired in stable MCI leading to a less pronounced hierarchical gradient effect. These results inform that the INT holds great potential as an additional measure for AD prediction, a stable biomarker for clinical diagnosis and an important therapeutic target in AD.

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 minutes, 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.

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 minutes, 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.

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.

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.

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.

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.