Preserved neural dynamics across animals performing similar behaviour.

Animals of the same species exhibit similar behaviours that are advantageously adapted to their body and environment. These behaviours are shaped at the species level by selection pressures over evolutionary timescales. Yet, it remains unclear how these common behavioural adaptations emerge from the idiosyncratic neural circuitry of each individual. The overall organization of neural circuits is preserved across individuals1 because of their common evolutionarily specified developmental programme2-4. Such organization at the circuit level may constrain neural activity5-8, leading to low-dimensional latent dynamics across the neural population9-11. Accordingly, here we suggested that the shared circuit-level constraints within a species would lead to suitably preserved latent dynamics across individuals. We analysed recordings of neural populations from monkey and mouse motor cortex to demonstrate that neural dynamics in individuals from the same species are surprisingly preserved when they perform similar behaviour. Neural population dynamics were also preserved when animals consciously planned future movements without overt behaviour12 and enabled the decoding of planned and ongoing movement across different individuals. Furthermore, we found that preserved neural dynamics extend beyond cortical regions to the dorsal striatum, an evolutionarily older structure13,14. Finally, we used neural network models to demonstrate that behavioural similarity is necessary but not sufficient for this preservation. We posit that these emergent dynamics result from evolutionary constraints on brain development and thus reflect fundamental properties of the neural basis of behaviour.

Effects of phase encoding direction on test-retest reliability of human functional connectome.

The majority of human connectome studies in the literature based on functional magnetic resonance imaging (fMRI) data use either an anterior-to-posterior (AP) or a posterior-to-anterior (PA) phase encoding direction (PED). However, whether and how PED would affect test-retest reliability of functional connectome is unclear. Here, in a sample of healthy subjects with two sessions of fMRI scans separated by 12 weeks (two runs per session, one with AP, the other with PA), we tested the influence of PED on global, nodal, and edge connectivity in the constructed brain networks. All data underwent the state-of-the-art Human Connectome Project (HCP) pipeline to correct for phase-encoding-related distortions before entering analysis. We found that at the global level, the PA scans showed significantly higher intraclass correlation coefficients (ICCs) for global connectivity compared with AP scans, which was particularly prominent when using the Seitzman-300 atlas (versus the CAB-NP-718 atlas). At the nodal level, regions most strongly affected by PED were consistently mapped to the cingulate cortex, temporal lobe, sensorimotor areas, and visual areas, with significantly higher ICCs during PA scans compared with AP scans, regardless of atlas. Better ICCs were also observed during PA scans at the edge level, in particular when global signal regression (GSR) was not performed. Further, we demonstrated that the observed reliability differences between PEDs may relate to a similar effect on the reliability of temporal signal-to-noise ratio (tSNR) in the same regions (that PA scans were associated with higher reliability of tSNR than AP scans). Averaging the connectivity outcome from the AP and PA scans could increase median ICCs, especially at the nodal and edge levels. Similar results at the global and nodal levels were replicated in an independent, public dataset from the HCP-Early Psychosis (HCP-EP) study with a similar design but a much shorter scan session interval. Our findings suggest that PED has significant effects on the reliability of connectomic estimates in fMRI studies. We urge that these effects need to be carefully considered in future neuroimaging designs, especially in longitudinal studies such as those related to neurodevelopment or clinical intervention.

Frequency specific resting state functional abnormalities in psychosis.

Resting state functional magnetic resonance imaging studies of psychosis have focused primarily on the amplitude of low-frequency fluctuations in the blood oxygen level dependent (BOLD) signal ranging from .01 to 0.1 Hz. Few studies, however, have investigated the amplitude of frequency fluctuations within discrete frequency bands and higher than 0.1 Hz in patients with psychosis at different illness stages. We investigated BOLD signal within three frequency ranges including slow-4 (.027-.073 Hz), slow-3 (.074-0.198 Hz) and slow-2 (0.199-0.25 Hz) in 89 patients with either first-episode or chronic psychosis and 119 healthy volunteers. We investigated the amplitude of frequency fluctuations within three frequency bands using 47 regions-of-interest placed within 14 known resting state networks derived using group independent component analysis. There were significant group x frequency interactions for the visual and motor cortex networks, with the largest significant group differences (patients < healthy volunteers) evident in slow-4 and slow-3, respectively. Also, healthy volunteers had an overall higher amplitude of frequency fluctuations compared to patients across the three frequency ranges in the visual cortex, dorsal attention and motor cortex networks with the opposite effect (patients > healthy volunteers) evident within the salience and frontal gyrus networks. Subsequent analyses indicated that these effects were evident in both first-episode and chronic patients. Our study provides new data regarding the importance of BOLD signal fluctuations within different frequency bands in the neurobiology of psychosis.

The phase difference between neural drives to antagonist muscles in essential tremor is associated with the relative strength of supraspinal and afferent input.

The pathophysiology of essential tremor (ET), the most common movement disorder, is not fully understood. We investigated which factors determine the variability in the phase difference between neural drives to antagonist muscles, a long-standing observation yet unexplained. We used a computational model to simulate the effects of different levels of voluntary and tremulous synaptic input to antagonistic motoneuron pools on the tremor. We compared these simulations to data from 11 human ET patients. In both analyses, the neural drive to muscle was represented as the pooled spike trains of several motor units, which provides an accurate representation of the common synaptic input to motoneurons. The simulations showed that, for each voluntary input level, the phase difference between neural drives to antagonist muscles is determined by the relative strength of the supraspinal tremor input to the motoneuron pools. In addition, when the supraspinal tremor input to one muscle was weak or absent, Ia afferents provided significant common tremor input due to passive stretch. The simulations predicted that without a voluntary drive (rest tremor) the neural drives would be more likely in phase, while a concurrent voluntary input (postural tremor) would lead more frequently to an out-of-phase pattern. The experimental results matched these predictions, showing a significant change in phase difference between postural and rest tremor. They also indicated that the common tremor input is always shared by the antagonistic motoneuron pools, in agreement with the simulations. Our results highlight that the interplay between supraspinal input and spinal afferents is relevant for tremor generation.

Influence of common synaptic input to motor neurons on the neural drive to muscle in essential tremor.

Tremor in essential tremor (ET) is generated by pathological oscillations at 4-12 Hz, likely originating at cerebello-thalamo-cortical pathways. However, the way in which tremor is represented in the output of the spinal cord circuitries is largely unknown because of the difficulties in identifying the behavior of individual motor units from tremulous muscles. By using novel methods for the decomposition of multichannel surface EMG, we provide a systematic analysis of the discharge properties of motor units in nine ET patients, with concurrent recordings of EEG activity. This analysis allowed us to infer the contribution of common synaptic inputs to motor neurons in ET. Motor unit short-term synchronization was significantly greater in ET patients than in healthy subjects. Furthermore, the strong association between the degree of synchronization and the peak in coherence between motor unit spike trains at the tremor frequency indicated that the high synchronization levels were generated mainly by common synaptic inputs specifically at the tremor frequency. The coherence between EEG and motor unit spike trains demonstrated the presence of common cortical input to the motor neurons at the tremor frequency. Nonetheless, the strength of this input was uncorrelated to the net common synaptic input at the tremor frequency, suggesting a contribution of spinal afferents or secondary supraspinal pathways in projecting common input at the tremor frequency. These results provide the first systematic analysis of the neural drive to the muscle in ET and elucidate some of its characteristics that determine pathological tremulous muscle activity.

Prevalence and correlates of antipsychotic polypharmacy in children and adolescents receiving antipsychotic treatment.

Antipsychotic polypharmacy (APP), which is common in adults with psychotic disorders, is of unproven efficacy and raises safety concerns. Although youth are increasingly prescribed antipsychotics, little is known about APP in this population. We performed a systematic PubMed search (last update 26 January 2013) of studies reporting the prevalence of APP in antipsychotic-treated youth. Summary statistics and statistical tests were calculated at the study level and not weighted by sample size. Fifteen studies (n = 58 041, range 68-23 183) reported on APP in youth [mean age = 13.4 ± 1.7 yr, 67.1 ± 10.2% male, 77.9 ± 27.4% treated with second-generation antipsychotics (SGAs)]. Data collected in these studies covered 1993-2008. The most common diagnoses were attention-deficit hyperactivity disorder (ADHD; 39.9 ± 23.5%) and conduct disorder/oppositional defiant disorder (CD/ODD; 33.6 ± 24.8). In studies including predominantly children (mean age = <13 yr, N = 5), the most common diagnosis were ADHD (50.6 ± 25.4%) and CD/ODD (39.5 ± 27.5%); while in studies with predominantly adolescents (mean age = ⩾13 yr, N = 7) the most common diagnoses were schizophrenia-spectrum disorders (28.6 ± 23.8%), anxiety disorders (26.9 ± 14.9%) and bipolar-spectrum disorders (26.6 ± 7.0%), followed closely by CD/ODD (25.8 ± 17.7). The prevalence of APP among antipsychotic-treated youth was 9.6 ± 7.2% (5.9 ± 4.5% in child studies, 12.0 ± 7.9% in adolescent studies, p = 0.15). Higher prevalence of APP was correlated with a bipolar disorder or schizophrenia diagnosis (p = 0.019) and APP involving SGA+SGA combinations (p = 0.0027). No correlation was found with APP definition [⩾1 d (N = 10) vs. >30-⩾90 d (N = 5), p = 0.88]. Despite lacking safety and efficacy data, APP in youth is not uncommon, even in samples predominantly consisting of non-psychotic patients. The duration, clinical motivations and effectiveness of this practice require further study.

Central band interosseus membrane reconstruction for longitudinal instability injuries of the forearm.

Aims: Acute and chronic injuries of the interosseus membrane can result in longitudinal instability of the forearm. Reconstruction of the central band of the interosseus membrane can help to restore biomechanical stability. Different methods have been used to reconstruct the central band, including tendon grafts, bone-ligament-bone grafts, and synthetic grafts. This Idea, Development, Exploration, Assessment, and Long-term (IDEAL) phase 1 study aims to review the clinical results of reconstruction using a synthetic braided cross-linked graft secured at either end with an Endobutton to restore the force balance between the bones of the forearm. Methods: An independent retrospective review was conducted of a consecutive series of 21 patients with longitudinal instability injuries treated with anatomical central band reconstruction between February 2011 and July 2019. Patients with less than 12 months' follow-up or who were treated acutely were excluded, leaving 18 patients in total. Preoperative clinical and radiological assessments were compared with prospectively gathered data using range of motion and the abbreviated version of the Disabilities of the Arm, Shoulder and Hand questionnaire (QuickDASH) functional outcome score. Results: Of the 18 patients (nine male, nine female) who met the inclusion criteria, the median follow-up was 8.5 years (interquartile range (IQR) 5.6 to 10). Their mean age was 49 years (SD 11). The mean extension improved significantly from 38° (SD 15°) to 24° (SD 9°) (p = 0.027), with a mean flexion-extension arc change from 81° (SD 27°) to 93° (SD 30°) (p = 0.172) but with no forearm rotational improvement (p = 0.233) at latest follow-up. The QuickDASH functional score improved significantly from 80 (SD 14) to 52 (SD 26) following reconstruction (p = 0.031), but generally the level of disability remains high. Radiological assessment showed no progression of proximal migration of the radius, with a stable interbutton distance and ulnar variance from immediate postoperative radiograph to the latest follow-up. Conclusion: Central band interosseus membrane reconstruction using a synthetic braided cross-linked graft can improve patient-rated arm function and range of motion, but significant functional deficits remain in patients with chronic injuries.

Integrating across behaviors and timescales to understand the neural control of movement.

The nervous system evolved to enable navigation throughout the environment in the pursuit of resources. Evolutionarily newer structures allowed increasingly complex adaptations but necessarily added redundancy. A dominant view of movement neuroscientists is that there is a one-to-one mapping between brain region and function. However, recent experimental data is hard to reconcile with the most conservative interpretation of this framework, suggesting a degree of functional redundancy during the performance of well-learned, constrained behaviors. This apparent redundancy likely stems from the bidirectional interactions between the various cortical and subcortical structures involved in motor control. We posit that these bidirectional connections enable flexible interactions across structures that change depending upon behavioral demands, such as during acquisition, execution or adaptation of a skill. Observing the system across both multiple actions and behavioral timescales can help isolate the functional contributions of individual structures, leading to an integrated understanding of the neural control of movement.

De novo motor learning creates structure in neural activity that shapes adaptation.

Animals can quickly adapt learned movements to external perturbations, and their existing motor repertoire likely influences their ease of adaptation. Long-term learning causes lasting changes in neural connectivity, which shapes the activity patterns that can be produced during adaptation. Here, we examined how a neural population's existing activity patterns, acquired through de novo learning, affect subsequent adaptation by modeling motor cortical neural population dynamics with recurrent neural networks. We trained networks on different motor repertoires comprising varying numbers of movements, which they acquired following various learning experiences. Networks with multiple movements had more constrained and robust dynamics, which were associated with more defined neural 'structure'-organization in the available population activity patterns. This structure facilitated adaptation, but only when the changes imposed by the perturbation were congruent with the organization of the inputs and the structure in neural activity acquired during de novo learning. These results highlight trade-offs in skill acquisition and demonstrate how different learning experiences can shape the geometrical properties of neural population activity and subsequent adaptation.

Evidence for cytokine dysregulation in schizophrenia spectrum disorders: A comparison of cerebrospinal fluid and blood samples.

Abnormalities in immune function have been described in schizophrenia but few studies have investigated cytokines in cerebrospinal fluid (CSF) and their correlation with blood levels. In this cross-sectional study, cytokines were measured in CSF and plasma of 30 subjects with schizophrenia spectrum disorder (SSD) diagnosis and 23 healthy volunteers (HV). Results showed that CSF TNFα was increased in SSD subjects compared to HV and there were no correlations between CSF and plasma cytokine levels. The present findings provide evidence of dysregulation of TNFα in CSF of schizophrenia. These results identify elevated CSF TNFα levels as a potential biomarker in schizophrenia.

Efficacy and safety of individual second-generation vs. first-generation antipsychotics in first-episode psychosis: a systematic review and meta-analysis.

Because early treatment choice is critical in first-episode schizophrenia-spectrum disorders (FES), this meta-analysis compared efficacy and tolerability of individual second-generation antipsychotics (SGAs) with first-generation antipsychotics (FGAs) in FES. We conducted systematic literature search (until 12 December 2010) and meta-analysis of acute, randomized trials with ≥1 FGA vs. SGA comparison; patients in their first episode of psychosis and diagnosed with schizophrenia-spectrum disorders; available data for psychopathology change, treatment response, treatment discontinuation, adverse effects, or cognition. Across 13 trials (n = 2509), olanzapine (seven trials) and amisulpride (one trial) outperformed FGAs (haloperidol: 9/13 trials) in 9/13 and 8/13 efficacy outcomes, respectively, risperidone (eight trials) in 4/13, quetiapine (one trial) in 3/13 and clozapine (two trials) and ziprasidone (one trial) in 1/13, each. Compared to FGAs, extrapyramidal symptom (EPS)-related outcomes were less frequent with olanzapine, risperidone and clozapine, but weight gain was greater with clozapine, olanzapine and risperidone. Pooled SGAs were similar to FGAs regarding total psychopathology change, depression, treatment response and metabolic changes. SGAs significantly outperformed FGAs regarding lower treatment discontinuation, irrespective of cause, negative symptoms, global cognition and less EPS and akathisia, while SGAs increased weight more (p < 0.05-0.01). Results were not affected by FGA dose or publication bias, but industry-sponsored studies favoured SGAs more than federally funded studies. To summarize, in FES, olanzapine, amisulpride and, less so, risperidone and quetiapine showed superior efficacy, greater treatment persistence and less EPS than FGAs. However, weight increase with olanzapine, risperidone and clozapine and metabolic changes with olanzapine were greater. Additional FES studies including broader-based SGAs and FGAs are needed.

De novo motor learning creates structure in neural activity space that shapes adaptation.

Animals can quickly adapt learned movements in response to external perturbations. Motor adaptation is likely influenced by an animal's existing movement repertoire, but the nature of this influence is unclear. Long-term learning causes lasting changes in neural connectivity which determine the activity patterns that can be produced. Here, we sought to understand how a neural population's activity repertoire, acquired through long-term learning, affects short-term adaptation by modeling motor cortical neural population dynamics during de novo learning and subsequent adaptation using recurrent neural networks. We trained these networks on different motor repertoires comprising varying numbers of movements. Networks with multiple movements had more constrained and robust dynamics, which were associated with more defined neural 'structure'-organization created by the neural population activity patterns corresponding to each movement. This structure facilitated adaptation, but only when small changes in motor output were required, and when the structure of the network inputs, the neural activity space, and the perturbation were congruent. These results highlight trade-offs in skill acquisition and demonstrate how prior experience and external cues during learning can shape the geometrical properties of neural population activity as well as subsequent adaptation.

Nonlinear manifolds underlie neural population activity during behaviour.

There is rich variety in the activity of single neurons recorded during behaviour. Yet, these diverse single neuron responses can be well described by relatively few patterns of neural co-modulation. The study of such low-dimensional structure of neural population activity has provided important insights into how the brain generates behaviour. Virtually all of these studies have used linear dimensionality reduction techniques to estimate these population-wide co-modulation patterns, constraining them to a flat "neural manifold". Here, we hypothesised that since neurons have nonlinear responses and make thousands of distributed and recurrent connections that likely amplify such nonlinearities, neural manifolds should be intrinsically nonlinear. Combining neural population recordings from monkey motor cortex, mouse motor cortex, mouse striatum, and human motor cortex, we show that: 1) neural manifolds are intrinsically nonlinear; 2) the degree of their nonlinearity varies across architecturally distinct brain regions; and 3) manifold nonlinearity becomes more evident during complex tasks that require more varied activity patterns. Simulations using recurrent neural network models confirmed the proposed relationship between circuit connectivity and manifold nonlinearity, including the differences across architecturally distinct regions. Thus, neural manifolds underlying the generation of behaviour are inherently nonlinear, and properly accounting for such nonlinearities will be critical as neuroscientists move towards studying numerous brain regions involved in increasingly complex and naturalistic behaviours.

A Functional Connectome-Based Neural Signature for Individualized Prediction of Antipsychotic Response in First-Episode Psychosis.

OBJECTIVE: Identification of robust biomarkers that predict individualized response to antipsychotic treatment at the early stage of psychotic disorders remains a challenge in precision psychiatry. The aim of this study was to investigate whether any functional connectome-based neural traits could serve as such a biomarker. METHODS: In a discovery sample, 49 patients with first-episode psychosis received multi-paradigm fMRI scans at baseline and were clinically followed up for 12 weeks under antipsychotic monotherapies. Treatment response was evaluated at the individual level based on the psychosis score of the Brief Psychiatric Rating Scale. Cross-paradigm connectivity and connectome-based predictive modeling were employed to train a predictive model that uses baseline connectomic measures to predict individualized change rates of psychosis scores, with model performance evaluated as the Pearson correlations between the predicted change rates and the observed change rates, based on cross-validation. The model generalizability was further examined in an independent validation sample of 24 patients in a similar design. RESULTS: The results revealed a paradigm-independent connectomic trait that significantly predicted individualized treatment outcome in both the discovery sample (predicted-versus-observed r=0.41) and the validation sample (predicted-versus-observed r=0.47, mean squared error=0.019). Features that positively predicted psychosis change rates primarily involved connections related to the cerebellar-cortical circuitry, and features that negatively predicted psychosis change rates were chiefly connections within the cortical cognitive systems. CONCLUSIONS: This study discovers and validates a connectome-based functional signature as a promising early predictor for individualized response to antipsychotic treatment in first-episode psychosis, thus highlighting the potential clinical value of this biomarker in precision psychiatry.

Contributions of Parasympathetic Arousal-Related Activity to Cognitive Performance in Patients With First-Episode Psychosis and Control Subjects.

BACKGROUND: Cognitive impairment is integral to the pathophysiology of psychosis. Recent findings implicate autonomic arousal-related activity in both momentary fluctuations and individual differences in cognitive performance. Although altered autonomic arousal is common in patients with first-episode psychosis (FEP), its contribution to cognitive performance is unknown. METHODS: A total of 24 patients with FEP (46% male, age = 24.31 [SD 4.27] years) and 24 control subjects (42% male, age = 27.06 [3.44] years) performed the Multi-Source Interference Task in-scanner with simultaneous pulse oximetry. First-level models included the cardiac-blood oxygen level-dependent regressor, in addition to task (congruent, interference, and error) and nuisance (motion and CompCor physiology) regressors. The cardiac-blood oxygen level-dependent regressor reflected parasympathetic arousal-related activity and was created by convolving the interbeat interval at each heartbeat with the hemodynamic response function. Group models examined the effect of group or cognitive performance (reaction times × error rate) on arousal-related and task activity, while controlling for sex, age, and framewise displacement. RESULTS: Parasympathetic arousal-related activity was robust in both groups but localized to different regions for patients with FEP and healthy control subjects. Within both groups, arousal-related activity was significantly associated with cognitive performance across occipital and temporal cortical regions. Greater arousal-related activity in the bilateral prefrontal cortex (Brodmann area 9) was related to better performance in healthy control subjects but not patients with FEP. CONCLUSIONS: Autonomic arousal circuits contribute to cognitive performance and the pathophysiology of FEP. Arousal-related functional activity is a novel indicator of cognitive ability and should be incorporated into neurobiological models of cognition in psychosis.

A robust Kalman algorithm to facilitate human-computer interaction for people with cerebral palsy, using a new interface based on inertial sensors.

This work aims to create an advanced human-computer interface called ENLAZA for people with cerebral palsy (CP). Although there are computer-access solutions for disabled people in general, there are few evidences from motor disabled community (e.g., CP) using these alternative interfaces. The proposed interface is based on inertial sensors in order to characterize involuntary motion in terms of time, frequency and range of motion. This characterization is used to design a filtering technique that reduces the effect of involuntary motion on person-computer interaction. This paper presents a robust Kalman filter (RKF) design to facilitate fine motor control based on the previous characterization. The filter increases mouse pointer directivity and the target acquisition time is reduced by a factor of ten. The interface is validated with CP users who were unable to control the computer using other interfaces. The interface ENLAZA and the RKF enabled them to use the computer.

Going beyond primary motor cortex to improve brain-computer interfaces.

Brain-computer interfaces (BCIs) for movement restoration typically decode the user's intent from neural activity in their primary motor cortex (M1) and use this information to enable 'mental control' of an external device. Here, we argue that activity in M1 has both too little and too much information for optimal decoding: too little, in that many regions beyond it contribute unique motor outputs and have movement-related information that is absent or otherwise difficult to resolve from M1 activity; and too much, in that motor commands are tangled up with nonmotor processes such as attention and feedback processing, potentially hindering decoding. Both challenges might be circumvented, we argue, by integrating additional information from multiple brain regions to develop BCIs that will better interpret the user's intent.

Skewing of the antibody repertoire in cerebrospinal fluid B cells from healthy controls and patients with schizophrenia.

Autoantibodies play a role in the etiology of some neuropsychiatric disorders. To address the possibility that B cells and their antibodies may be involved in the pathophysiology of schizophrenia, we examined B cells in cerebrospinal fluid (CSF) and peripheral blood (PB) of 4 schizophrenic patients (SP) and 4 healthy control (HC) volunteers by analyzing immunoglobulin VH gene usage. All CSF samples contained measurable levels of B cells. We found for both SP and HC, CSF B cells represented a select subset of, and were not the same as, B cells in PB. Moreover, we found statistically significant differences in antibodies generated by CSF B cells in SP compared to CSF B cells in HC. Although binding characteristics of CSF SP-associated B cell antibodies is unknown, the study number is small, and pathophysiology has not been established, these results suggest the value of focusing further study on the distinctly separate population of CSF B cells in SP.

Small, correlated changes in synaptic connectivity may facilitate rapid motor learning.

Animals rapidly adapt their movements to external perturbations, a process paralleled by changes in neural activity in the motor cortex. Experimental studies suggest that these changes originate from altered inputs (Hinput) rather than from changes in local connectivity (Hlocal), as neural covariance is largely preserved during adaptation. Since measuring synaptic changes in vivo remains very challenging, we used a modular recurrent neural network to qualitatively test this interpretation. As expected, Hinput resulted in small activity changes and largely preserved covariance. Surprisingly given the presumed dependence of stable covariance on preserved circuit connectivity, Hlocal led to only slightly larger changes in activity and covariance, still within the range of experimental recordings. This similarity is due to Hlocal only requiring small, correlated connectivity changes for successful adaptation. Simulations of tasks that impose increasingly larger behavioural changes revealed a growing difference between Hinput and Hlocal, which could be exploited when designing future experiments.

Frontal lobe fALFF measured from resting-state fMRI as a prognostic biomarker in first-episode psychosis.

Clinical response to antipsychotic drug treatment is highly variable, yet prognostic biomarkers are lacking. The goal of the present study was to test whether the fractional amplitude of low-frequency fluctuations (fALFF), as measured from baseline resting-state fMRI data, can serve as a potential biomarker of treatment response to antipsychotics. Patients in the first episode of psychosis (n = 126) were enrolled in two prospective studies employing second-generation antipsychotics (risperidone or aripiprazole). Patients were scanned at the initiation of treatment on a 3T MRI scanner (Study 1, GE Signa HDx, n = 74; Study 2, Siemens Prisma, n = 52). Voxelwise fALFF derived from baseline resting-state fMRI scans served as the primary measure of interest, providing a hypothesis-free (as opposed to region-of-interest) search for regions of the brain that might be predictive of response. At baseline, patients who would later meet strict criteria for clinical response (defined as two consecutive ratings of much or very much improved on the CGI, as well as a rating of ≤3 on psychosis-related items of the BPRS-A) demonstrated significantly greater baseline fALFF in bilateral orbitofrontal cortex compared to non-responders. Thus, spontaneous activity in orbitofrontal cortex may serve as a prognostic biomarker of antipsychotic treatment.