fMRIPrep: a robust preprocessing pipeline for functional MRI

Social Cognition and Functional Connectivity in Early and Chronic Schizophrenia

BACKGROUND

Individuals with schizophrenia (SZ) experience impairments in social cognition that contribute to poor functional outcomes. However, mechanisms of social cognitive dysfunction in SZ remain poorly understood, which impedes the design of novel interventions to improve outcomes. In this preregistered project, we examined the representation of social cognition in the brain's functional architecture in early and chronic SZ.

METHODS

The study contains 2 parts: a confirmatory and an exploratory portion. In the confirmatory portion, we identified resting-state connectivity disruptions evident in early and chronic SZ. We performed a connectivity analysis using regions associated with social cognitive dysfunction in early and chronic SZ to test whether aberrant connectivity observed in chronic SZ (n = 47 chronic SZ and n = 52 healthy control participants) was also present in early SZ (n = 71 early SZ and n = 47 healthy control participants). In the exploratory portion, we assessed the out-of-sample generalizability and precision of predictive models of social cognition. We used machine learning to predict social cognition and established generalizability with out-of-sample testing and confound control.

RESULTS

Results revealed decreases between the left inferior frontal gyrus and the intraparietal sulcus in early and chronic SZ, which were significantly associated with social and general cognition and global functioning in chronic SZ and with general cognition and global functioning in early SZ. Predictive modeling revealed the importance of out-of-sample evaluation and confound control.

CONCLUSIONS

This work provides insights into the functional architecture in early and chronic SZ and suggests that inferior frontal gyrus-intraparietal sulcus connectivity could be a prognostic biomarker of social impairments and a target for future interventions (e.g., neuromodulation) focused on improved social functioning.

Neuroimaging-based variability in subtyping biomarkers for psychiatric heterogeneity

Neuroimaging-based subtyping is increasingly used to explain heterogeneity in psychiatric disorders. However, the clinical utility of these subtyping efforts remains unclear, and replication has been challenging. Here we examined how the choice of neuroimaging measures influences the derivation of neuro-subtypes and the consequences for clinical delineation. On a clinically heterogeneous dataset (total n = 566) that included controls (n = 268) and cases (n = 298) of psychiatric conditions, including individuals diagnosed with post-traumatic stress disorder (PTSD), traumatic brain injury (TBI), and comorbidity of both (PTSD&TBI), we identified neuro-subtypes among the cases using either structural, resting-state, or task-based measures. The neuro-subtypes for each modality had high internal validity but did not significantly differ in their clinical and cognitive profiles. We further show that the choice of neuroimaging measures for subtyping substantially impacts the identification of neuro-subtypes, leading to low concordance across subtyping solutions. Similar variability in neuro-subtyping was found in an independent dataset (n = 1642) comprised of major depression disorder (MDD, n = 848) and controls (n = 794). Our results suggest that the highly anticipated relationships between neuro-subtypes and clinical features may be difficult to discover.

Versatile MRI acquisition and processing protocol for population-based neuroimaging

Neuroimaging has an essential role in studies of brain health and of cerebrovascular and neurodegenerative diseases, requiring the availability of versatile magnetic resonance imaging (MRI) acquisition and processing protocols. We designed and developed a multipurpose high-resolution MRI protocol for large-scale and long-term population neuroimaging studies that includes structural, diffusion-weighted and functional MRI modalities. This modular protocol takes almost 1 h of scan time and is, apart from a concluding abdominal scan, entirely dedicated to the brain. The protocol links the acquisition of an extensive set of MRI contrasts directly to the corresponding fully automated data processing pipelines and to the required quality assurance of the MRI data and of the image-derived phenotypes. Since its successful implementation in the population-based Rhineland Study (ongoing, currently more than 11,000 participants, target participant number of 20,000), the proposed MRI protocol has proved suitable for epidemiological and clinical cross-sectional and longitudinal studies, including multisite studies. The approach requires expertise in magnetic resonance image acquisition, in computer science for the data management and the execution of processing pipelines, and in brain anatomy for the quality assessment of the MRI data. The protocol takes ~1 h of MRI acquisition and ~20 h of data processing to complete for a single dataset, but parallelization over multiple datasets using high-performance computing resources reduces the processing time. By making the protocol, MRI sequences and pipelines available, we aim to contribute to better comparability, interoperability and reusability of large-scale neuroimaging data.

NaDyNet: A toolbox for dynamic network analysis of naturalistic stimuli

Experiments with naturalistic stimuli (e.g., listening to stories or watching movies) are emerging paradigms in brain function research. The content of naturalistic stimuli is rich and continuous. The fMRI signals of naturalistic stimuli are complex and include different components. A major challenge is isolate the stimuli-induced signals while simultaneously tracking the brain's responses to these stimuli in real-time. To this end, we have developed a user-friendly graphical interface toolbox called NaDyNet (Naturalistic Dynamic Network Toolbox), which integrates existing dynamic brain network analysis methods and their improved versions. The main features of NaDyNet are: 1) extracting signals of interest from naturalistic fMRI signals; 2) incorporating six commonly used dynamic analysis methods and three static analysis methods; 3) improved versions of these dynamic methods by adopting inter-subject analysis to eliminate the effects of non-interest signals; 4) performing K-means clustering analysis to identify temporally reoccurring states along with their temporal and spatial attributes; 5) Visualization of spatiotemporal results. We then introduced the rationale for incorporating inter-subject analysis to improve existing dynamic brain network analysis methods and presented examples by analyzing naturalistic fMRI data. We hope that this toolbox will promote the development of naturalistic neuroscience. The toolbox is available at https://github.com/yuanbinke/Naturalistic-Dynamic-Network-Toolbox.

Loneliness - not for the faint of heart? Effects of transient loneliness induction on neural and parasympathetic responses to affective stimuli

While loneliness has been associated with altered neural activity in social brain networks and reduced heart rate variability (HRV) in response to social stressors, it is still unclear whether these are related or parallel effects. Thus, the current study aimed to examine the relationship between loneliness and neural and parasympathetic responses to social stimuli by using an experimental induction of momentary loneliness. Sixty-three participants (18-35 y.o.) received manipulated feedback about their future relationships to induce either loneliness (Future Alone, FA; n = 31) or feelings of belonging (Future Belong, FB, n = 32) and completed a functional magnetic resonance imaging session with concomitant HRV measurement during which affective pictures with social or nonsocial content were presented. In line with our previous research, decreased vagal flexibility and more negative affect were observed in participants subjected to the loneliness induction. Furthermore, even though no significant between-group differences in neural activity were observed, the neural response to negative social vs nonsocial stimuli in the temporoparietal junction was positively associated with the parasympathetic response, and this relationship was stronger in the FA group. Taken together, these results suggest that transient feelings of loneliness may disrupt adaptive responding to environmental demands and negatively impact brain-heart interactions.

Adversarial testing of global neuronal workspace and integrated information theories of consciousness

Different theories explain how subjective experience arises from brain activity1,2. These theories have independently accrued evidence, but have not been directly compared3. Here we present an open science adversarial collaboration directly juxtaposing integrated information theory (IIT)4,5 and global neuronal workspace theory (GNWT)6-10 via a theory-neutral consortium11-13. The theory proponents and the consortium developed and preregistered the experimental design, divergent predictions, expected outcomes and interpretation thereof12. Human participants (n = 256) viewed suprathreshold stimuli for variable durations while neural activity was measured with functional magnetic resonance imaging, magnetoencephalography and intracranial electroencephalography. We found information about conscious content in visual, ventrotemporal and inferior frontal cortex, with sustained responses in occipital and lateral temporal cortex reflecting stimulus duration, and content-specific synchronization between frontal and early visual areas. These results align with some predictions of IIT and GNWT, while substantially challenging key tenets of both theories. For IIT, a lack of sustained synchronization within the posterior cortex contradicts the claim that network connectivity specifies consciousness. GNWT is challenged by the general lack of ignition at stimulus offset and limited representation of certain conscious dimensions in the prefrontal cortex. These challenges extend to other theories of consciousness that share some of the predictions tested here14-17. Beyond challenging the theories, we present an alternative approach to advance cognitive neuroscience through principled, theory-driven, collaborative research and highlight the need for a quantitative framework for systematic theory testing and building.

Modeling Temporal Dependencies in Brain Functional Connectivity to Identify Autism Spectrum Disorders Based on Heterogeneous rs-fMRI Data

Brain functional connectivity has shown promise for developing objective biomarkers for autism spectrum disorder (ASD). Although many imaging studies have demonstrated its potential, most have focused on static measurements. In this study, we explored the dynamic changes in functional connectivity over time to uncover potential temporal dependencies. These dynamic patterns were abstracted into high-level representations and used as predictors to identify individuals at risk of ASD. To achieve this, we employed a deep learning framework that combines attention mechanism with long short-term memory (LSTM) neural network. Experiments were conducted using heterogeneous resting-state functional magnetic resonance imaging (rs-fMRI) data from the Autism Brain Imaging Data Exchange (ABIDE) database. The resulting classification achieved an accuracy of 74.9% and precision of 75.5% under intra-site cross-validation, outperforming traditional classifiers such as support vector machines (SVM), random forests (RF), and single LSTM network. Further analyses demonstrated the robustness and generalizability of our model, with classification performance less affected by subjects' gender or age. The optimal model's weights revealed atypical temporal dependencies in the brain functional connectivity of individuals with ASD, highlighting the potential for these patterns to serve as biomarkers. Our findings underscore the importance of dynamic functional connectivity in understanding ASD and suggest that our deep learning framework could aid in the development of more accurate and reliable diagnostic tools for this disorder.

Neural processing of social reciprocity in autism

Social reciprocity and interpersonal synchrony implicitly mediate social interactions to facilitate natural exchanges. These processes are altered in autism, but it is unclear how such alterations manifest at the neural level during social interaction processing. Using task-based fMRI, we investigated the neural correlates of interpersonal synchrony during basic reciprocal interactions in a preregistered study. Participants communicated with a virtual partner by sending visual signals. Analyses showed comparable activation patterns and experienced synchrony ratings between autistic and non-autistic participants, as well as between interactions with virtual partners who had high or low synchronous responses. An exploratory whole brain analysis for the effect of task revealed significant activation of the inferior frontal gyrus, insular cortex, and anterior inferior parietal lobe; areas associated with cognitive control, rhythmic temporal coordination, and action observation. This activation was independent of the virtual partner's response synchrony and was similar for autistic and non-autistic participants. These results provide an initial look into the neural basis of processing social reciprocity in autism, particularly when individuals are part of an interaction, and hint that the neural processing of social reciprocity may be spared in autism when their partners' behavior is predictable.

Neural representations of noncentral events during narrative encoding predict subsequent story ending originality

On the basis of the confluence theories of creativity, creative ideation depends on forging links between existing memory traces. The synergy between memory and creative thought is well-established, but neural dynamics of memory integration for creativity are understudied. Here, we extended the traditional memory paradigm. Participants read, recalled narratives, and wrote endings. Computational linguistic analysis showed that those integrating more noncentral events-those less semantically connected to other events within the narrative-wrote more original endings. Analyzing fMRI data captured during narrative encoding, we discovered that story ending originality can be predicted by shared event representation across participants in the right Brodmann area 25 (BA25) and stronger hippocampal event segmentation signal during noncentral event encoding. These results held across different narrative types (i.e., crime, romance, and fantasy stories). Overall, these results offer notable insights, from the perspective of network structure into how humans encode and retrieve complex real-world experiences to enhance creativity.

Human V4 size predicts crowding distance

Visual recognition is limited by both object size (acuity) and spacing. The spacing limit, called "crowding", is the failure to recognize an object in the presence of other objects. Here, we take advantage of individual differences in crowding to investigate its biological basis. Crowding distance, the minimum object spacing needed for recognition, varies 2-fold among healthy adults. We test the conjecture that this variation in psychophysical crowding distance is due to variation in cortical map size. To test this, we make paired measurements of brain and behavior in 49 observers. We use psychophysics to measure crowding distance and calculate λ, the number of letters that fit into each observer's visual field without crowding. In the same observers, we use functional magnetic resonance imaging (fMRI) to measure the surface area A of retinotopic maps V1, V2, V3, and V4. Across observers, λ is proportional to the surface area of V4 but is uncorrelated with the surface area of V1 to V3. The proportional relationship of λ to area of V4 indicates conservation of cortical crowding distance across individuals: letters can be recognized if they are spaced by at least 1.4 mm on the V4 map, irrespective of map size and psychophysical crowding distance. We conclude that the size of V4 predicts the spacing limit of visual perception.

Associations of choroid plexus volume with white matter hyperintensity volume and susceptibility and plasma amyloid markers in cerebral small vessel disease

BACKGROUND

White matter hyperintensity (WMH) is a key feature of cerebral small vessel disease (CSVD). The impact of the choroid plexus (CP) volume on disease progression remains largely unexplored. This study evaluated the relationship between CP volume and CSVD severity via WMH volume and susceptibility values. Additionally, we explored whether Alzheimer's disease (AD)-related plasma proteins influence the volume of the CP.

METHODS AND MATERIALS

Our study included 291 CSVD individuals, with 84 participants completing subsequent brain MRI at a mean follow-up of 20 months. To explore the potential CP-associated pathways, we assessed the relationships between AD-related plasma biomarkers and CP volume via multiple linear regression analysis. The longitudinal associations between CP volume and WMH characteristics (WMH volume and susceptibility) were analyzed via linear mixed-effects models. Finally, we employed random forest analysis with the Boruta algorithm to identify key predictors of CSVD severity.

RESULTS

Plasma Aβ1‒40 levels were positively correlated with CP volume (β = 0.115, P = 0.009), whereas Aβ42‒40 ratio were negatively associated with CP volume (β = -0.135, P = 0.03). Notably, increased CP volume was associated with both greater WMH burden (β = 0.191, P = 0.011) and decreased WMH susceptibility (β = -0.192, P = 0.012). Furthermore, random forest modeling identified CP volume and WMH susceptibility as the strongest predictors of CSVD severity.

CONCLUSIONS

CP volume changes were significantly correlated with both WMH volume and WMH susceptibility in CSVD patients. These findings suggest that CP-mediated pathways may link amyloid metabolism to CSVD progression.

Visual Statistical Learning Alters Low-Dimensional Cortical Architecture

Our brains are in a constant state of generating predictions, implicitly extracting environmental regularities to support later cognition and behavior, a process known as statistical learning (SL). While prior work investigating the neural basis of SL has focused on the activity of single brain regions in isolation, much less is known about how distributed brain areas coordinate their activity to support such learning. Using fMRI and a classic visual SL task, we investigated changes in whole-brain functional architecture as human female and male participants implicitly learned to associate pairs of images, and later, when predictions generated from learning were violated. By projecting individuals' patterns of cortical and subcortical functional connectivity onto a low-dimensional manifold space, we found that SL was associated with changes along a single neural dimension describing covariance across the visual-parietal and perirhinal cortex (PRC). During learning, we found regions within the visual cortex expanded along this dimension, reflecting their decreased communication with other networks, whereas regions within the dorsal attention network (DAN) contracted, reflecting their increased connectivity with higher-order cortex. Notably, when SL was interrupted, we found the PRC and entorhinal cortex, which did not initially show learning-related effects, now contracted along this dimension, reflecting their increased connectivity with the default mode and DAN, and decreased covariance with visual cortex. While prior research has linked SL to either broad cortical or medial temporal lobe changes, our findings suggest an integrative view, whereby cortical regions reorganize during association formation, while medial temporal lobe regions respond to their violation.

Transdiagnostic Neurobiology of Social Cognition and Individual Variability as Measured by Fractional Amplitude of Low-Frequency Fluctuation in Autism and Schizophrenia Spectrum Disorders

BACKGROUND

Fractional amplitude of low-frequency fluctuation (fALFF) is a validated measure of resting-state spontaneous brain activity. Previous fALFF findings in autism and schizophrenia spectrum disorders (SSDs) have been highly heterogeneous. We aimed to use fALFF in a large sample of typically developing control (TDC), autistic, and SSD participants to explore group differences and relationships with inter-individual variability of fALFF maps and social cognition.

METHODS

FALFF from 495 participants (185 TDC, 68 autism, and 242 SSD) was computed using functional magnetic resonance imaging as signal power within two frequency bands (i.e., slow-4 and slow-5), normalized by the power in the remaining frequency spectrum. Permutation analysis of linear models was employed to investigate the relationship of fALFF with diagnostic groups, higher-level social cognition, and lower-level social cognition. Each participant's average distance of fALFF map to all others was defined as a variability score, with higher scores indicating less typical maps.

RESULTS

Lower fALFF in the visual and higher fALFF in the frontal regions were found in both SSD and autistic participants compared with TDCs. Limited differences were observed between autistic and SSD participants in the cuneus regions only. Associations between slow-4 fALFF and higher-level social cognitive scores across the whole sample were observed in the lateral occipitotemporal and temporoparietal junction. Individual variability within the autism and SSD groups was also significantly higher compared with TDC.

CONCLUSIONS

Similar patterns of fALFF and individual variability in autism and SSD suggest some common neurobiological features across these related heterogeneous conditions.

A naturalistic fMRI dataset in response to public speaking

Public speaking serves as a powerful tool for informing, inspiring, persuading, motivating, or entertaining an audience. While some speeches effectively engage audience and disseminate knowledge, others fail to resonate. This dataset presents functional magnetic resonance imaging (fMRI) data from 31 participants (14 females; age: 22.29 ± 2.84 years) who viewed two informative speeches with varying effectiveness, selected from YiXi talks (similar to TED Talks), and matched in length and topic. A total of 22 participants (10 females; age: 22.64 ± 2.77 years) who completed the full task were included in the validation analyses. A comprehensive validation process, involving behavioral data analysis and head motion assessment, confirmed the quality of the fMRI dataset. While previous analyses have used inter-subject correlation to examine neural synchronization during the reception of informative public speaking, this dataset can be utilized for a variety of analyses to further elucidate the neural mechanisms underlying audience engagement and effective communication.

Neuroticism modulates functional connectivity of the midcingulate cortex during emotional conflict

Neuroticism (NT) is a fundamental personality trait and a major risk factor for both the onset and persistence of depression and anxiety disorders. Although NT involves alterations in emotion-cognition interaction, its precise neural mechanism remains insufficiently understood. Leveraging the word-face Stroop task, we examined neural circuits engaged during emotional conflict using a relatively large sample that exhibited a wide range of NT levels. Generalized psychophysiological interaction (gPPI) analyses revealed that individuals with high NT were characterized by decreased functional connectivity between the anterior midcingulate cortex (aMCC) and both the left dorsolateral prefrontal cortex (dlPFC) and the left amygdala. None of these regions showed modulated brain activation by NT. Our findings suggest that the neural substrates of NT can be better characterized by reduced top-down aMCC-amygdala regulation as well as inefficient communication within the dorsal cognitive system (aMCC-dlPFC), rather than changes in brain activation in isolated regions. These observations offer valuable insights into the neural markers of vulnerability to mood and anxiety disorders.

A neurocomputational account of the link between social perception and social action

People selectively help others based on perceptions of their merit or need. Here, we develop a neurocomputational account of how these social perceptions translate into social choice. Using a novel fMRI social perception task, we show that both merit and need perceptions recruited the brain's social inference network. A behavioral computational model identified two non-exclusive mechanisms underlying variance in social perceptions: a consistent tendency to perceive others as meritorious/needy (bias) and a propensity to sample and integrate normative evidence distinguishing high from low merit/need in other people (sensitivity). Variance in people's merit (but not need) bias and sensitivity independently predicted distinct aspects of altruism in a social choice task completed months later. An individual's merit bias predicted context-independent variance in people's overall other-regard during altruistic choice, biasing people toward prosocial actions. An individual's merit sensitivity predicted context-sensitive discrimination in generosity toward high and low merit recipients by influencing other- and self-regard during altruistic decision-making. This context-sensitive perception-action link was associated with activation in the right temporoparietal junction. Together, these findings point toward stable, biologically based individual differences in perceptual processes related to abstract social concepts like merit, and suggest that these differences may have important behavioral implications for an individual's tendency toward favoritism or discrimination in social settings.

The Beholder's Share: Bridging art and neuroscience to study individual differences in subjective experience

Our experience of the world is inherently subjective, shaped by individual history, knowledge, and perspective. Art offers a framework within which this subjectivity is practiced and promoted, inviting viewers to engage in interpretation. According to art theory, different forms of art-ranging from the representational to the abstract-challenge these interpretive processes in different ways. Yet, much remains unknown about how art is subjectively interpreted. In this study, we sought to elucidate the neural and cognitive mechanisms that underlie the subjective interpretation of art. Using brain imaging and written descriptions, we quantified individual variability in responses to paintings by the same artists, contrasting figurative and abstract paintings. Our findings revealed that abstract art elicited greater interindividual variability in activity within higher-order, associative brain areas, particularly those comprising the default-mode network. By contrast, no such differences were found in early visual areas, suggesting that subjective variability arises from higher cognitive processes rather than differences in sensory processing. These findings provide insight into how the brain engages with and perceives different forms of art and imbues it with subjective interpretation.

Neural representation of allegedly sex-specific human body odor compounds

Body odors play an important role in nonverbal communication, and particularly in one's attractiveness. However, their central processing remains underexplored, especially as a function of gender. This study aims at identifying the neural networks involved in the processing of two allegedly sex-specific human body odor compounds (3-hydroxy-3-methylhexanoic acid, HMHA, and 3-methyl-3-sulfanylhexan-1-ol, MSH). We hypothesized that i) these body odors would be processed by different brain regions than non-body odors, and that ii) their role in attractiveness, if any, would be indicated by the activation of specific regions and by differential verbal and neurophysiological responses in men and women. Thirty participants (15 men, 15 women) performed a functional Magnetic Resonance Imaging (fMRI) session during which they rated the attractiveness of HMHA, MSH, and 5 non-body odorants. At the end of the session, participants rated all odors on multiple perceptual scales. HMHA activated visual (striate area and occipital gyrus) rather than olfactory brain regions. Men rated HMHA as more masculine than women did, and presented greater neural activity in the superior and medial frontal gyri while women activated the inferior frontal gyrus significantly more than men in response to this odor. MSH was processed as the other non-body odors, and not subject to gender differences. The results suggest that HMHA (not MSH) bears specific social information, resulting in a neural processing outside the main olfactory network. It is also processed differently in men and women, although our findings do not provide clear evidence in favor of relevance for one's attractiveness.

Fingerprint patterns of human brain activity reveal a dynamic mix of emotional responses during virtual intergroup encounters

The Stereotype Content Model (SCM) states that different social groups elicit different emotions according to their perceived level of competence and warmth. Because of this relationship between stereotypes and emotional states and because emotions are highly predictive of intergroup behaviors, emotional evaluation is crucial for research on intergroup relations. However, emotional assessment heavily relies on self-reports, which are often compromised by social desirability and challenges in reporting immediate emotional appraisals. In this study, we used machine learning to identify emotional brain patterns using functional magnetic resonance imaging. Subsequently, those patterns were used to monitor emotional reactions during virtual intergroup encounters. Specifically, we showed Finnish majority group members 360-videos depicting members of their ethnic ingroup and immigrant outgroups approaching and entering participants' personal space. All the groups showed different levels of perceived competence and warmth. In alignment with the SCM, our results showed that the groups perceived as low in competence and warmth evoked contempt and discomfort. Moreover, the ambivalent low-competent/high-warm group elicited both happiness and discomfort. Additionally, upon the protagonists' approach into personal space, emotional reactions were modulated differently for each group. Taken together, our findings suggest that our method could be used to explore the temporal dynamics of emotional responses during intergroup encounters.

Unveiling the neural network involved in mentally projecting the self through episodic autobiographical memories

Episodic autobiographical memory involves the ability to travel along the mental timeline, so that events of our own life can be recollected and re-experienced. In the present study, we tested the neural underpinnings of mental travel across past and future autobiographical events by using a spatiotemporal interference task. Participants were instructed to mentally travel across past and future personal (Episodic Autobiographical Memories; EAMs) and Public Events (PEs) during Functional Magnetic Resonance Imaging (fMRI). We found that a distributed network of brain regions (i.e., occipital, temporal, parietal, frontal, and subcortical regions) is implicated in mental projection across past and future independently from the memory category (EAMs or PEs). Interestingly, we observed that most of these regions exhibited a neural modulation as a function of the lifetime period and/or as a function of the compatibility with a back-to-front mental timeline, specifically for EAMs, indicating the key role of these regions in representing the temporal organization of personal but not public events. Present findings provide insights into how personal events are temporally organized within the human brain.

Decoding Anxiety and/or Depressive Status in Functional Constipation: Insights From Surface-Based Functional-Structural Coupling Analysis

BACKGROUND

While patients with functional constipation (FC) are more susceptible to psychiatric issues such as anxiety and depression, the mechanism underlying gut-brain interactions remains elusive.

METHODS

This study included 39 FC patients with anxiety/depressive status (FCAD), 32 FC patients without anxiety/depressive status (FCNAD), and 42 healthy controls. Participants underwent clinical examinations and MRI scans, and changes in functional-structural coupling were assessed using surface-based regional homogeneity and cortical thickness. Receiver operating characteristic (ROC) curve analyses were performed to assess the predictive value of these changes.

KEY RESULTS

Abnormal coupling changes were exclusively observed in the FCAD group at both global and regional levels, primarily including significantly decreased coupling indices in the left hemisphere and regions within the bilateral visual cortex, left dorsolateral prefrontal cortex, and left posterior cingulate cortex. The FCAD and FCNAD groups were compared and analyzed using ROC curves, which revealed that coupling ratios in the bilateral visual cortex yielded higher predictive accuracy. Specifically, in the 12th sub-region of the left hemisphere, the coupling ratio achieved a sensitivity of 71.9% and a specificity of 74.4%. Meanwhile, the 8th sub-region of the right hemisphere showed a sensitivity of 78.1% and a specificity of 71.8%.

CONCLUSIONS AND INFERENCES

These results collectively highlighted asymmetric hemispheric decoupling and impairments in brain regions associated with visual and default mode networks in FCAD patients. These findings offer novel insights into the neurophysiological mechanisms underlying FCAD and may inform the development of more personalized treatment approaches.

Encoding-related hippocampus connectivity for scenes, faces, and words: Healthy people compared to people with temporal and frontal lobe epilepsy

Interactions of the hippocampus with other brain structures are supposed to support memory formation but knowledge is limited regarding hippocampal task-based functional connectivity (FC) during encoding in both healthy people and people with epilepsy, who frequently have impaired memory. We compared absolute [FC(encoding)] and relative FC (isolating task-specific FC [FC(encoding)-FC(baseline)]) of the anterior hippocampus in 30 controls, 56 mesial temporal (mTLE, 26 right) and 24 frontal lobe epilepsy (FLE) patients using a memory fMRI-task of encoding scenes, faces and words. In controls, absolute hippocampus FC comprised regions typically active in memory fMRI-tasks and the default mode network (DMN): For faces and scenes, FC was pronounced to temporo-occipital areas, whereas for words it extended to lateral-temporal regions. Relative FC was more circumscribed and encompassed temporo-occipital and frontal stimulus-selective regions for scenes and faces. Also, relative FC revealed weaker hippocampus - DMN connectivity during encoding. mTLE patients had decreased FC from the epileptogenic hippocampus and slight disruptions from the contralateral hippocampus. Decreased absolute FC was found to the contralateral mTL, the precuneus and the posterior cingulate gyrus. Further, mTLE patients' weaker FC to frontal and temporo-occipital regions reflected material-specific changes. Conversely, mTLE patients had higher absolute FC to regions to which the hippocampus is normally anticorrelated and increased relative FC to DMN regions. During word encoding only, FLE patients had increased left hippocampal relative FC to right-sided regions. Together, these findings further delineate the network architecture of memory in healthy people and its dysfunction in focal epilepsies, which prospectively could inform surgical interventions.

Dorsal-Ventral Reinforcement Learning Network Connectivity and Incentive-Driven Changes in Exploration

Probabilistic reinforcement learning (RL) tasks assay how individuals make decisions under uncertainty. The use of internal models (model-based) or direct learning from experiences (model-free), and the degree of choice stochasticity across alternatives (i.e., exploration), can all be influenced by the state space of the decision-making task. There is considerable individual variation in the balance between model-based and model-free control during decision-making, and this balance is affected by incentive motivation. The effect of variable reward incentives on the arbitration between model-based and model-free learning remains understudied, and individual differences in neural signatures and cognitive traits that moderate the effect of reward on model-free/model-based control are unknown. Here we combined a two-stage decision-making task utilizing differing reward incentives with computational modeling, neuropsychological tests, and neuroimaging to address these questions. Results showed the prospect of greater reward decreased exploration of alternative options and increased the balance toward model-based learning. These behavioral effects were replicated across two independent datasets including both sexes. Individual differences in processing speed and analytical thinking style affected how reward altered the dependence on both systems. Using a systems neuroscience-inspired approach to resting-state functional connectivity, we found reduced exploration of the options during the first stage of our task under high relative to low incentives was predicted by increased cross-network coupling between ventral and dorsal RL circuitry. These findings suggest that integrity of functional connections between stimulus valuation (ventral) and action valuation (dorsal) RL networks is associated with changes in the balance between explore-exploit decisions under changing reward incentives.

Altered integrated and segregated states in cocaine use disorder

Introduction

Cocaine use disorder (CUD) is a chronic brain condition that severely impairs cognitive function and behavioral control. The neural mechanisms underlying CUD, particularly its impact on brain integration-segregation dynamics, remain unclear.

Methods

In this study, we integrate dynamic functional connectivity and graph theory to compare the brain state properties of healthy controls and CUD patients.

Results

We find that CUD influences both integrated and segregated states, leading to distinct alterations in connectivity patterns and network properties. CUD disrupts connectivity involving the default mode network, frontoparietal network, and subcortical structures. In addition, integrated states show distinct sensorimotor connectivity alterations, while segregated states exhibit significant alterations in frontoparietal-subcortical connectivity. Regional connectivity alterations among both states are significantly associated with MOR and H3 receptor distributions, with integrated states showing more receptor-connectivity couplings. Furthermore, CUD alters the positive-negative correlation balance, increases functional complexity at threshold 0, and reduces mean betweenness centrality and modularity in the critical subnetworks. Segregated states in CUD exhibit lower normalized clustering coefficients and functional complexity at a threshold of 0.3. We also identify network properties in integrated states that are reliably correlated with cocaine consumption patterns.

Discussion

Our findings reveal temporal effects of CUD on brain integration and segregation, providing novel insights into the dynamic neural mechanisms underlying cocaine addiction.

Functional network reorganization and memory impairment in unruptured brain arteriovenous malformations

Background

Brain arteriovenous malformations (AVMs) are congenital vascular anomalies that can affect cognitive, particularly memory functions. However, the underlying mechanisms of neurocognitive abnormalities in unruptured AVMs remain unclear. This study aimed to explore spontaneous functional network reorganization associated with memory impairment in unruptured AVM patients using resting-state functional MRI (rsfMRI).

Methods

Using rsfMRI data, we compared functional activity and connectivity patterns between 25 AVM patients and healthy controls, including regional homogeneity (ReHo), fractional amplitude of low-frequency fluctuations (fALFF), seed-based functional connectivity (FC), and lesion network mapping. Correlation analysis was performed to clarify the relationship between these parameters and memory performance in AVM patients.

Results

We identified memory-related spontaneous functional network reorganization in AVM patients, particularly involving the somatomotor network (SMN), frontoparietal control network (FPN), and default mode network (DMN). Subgroup analyses based on lesion location (frontal vs. non-frontal) and laterality (left vs. right) revealed location-dependent differences in connectivity reorganization. In particular, left-sided AVMs showed disrupted FC within the SMN, correlated with working memory and executive function, while right-sided and frontal AVMs exhibited more complex patterns involving multiple networks. Moreover, functional disconnection maps indicated that AVM lesions did not directly impair resting-state memory networks.

Conclusion

Patients with unruptured AVMs exhibit resting-state memory network reorganization, which is closely related to the lesion location. These findings highlight the functional network alterations in AVM patients and suggest the potential neural mechanisms underlying memory deficits.

Clinical and MRI features contributing to the clinico-radiological dissociation in a large cohort of people with multiple sclerosis

BACKGROUND

People with Multiple Sclerosis (PwMS) often show a mismatch between disability and T2-hyperintense white matter (WM) lesion volume (LV), that in general is referred to as the clinico-radiological paradox.

OBJECTIVES

This study aimed to understand how an extensive clinical, neuropsychological, and MRI analysis could better elucidate the clinico-radiological dissociation in a large cohort of PwMS.

METHODS

Clinical scores, such as Expanded Disability Status Scale (EDSS), 9 Hole Peg Test (9HPT), 25-foot Walking Test (25-FWT), Paced Auditory Serial Addition Test at 3 s (PASAT3), Symbol digit Modalities Test (SDMT), demographics, and 3 T-MRI of 717 PwMS and 284 healthy subjects (HS) were downloaded from the INNI database. Considering medians of LV and EDSS scores, PwMS were divided into four groups: low LV and disability (LL/LD); high LV and low disability (HL/LD); low LV and high disability (LL/HD); high LV and disability (HL/HD). MRI measures included: volumes of gray matter (GM), WM, cerebellum, basal ganglia and thalamus, spinal cord (SC) area, and functional connectivity of resting-state networks.

RESULTS

The clinico-radiological dissociation involved 36% of our sample. HL/LD showed worse SDMT scores and lower global and deep GM volumes than HS and LL/LD. LL/HD showed lower GM, thalamus, and cerebellum volumes, and SC area than HS, and lower SC area than LL/LD.

CONCLUSIONS

A more extensive clinical assessment, including cognitive tests, and MRI evaluation including deep GM and SC, could better describe the real status of the disease and help clinicians in early and tailored treatment in PwMS.

The precision of hippocampal representations predicts incremental value-learning across the adult lifespan

Correctly assigning value to different options and leveraging this information to guide choice is a cornerstone of adaptive decision-making. Reinforcement learning (RL) has provided a computational framework to study this process, and neural signals linked to RL have been identified in the striatum and medial prefrontal cortex. More recently, hippocampal contributions to this kind of value-learning have been proposed, at least under some conditions. Here, we test whether the hippocampus provides a signal of the option's identity that aids in credit assignment when learning about several perceptually similar items, and evaluate how this process differs across the lifespan. A sample of 251 younger and older adults, including a subset (n = 76) with simultaneous fMRI, completed an RL task in which they learned the value of four houses through trial-and-error. Older adults showed decreased choice accuracy, accompanied by reduced neural signaling of value at choice but not feedback. Using representational similarity analysis, we found that the precision with which choice options were represented in the posterior hippocampus during choice predicted accurate decisions across age groups. Interestingly, despite previous evidence for neural de-differentiation in older adults, we found no support for a "blurring" of these stimulus representations in older adults. Rather, we observed reduced connectivity between the posterior hippocampus and the medial PFC in older adults, and this connectivity correlated with choice consistency. Taken together, these findings identify a hippocampal contribution to incremental value learning, and that reductions in incremental value learning in older adults are associated with the reduced transfer of information between the hippocampus and mPFC, rather than the precision of the information in the hippocampus itself.

Reflex "toothbrushing" epilepsy: Seizure freedom after focal ablation assisted by ictal fMRI

A 22-year-old female presented with drug-resistant focal motor seizures with onset at age 14. This manifested as daily episodes of right facial dystonia triggered by toothbrushing, but also by eating, talking, and strenuous exercise. On ictal scalp EEG, there was low-voltage fast activity over the left pericentral area. Structural MRI did not identify a definite lesion. Functional MRI (fMRI) of a reflex seizure, as well as task-based fMRI during toothbrushing, both demonstrated focal activation at the left low pericentral cortex. Stereoelectroencephalography (sEEG) showed recurrent ictal trains of focal spiking concordant with the fMRI activation. Radiofrequency (RF) thermocoagulation was applied at the posterior bank of the left low pre-central gyrus, with post-operative MRI confirming small ablative lesions immediately deep to the ictal fMRI activation, and the patient remains seizure-free more than 3 years after this treatment. Toothbrushing epilepsy is a rare form of reflex epilepsy where seizures are induced by toothbrushing. In this unique case, ictal fMRI assisted targeting of the sEEG implantation, to confirm seizure onset and enable minimally invasive treatment via RF thermocoagulation, resulting in seizure freedom.

Brain Structural and Functional Neuroimaging Features are Associated With Improved Auditory Hallucinations in Patients With Schizophrenia After Real-Time fMRI Neurofeedback

Auditory hallucinations (AHs) are debilitating and often treatment-resistant symptoms of schizophrenia (SZ). Real-time functional magnetic resonance imaging (fMRI) neurofeedback (NFB) is emerging as a flexible brain circuit-based tool for targeting AH via self-modulation of brain activity. A better understanding of what baseline characteristics predict NFB success will enhance its clinical utility. Previous work suggests that AH symptomology implicates measures across multiple modalities, including T1 structural MRI (sMRI), diffusion-weighted MRI (dMRI), and resting-state fMRI (rsfMRI). Specifically, AH severity and treatment response are associated with thinner superior temporal gyrus (STG), thinner dorsolateral prefrontal cortex (DLPFC), reduced white matter integrity in tracts connecting brain regions implicated in SZ symptomatology, increased within-default mode network (DMN) connectivity, and reduced DMN-DLPFC anticorrelation. In this study, we tested the individual and combined contributions of multimodal brain features for the prediction of AH change after NFB in adults (N = 25, 36.1 ± 10.0 years, 24% females) with SZ spectrum disorders (SZ or schizoaffective disorder) and frequent medication-resistant AH. Participants underwent a baseline MRI scan (including sMRI, dMRI, and rsfMRI) and were randomly assigned to receive NFB from their STG (n = 12, real condition) or NFB from their motor cortex (MC) (n = 13, sham condition). NFB success was operationalized as the improvement in AH severity after NFB. We found that higher baseline AH severity, greater STG thickness, decreased dorsal cingulum integrity, increased within-DMN resting-state functional connectivity, and increased DMN-DLPFC anticorrelation were each individually correlated with reduction in AH severity. However, in a combined regression model, DMN-DLPFC connectivity emerged as the only independent variable that explained the unique variance in AH change. These results suggest that a specific rsfMRI measure, namely DMN-DLPFC connectivity, may be a promising predictor of NFB success in reducing AH and support the precision medicine approach. Trial Registration: ClinicalTrials.gov identifier: NCT03504579.

Context modulates brain state dynamics and behavioral responses during narrative comprehension

Narrative comprehension is inherently context-sensitive, yet the brain and cognitive mechanisms by which brief contextual priming shapes story interpretation remain unclear. Using hidden Markov modeling (HMM) of fMRI data, we identified dynamic brain states as participants listened to an ambiguous spoken story under two distinct narrative contexts (affair vs. paranoia). We uncovered both context-invariant states-engaging auditory, language, and default mode networks-and context-specific states characterized by differential recruitment of control, salience, and visual networks. Narrative context selectively modulated the influence of character speech and linguistic features on brain state expression, with the central character's speech enhancing activation in shared states but suppressing activation in context-specific ones. Independent behavioral analyses revealed parallel context-dependent effects, with character-driven features exerting strong, selectively modulated influences on participants' judgments of narrative evidence. These findings demonstrate that brief narrative priming actively reshapes brain state dynamics and feature sensitivity during story comprehension, revealing how context guides moment-by-moment interpretive processing in naturalistic settings.

Temporal interactions between neural proxies for memory recall, negative affect, and emotion regulation in major depression

Dysfunction in emotion regulation (ER) and autobiographical memory are components of major depressive disorder (MDD). However, little is known about how they mechanistically interact with mood disturbances in real time. Using machine learning-based neural signatures, we can quantify negative affect (NA), ER, and memory continuously to evaluate how these processes dynamically interact in MDD. Unmedicated individuals with MDD (N = 45) and healthy volunteers (HV; N = 38) completed a negative autobiographical memory functional magnetic resonance imaging task wherein they recalled, distanced from (an ER strategy), and immersed into memories. We used a negative affect signature (PINES) and an emotion regulation signature (ERS) to quantify moment-to-moment NA and ER. We then examined whether memory engagement, indexed by hippocampal activity, predicted subsequent change in PINES and ERS over time. During memory recall and immersion, greater hippocampal activity predicted increased PINES across groups. During distancing, greater hippocampal activity in HVs predicted increased ERS but not PINES. In MDD, greater hippocampal activity predicted increased PINES but not ERS. Findings suggest abnormalities in the real-time relationship between memory, NA, and ER in MDD. During distancing, as predicted, HVs showed an attenuation of the linkage between memory engagement and NA, and they had subsequent increases in ER following memory reactivation. In contrast, MDD was characterized by continued linkage between memory engagement and NA, without subsequent increases in ER. Deficits in engagement of ER and ineffective modulation of NA following negative memory recall may contribute to the mood disturbances in MDD and are potential targets for clinical intervention.

Intranasal oxytocin modulates brain activity during emotional processing in children with treatment resistant conduct problems

One of the most highly replicated neural correlates of Conduct Problems (CP) is amygdala hypoactivity to another person's fear. We recently reported that this correlate was only observed in boys with persistent CP (i.e. antisocial behaviour that persisted following a gold-standard psychological intervention), suggesting that amygdala hypoactivity to fear could be an important neural signature for treatment-resistant CP, and a putative target for future treatments. Potential treatment candidates include the oxytocin system, as this has been reported to modulate amygdala activity and social behaviour across species. Further, in adults with antisocial personality disorder, intranasal oxytocin improved facial emotion recognition for fearful and happy faces. However, to-date, no-one has studied whether intranasal oxytocin can normalise neural processing differences in children with CP. Twenty boys (mean age 9.85±1.26 years) with persistent CP underwent functional magnetic resonance imaging in a within-subject randomised control design to investigate whether, compared to placebo, a single-dose of intranasal oxytocin could 'shift' abnormal neural processing to fear. Oxytocin failed to reduce amygdala hypoactivity to fearful faces, but increased activation in the posterior cingulate cortex / precuneus to happy faces. These findings tentatively suggest that intranasal oxytocin may promote a more neurotypical profile in treatment-resistant CP children, therefore, supporting the merit of investigating oxytocin in further larger clinical studies in this population.

Somatosensory false feedback biases emotional ratings through interoceptive embodiment

Mismatches between perceived and veridical physiological signals during false feedback (FFB) can bias emotional judgements. Paradigms using auditory FFB suggest perceived changes in heart rate (HR) increase ratings of emotional intensity irrespective of feedback type (increased or decreased HR), implicating right anterior insula as a mismatch comparator between exteroceptive and interoceptive information. However, few paradigms have examined effects of somatosensory FFB. Participants rated the emotional intensity of randomized facial expressions while they received 20 s blocks of pulsatile somatosensory stimulation at rates higher than HR, lower than HR, equivalent to HR, or no stimulation during a functional magnetic resonance neuroimaging scan. FFB exerted a bidirectional effect on reported intensity ratings of the emotional faces, increasing over the course of each 20 s stimulation block. Neuroimaging showed FFB engaging regions indicative of affective touch processing, embodiment, and reflex suppression. Contrasting higher vs. lower HR FFB revealed engagement of right insula and centres supporting socio-emotional processing. Results indicate that exposure to pulsatile somatosensory stimulation can influence emotional judgements though its progressive embodiment as a perceived interoceptive arousal state, biasing how affective salience is ascribed to external stimuli. Results are consistent with multimodal integration of priors and prediction-error signalling in shaping perceptual judgments.

Neural Reinforcement Learning Signals Predict Recovery From Impulse Control Disorder Symptoms in Parkinson's Disease

BACKGROUND

Impulse control disorders (ICDs) in Parkinson's disease are associated with a heavy burden on patients and caretakers. While recovery can occur, ICDs persist in many patients despite optimal management. The basis for this interindividual variability in recovery is unclear and poses a major challenge to personalized health care.

METHODS

We adopted a computational psychiatry approach and leveraged the longitudinal, prospective Personalized Parkinson Project (136 people with Parkinson's disease, within 5 years of diagnosis) to combine dopaminergic learning theory-informed functional magnetic resonance imaging with machine learning (at baseline) to predict ICD symptom recovery after 2 years of follow-up. We focused on change in Questionnaire for Impulsive-Compulsive Disorders in Parkinson's Disease Rating Scale scores in the entire sample regardless of an ICD diagnosis.

RESULTS

Greater reinforcement learning signals during gain trials but not loss trials at baseline, including those in the ventral striatum and medial prefrontal cortex, and the behavioral accuracy score measured while on medication were associated with greater recovery from impulse control symptoms 2 years later. These signals accounted for a unique proportion of the relevant variability over and above that explained by other known factors, such as decreases in dopamine agonist use.

CONCLUSIONS

Our results provide a proof of principle for combining generative model-based inference of latent learning processes with machine learning-based predictive modeling of variability in clinical symptom recovery trajectories. We showed that reinforcement learning modeling parameters predicted recovery from ICD symptoms in Parkinson's disease.

Abnormal sensory processing cortex in insomnia disorder: a degree centrality study

Insomnia disorder is a significant global health concern. This research aimed to explore the pathogenesis of insomnia disorder using static and dynamic degree centrality methods at the voxel level. A total of 29 patients diagnosed with insomnia disorder and 28 healthy controls were ultimately included to examine differences in degree centrality between the two groups. Additionally, the relationship between altered degree centrality values and various clinical indicators was analyzed. The results revealed that patients with insomnia disorder exhibited higher static degree centrality in brain regions associated with sensory processing, such as the occipital gyrus, inferior temporal gyrus, and supramarginal gyrus. In contrast, lower static degree centrality was observed in the parahippocampal gyrus, amygdala, insula, and thalamus. Changes in dynamic degree centrality were identified in regions including the parahippocampal gyrus, anterior cingulum, medial superior frontal gyrus, inferior parietal gyrus, and precuneus. Notably, a negative correlation was found between dynamic degree centrality in the inferior parietal gyrus and the Pittsburgh Sleep Quality Index, while a positive correlation was observed between static degree centrality in the inferior temporal gyrus and the Hamilton Depression Scale. These findings suggest that dysfunction in centrality within the sensory processing cortex and subcortical nuclei may be associated with the sleep-wake imbalance in individuals with insomnia disorder, contributing to our understanding of hyperarousal mechanisms in insomnia. Moreover, the abnormalities observed in the default mode network and the salience network provide insights into understanding the neuropathogenesis of insomnia from both static and dynamic centrality perspectives. The clinical trial registration number: ChiCTR2200058768. Date: 2022-04-16.

Network segregation during episodic memory shows age-invariant relations with memory performance from 7 to 82 years

Lower episodic memory capability, as seen in development and aging compared with younger adulthood, may partly depend on lower brain network segregation. Here, our objective was twofold: (1) test this hypothesis using within- and between-network functional connectivity (FC) during episodic memory encoding and retrieval, in two independent samples (n = 734, age 7-82 years). (2) Assess associations with age and the ability to predict memory comparing task-general FC and memory-modulated FC. In a multiverse-inspired approach, we performed tests across multiple analytic choices. Results showed that relationships differed based on these analytic choices and were mainly present in the largest dataset,. Significant relationships indicated that (i) memory-modulated FC predicted memory performance and associated with memory in an age-invariant manner. (ii) In line with the so-called neural dedifferentiation view, task-general FC showed lower segregation with higher age in adults which was associated with worse memory performance. In development, although there were only weak signs of a neural differentiation, that is, gradually higher segregation with higher age, we observed similar lower segregation-worse memory relationships. This age-invariant relationships between FC and episodic memory suggest that network segregation is pivotal for memory across the healthy lifespan.

Associations Between Neighborhood Resources and Youths' Response to Reward Omission in a Task Modeling Negatively Biased Environments

OBJECTIVE

Neighborhoods provide essential resources (eg, education, safe housing, green space) that influence neurodevelopment and mental health. However, we need a clearer understanding of the mechanisms mediating these relationships. Limited access to neighborhood resources may hinder youths from achieving their goals and, over time, shape their behavioral and neurobiological response to negatively biased environments blocking goals and rewards.

METHOD

To test this hypothesis, 211 youths (aged ∼13.0 years, 48% boys, 62% identifying as White, 75% with a psychiatric disorder diagnosis) performed a task during functional magnetic resonance imaging. Initially, rewards depended on performance (unbiased condition); but later, rewards were randomly withheld under the pretense that youths did not perform adequately (negatively biased condition), a manipulation that elicits frustration, sadness, and a broad response in neural networks. We investigated associations between the Childhood Opportunity Index (COI), which quantifies access to youth-relevant neighborhood features in 1 metric, and the multimodal response to the negatively biased condition, controlling for age, sex, medication, and psychopathology.

RESULTS

Youths from less-resourced neighborhoods responded with less anger (p < .001, marginal R2 = 0.42) and more sadness (p < .001, marginal R2 = 0.46) to the negatively biased condition than youths from well-resourced neighborhoods. On the neurobiological level, lower COI scores were associated with a more localized processing mode (p = .039, marginal R2 = 0.076), reduced connectivity between the somatic-motor-salience and the control network (p = .041, marginal R2 = 0.040), and fewer provincial hubs in the somatic-motor-salience, control, and default mode networks (all pFWE < .05).

CONCLUSION

The present study adds to a growing literature documenting how inequity may affect the brain and emotions in youths. Future work should test whether findings generalize to more diverse samples and should explore effects on neurodevelopmental trajectories and emerging mood disorders during adolescence.

PLAIN LANGUAGE SUMMARY

A growing body of literature suggests that access to resources at the neighborhood level affects the neurodevelopment and mental health of youth. This study explores how access to neighborhood resources shapes the behavioral and neurobiological responses to negatively biased environments in youth. During brain imaging, 211 youth participated in a task where rewards were randomly withheld under the pretense that the youth performed poorly, an "unfair" intervention that elicits frustration. The authors found that youth from less-resourced neighborhoods exhibited less anger and more sadness in response to the unfair condition compared to youth from well-resourced neighborhoods. Limited access to neighborhood resources was also associated with reduced connectivity between the control and motor brain networks. These findings suggest that neighborhood inequity may impact the neurodevelopment and mental health of youth.

DIVERSITY & INCLUSION STATEMENT

One or more of the authors of this paper self-identifies as a member of one or more historically underrepresented racial and/or ethnic groups in science. One or more of the authors of this paper self-identifies as a member of one or more historically underrepresented sexual and/or gender groups in science. One or more of the authors of this paper received support from a program designed to increase minority representation in science. We actively worked to promote sex and gender balance in our author group. We actively worked to promote inclusion of historically underrepresented racial and/or ethnic groups in science in our author group.

Sex, Neural Networks, and Behavioral Symptoms Among Adolescents With Multisite Pain

Importance

Multisite pain disproportionately affects females starting in adolescence and is associated with central nervous system dysregulation. Understanding the heterogeneity of underlying neural networks and behavioral symptoms is essential.

Objective

To characterize sex-related resting-state neural networks and co-occurring symptoms, including sleep and behavioral problems, in youth with multisite pain.

Design, Setting, and Participants

This cross-sectional analysis leverages the 2-year follow-up data from the Adolescent Brain and Cognitive Development Study. A total of 684 youth aged 11 to 12 years with multisite pain were compared with 1368 youth with no pain or with regional pain, matched by pubertal status, handedness, and race and ethnicity. Data were collected from July 2018 to February 2021 and released October 2021. Data were analyzed from June 2023 to July 2024.

Exposure

Youth-reported number of painful regions during the last month classified into multisite (≥3), regional (1-2), and no pain groups.

Main Outcomes and Measures

Sex-stratified group iterative multiple model estimation was used for sparse network estimation of regions from the salience network (SLN), sensorimotor network (SMN), and default mode network (DMN). Individual within-network and between-network densities were calculated. Symptoms were behavioral problems and sleep disturbances. Sex-stratified differences in network densities and symptoms were examined between groups. Associations between brain networks and co-occurring symptoms were explored.

Results

Of 2052 participants (1044 [50.88%] female), mean (SD) pubertal status was 2.23 (0.65) and mean (SD) age was 12.02 (0.66) years; 25 (1.22%) were Asian, 149 (7.26%) were Black, 361 (17.59%) were Hispanic, 1307 (63.69%) were White, and 210 (10.23%) were other race or ethnicity. A total of 1646 participants (80.21%) were right-handed, 100 (4.87%) were left-handed, and 306 (14.91%) were ambidextrous. Multisite pain was associated with lower within-SMN connectivity in male (F2,1005 = 61.40; η2 = 0.11; false discovery rate [FDR] P < .001) and female (F2,1041 = 13.38; η2 = 0.03; FDR P < .001) participants and was associated with greater behavioral problems in male (F2,918 = 28.12; η2 = 0.04; FDR P < .001) and female (F2,945 = 9.12; η2 = 0.02; FDR P < .001) participants compared with the subgroup with no pain. Male participants with multisite pain had heightened DMN-SMN connectivity (F2,1005 = 3.55; η2 = 0.007; FDR P = .04). Female participants with multisite pain had heightened sleep disturbances (F2,1039 = 10.64; η2 = 0.02; FDR P = .002), partially explained by reduced within-SMN connectivity (indirect effect estimate, 0.15; 95% CI, 0.03-0.34).

Conclusions and Relevance

In this cross-sectional study of 2052 adolescents, sex-related neurophysiological mechanisms were associated with multisite pain. Brain connectivity partially explained the sleep-pain association in female participants only. On replication and evidence of persistence, these findings suggest that female adolescents with pain may especially benefit from interventions targeting sleep disturbances.

Transcranial Magnetic Stimulation-Induced Plasticity Improving Cognitive Control in Obsessive-Compulsive Disorder, Part I: Clinical and Neuroimaging Outcomes From a Randomized Trial

BACKGROUND

Repetitive transcranial magnetic stimulation (rTMS) is an emerging treatment for obsessive-compulsive disorder (OCD). The neurobiological mechanisms of rTMS in OCD have been incompletely characterized. We compared clinical outcomes and changes in task-based brain activation following 3 different rTMS protocols, all combined with exposure and response prevention.

METHODS

In this 3-arm proof-of-concept randomized trial, 61 treatment-refractory adult patients with OCD received 16 sessions of rTMS immediately before exposure and response prevention over 8 weeks, with task-based functional magnetic resonance imaging scans and clinical assessments before and after treatment. Patients received high-frequency rTMS to the left dorsolateral prefrontal cortex (n = 19 [13 women/6 men]), high-frequency rTMS to the left presupplementary motor area (preSMA) (n = 23 [13 women/10 men]), or control rTMS to the vertex (n = 19 [13 women/6 men]). Changes in task-based functional magnetic resonance imaging activation before/after treatment were compared using both a Bayesian region of interest and a general linear model whole-brain approach.

RESULTS

Mean OCD symptom severity decreased significantly in all treatment groups (Δ = -10.836, p < .001, 95% CI -12.504 to -9.168), with no differences between groups. Response rate in the entire sample was 57.4%. The dorsolateral prefrontal cortex rTMS group showed decreased planning-related activation after treatment that was associated with greater symptom improvement. No group-level activation changes were observed for the preSMA and vertex rTMS groups. Participants in the preSMA group with greater symptom improvement showed decreased error-related activation, and symptom improvement in the vertex group was associated with increased inhibition-related activation.

CONCLUSIONS

rTMS to preSMA and dorsolateral prefrontal cortex combined with exposure and response prevention led to activation decreases in targeted task networks in individuals showing greater symptom improvement, although we observed no differences in symptom reduction between groups.

Trial-level Representational Similarity Analysis

Neural representation refers to the brain activity that stands in for one's cognitive experience, and in cognitive neuroscience, the principal method to studying neural representations is representational similarity analysis (RSA). The classic RSA (cRSA) approach examines the overall quality of representations across numerous items by assessing the correspondence between two representational similarity matrices (RSMs): one based on a theoretical model of stimulus similarity and the other based on similarity in measured neural data. However, because cRSA cannot model representation at the level of individual trials, it is fundamentally limited in its ability to assess subject-, stimulus-, and trial-level variances that all influence representation. Here, we formally introduce trial-level RSA (tRSA), an analytical framework that estimates the strength of neural representation for singular experimental trials and evaluates hypotheses using multi-level models. First, we verified the correspondence between tRSA and cRSA in quantifying the overall representation strength across all trials. Second, we compared the statistical inferences drawn from both approaches using simulated data that reflected a wide range of scenarios. Compared to cRSA, the multi-level framework of tRSA was both more theoretically appropriate and significantly sensitive to true effects. Third, using real fMRI datasets, we further demonstrated several issues with cRSA, to which tRSA was more robust. Finally, we presented some novel findings of neural representations that could only be assessed with tRSA and not cRSA. In summary, tRSA proves to be a robust and versatile analytical approach for cognitive neuroscience and beyond.

The effects of intermittent theta burst stimulation (iTBS) on resting-state brain entropy (BEN)

Intermittent theta burst stimulation (iTBS), a novel protocol within repetitive transcranial magnetic stimulation (rTMS), has shown superior therapeutic effects for depression compared to conventional high-frequency rTMS (HF-rTMS). However, the neural mechanisms underlying iTBS remain poorly understood. Brain entropy (BEN), a measure of the irregularity of brain activity, has recently emerged as a promising marker for regional brain function and has demonstrated sensitivity to depression and HF-rTMS. Given its potential, BEN may help elucidate the mechanisms of iTBS. In this study, we computed BEN using resting-state fMRI data from sixteen healthy participants obtained from OpenNeuro. Participants underwent iTBS over the left dorsolateral prefrontal cortex (L-DLPFC) at two different intensities (90 ​% and 120 ​% of resting motor threshold (rMT)) on separate days. We used a 2 ​× ​2 repeated measures analysis of variance (ANOVA) to analyze the interaction between iTBS stimulation intensity and the pre- vs. post-stimulation effects on BEN and paired sample t-tests to examine the specific BEN effects of iTBS at different intensities. Additionally, spatial correlation analysis was conducted to determine whether iTBS altered the baseline coupling between BEN and neurotransmitter receptors/transporters, to investigate potential neurotransmitter changes induced by iTBS. Our results indicate that subthreshold iTBS (90 ​% rMT) reduced striatal BEN, while suprathreshold iTBS (120 ​% rMT) increased it. Subthreshold iTBS led to changes in the baseline coupling between BEN and several neurotransmitter receptor/transporter maps, primarily involving serotonin (5-HT), cannabinoid (CB), acetylcholine (ACh), and glutamate (Glu). Our findings suggest that BEN is sensitive to the effects of iTBS, with different stimulation intensities having distinct effects on neural activity. Notably, subthreshold iTBS may offer more effective stimulation. This research highlights the crucial role of stimulation intensity in modulating brain activity and lays the groundwork for future clinical studies focused on optimizing therapeutic outcomes through precise stimulation intensity.

Dynamic and Static Structure-Function Coupling With Machine Learning for the Early Detection of Alzheimer's Disease

The progression of Alzheimer's disease (AD) involves complex changes in brain structure and function that are driven by their interaction, making structure-function coupling (SFC) a valuable indicator for early detection of AD. Static SFC refers to the overall structure-function interaction, whereas dynamic SFC refers to transient coupling variations. In this study, we aimed to assess the potential of combining static and dynamic SFC with machine learning (ML) for the early detection of AD. We analyzed a discovery cohort and an external validation cohort, including AD, mild cognitive impairment (MCI), and healthy control (HC) groups. Then, we quantified differences between static SFC and dynamic SFC at different stages of AD progression. Feature selection was performed using ElasticNet. A Gaussian naive Bayes (GNB) classifier was used to test the ability of SFC to classify AD stages. We also analyzed the correlations between SFC features and early AD physiological biomarkers. Static SFC increased with AD progression, whereas dynamic SFC showed greater variability and decreased stability. Using SFC features selected by ElasticNet, the GNB classifier achieved high performance in differentiating between the HC and MCI stages (area under the curve [AUC] = 91.1%) and between the MCI and AD stages (AUC = 89.03%). Significant correlations were found between SFC features and physiological biomarkers. The combined use of SFC features and ML has strong potential value for the accurate classification of AD stages and significant potential value for the early detection of AD. This study demonstrates that combining static and dynamic SFC with ML provides a novel perspective for understanding the mechanisms of AD and contributes to improving its early detection.

Depthwise cortical iron relates to functional connectivity and fluid cognition in healthy aging

Age-related differences in fluid cognition have been associated with both the merging of functional brain networks, defined from resting-state functional magnetic resonance imaging (rsfMRI), and with elevated cortical iron, assessed by quantitative susceptibility mapping (QSM). Limited information is available, however, regarding the depthwise profile of cortical iron and its potential relation to functional connectivity. Here, using an adult lifespan sample (n = 138; 18-80 years), we assessed relations among graph theoretical measures of functional connectivity, column-based depthwise measures of cortical iron, and fluid cognition (i.e., tests of memory, perceptual-motor speed, executive function). Increased age was related both to less segregated functional networks and to increased cortical iron, especially for superficial depths. Functional network segregation mediated age-related differences in memory, whereas depthwise iron mediated age-related differences in general fluid cognition. Lastly, higher mean parietal iron predicted lower network segregation for adults younger than 45 years of age. These findings suggest that functional connectivity and depthwise cortical iron have distinct, complementary roles in the relation between age and fluid cognition in healthy adults.

Generalizable and transportable resting-state neural signatures characterized by functional networks, neurotransmitters, and clinical symptoms in autism

Autism spectrum disorder (ASD) is a lifelong condition with elusive biological mechanisms. The complexity of factors, including inter-site and developmental differences, hinders the development of a generalizable neuroimaging classifier for ASD. Here, we developed a classifier for ASD using a large-scale, multisite resting-state fMRI dataset of 730 Japanese adults, aiming to capture neural signatures that reflect pathophysiology at the functional network level, neurotransmitters, and clinical symptoms of the autistic brain. Our adult ASD classifier was successfully generalized to adults in the United States, Belgium, and Japan. The classifier further demonstrated its successful transportability to children and adolescents. The classifier contained 141 functional connections (FCs) that were important for discriminating individuals with ASD from typically developing controls. These FCs and their terminal brain regions were associated with difficulties in social interaction and dopamine and serotonin, respectively. Finally, we mapped attention-deficit/hyperactivity disorder (ADHD), schizophrenia (SCZ), and major depressive disorder (MDD) onto the biological axis defined by the ASD classifier. ADHD and SCZ, but not MDD, were located proximate to ASD on the biological dimensions. Our results revealed functional signatures of the ASD brain, grounded in molecular characteristics and clinical symptoms, achieving generalizability and transportability applicable to the evaluation of the biological continuity of related diseases.

The Impact of Bullying Victimization on Short Video Addiction in Adolescents: The Role of Emotional Distress and Neural Mechanisms

Short-video addiction (SVA) has become a growing concern among adolescents. Bullying victimization (BV) is considered a significant factor contributing to it, yet its relationship with SVA remains underexplored. This study investigated the role of BV in SVA, examining developmental and psychological pathways across middle school students (MSS; n = 1269), college students (CS; n = 1615) and a replicated college sample (RCS; n = 112). Descriptive statistics revealed significant correlations between SVA and BV, including subdimensions such as verbal, physical and relational bullying, as well as negative affect (NA). Mediation analyses showed that NA partially mediated the relationship between BV and SVA across both MSS and CS groups, although mediation effects were absent in addicted subgroups, highlighting differing psychological pathways between addicted and nonaddicted populations. Neuroimaging analyses in the RCS sample identified spontaneous functional brain activity linked to SVA in the inferior temporal gyrus (ITG) and parahippocampal gyrus (PHG), with intersubject representational similarity analyses (IS-RSA) further associating PHG and dorsomedial prefrontal cortex (DMPFC) activity patterns with intersubject variations in SVA. These findings underscore bullying victimization as a critical predictor of short video addiction, mediated by NA in nonaddicted groups, and illuminate spontaneous brain activity patterns associated with addiction.

Default Mode Network Functional Connectivity As a Transdiagnostic Biomarker of Cognitive Function

The default mode network (DMN) is intricately linked with processes such as self-referential thinking, episodic memory recall, goal-directed cognition, self-projection, and theory of mind. In recent years, there has been a surge in the number of studies examining its functional connectivity, particularly its relationship with frontoparietal networks involved in top-down attention, executive function, and cognitive control. The fluidity in switching between these internal and external modes of processing, which is highlighted by anticorrelated functional connectivity, has been proposed as an indicator of cognitive health. Due to the ease of estimation of functional connectivity-based measures through resting-state functional magnetic resonance imaging paradigms, there is now a wealth of large-scale datasets, paving the way for standardized connectivity benchmarks. In this review, we explore the promising role of DMN connectivity metrics as potential biomarkers of cognitive state across attention, internal mentation, mind wandering, and meditation states and investigate deviations in trait-level measures across aging and in clinical conditions such as Alzheimer's disease, Parkinson's disease, depression, attention-deficit/hyperactivity disorder, and others. We also tackle the issue of reliability of network estimation and functional connectivity and share recommendations for using functional connectivity measures as a biomarker of cognitive health.

Task-based neural correlates of self-focused attention associated with cognitive behavioral therapy response

Self-focused attention (SFA), a form of self-referential processing, is maladaptive in various psychiatric disorders and may be associated with poor treatment response. This study examined SFA in individuals with social anxiety disorder (SAD) and body dysmorphic disorder (BDD), testing the hypothesis that SFA is associated with hyperactivity within default network (DN) regions and with treatment response during cognitive-behavioral therapy. Participants included 30 patients with primary SAD or BDD and 28 healthy controls, who displayed above average and below average scores (respectively) on the Public Self-Consciousness Scale, which measured trait SFA. SFA was also measured by a self-referential encoding task, which yielded both behavioral reaction time measures and task-related fMRI measures of SFA. Results indicated significantly longer reaction times at pre-treatment for self vs. other trials in patients compared to controls, with patients showing notable improvement post-treatment. Neuroimaging revealed greater activation in DN regions, including the medial prefrontal cortex, during self vs. other trials in all participants; however, there were no significant group differences at pre- or post-treatment, nor in the changes from pre- to post-treatment. Neural measures of SFA were significantly associated with treatment response, whereas behavioral measures were not. These findings suggest that activity in DN regions may serve as a transdiagnostic biomarker of maladaptive SFA that is associated with treatment response.

Reduced recruitment of inhibitory control regions in very young children with ADHD during a modified Kiddie Continuous Performance Task: A fMRI study

Attention-Deficit/Hyperactivity Disorder (ADHD) symptom profiles are known to undergo changes throughout development, rendering the neurobiological assessment of ADHD challenging across different developmental stages. Particularly in young children (ages 4- to 7-years), measuring inhibitory control network activity in the brain has been a formidable task due to the lack of child-friendly functional Magnetic Resonance Imaging (fMRI) paradigms. This study aims to address these difficulties by focusing on measuring inhibitory control in very young children within the MRI environment. A total of 56 children diagnosed with ADHD and 78 typically developing (TD) 4-7-year-old children were successfully examined using a modified version of the Kiddie-Continuous Performance Test (K-CPT) during BOLD fMRI to assess inhibitory control. We also evaluated their performance on the standardized K-CPT outside the MRI scanner. Our findings suggest that the modified K-CPT effectively elicited robust and expected brain activity related to inhibitory control in both groups who were successfully scanned. Comparisons between the two groups revealed differences in brain activity, primarily observed in inferior frontal gyrus, anterior insula, dorsal striatum, medial pre-supplementary motor area (pre-SMA), and cingulate cortex (p < .005, corrected). Notably, for both groups increased activity in the right anterior insula was associated with improved response time (RT) and reduced RT variability on the K-CPT administered outside the MRI environment, although this did not survive statistical correction for multiple comparisons. The study also revealed continuing challenges for scanning this population-an additional 51 TD children and 78 children with ADHD were scanned, but failed to provide useable data due to movement. In summary, for a subsample of children, we successfully overcame some of the challenges of measuring inhibitory control in very young children within the MRI environment by using a modified K-CPT during BOLD fMRI, but further challenges remain for scanning in this population. The findings shed light on the neurobiological correlates of inhibitory control in ADHD and TD children, provide valuable insights for understanding ADHD across development, and potentially inform ADHD diagnosis and intervention strategies. The research also highlights remaining challenges with task fMRI in very young clinical samples.

Qualitative and quantitative educational disparities and brain signatures in healthy aging and dementia across global settings

Background

While education is crucial for brain health, evidence mainly relies on individual measures of years of education (YoE), neglecting education quality (EQ). The effect of YoE and EQ on aging and dementia has not been compared.

Methods

We conducted a cross-sectional assessment of the effect of EQ and YoE on brain health in 7533 subjects from 20 countries, including healthy controls (HCs), Alzheimer's disease (AD), and frontotemporal lobar degeneration (FTLD). EQ was based on country-level quality indicators provided by the programme for international student assessment (PISA). After applying neuroimage harmonization, we examined its effect, along with YoE, on gray matter volume and functional connectivity. Regression models were adjusted for age, sex, and cognition, controlling for multiple comparisons. The influence of image quality was assessed through sensitivity analysis. Data collection was conducted between June 1 and October 30, 2024.

Findings

Less EQ and YoE were associated with brain alterations across groups. However, EQ had a stronger influence, mainly targeting the critical areas of each condition. At the whole-brain level, EQ influenced volume (HCs: Δmean = 2·0 [1·9-2·0] × 10-2, p < 10-5; AD: Δmean = 0·1 [-0·0 to 0·3] × 10-2, p = 0·18; FTLD: Δmean = 3·5 [3·0-4·0] × 10-2, p < 10-5; all with 95% confidence intervals) and networks (HCs: Δmean = 13·5 [13·2-13·7] × 10-2, p < 10-5; AD: Δmean = 5·9 [5·2-6·7] × 10-2, p < 10-5; FTLD: Δmean = 13·2 [11·2-13·7] × 10-2, p < 10-5) 1·3 to 7·0 times more than YoE. These effects remain robust despite variations in income and socioeconomic factors at country and individual levels.

Interpretation

The results support the need to incorporate education quality into studying and improving brain health, underscoring the importance of country-level measures.

Funding

Multi-partner consortium to expand dementia research in Latin America (ReDLat).

Anhedonia relates to reduced striatal reward anticipation in depression but not in schizophrenia or bipolar disorder: A transdiagnostic study

Anhedonia, i.e., the loss of pleasure or lack of reactivity to reward, is a core symptom of major psychiatric conditions. Altered reward processing in the striatum has been observed across mood and psychotic disorders, but whether anhedonia transdiagnostically contributes to these deficits remains unclear. We investigated associations between self-reported anhedonia and neural activation during reward anticipation and consumption across patients with schizophrenia (SZ), bipolar disorder (BD), major depressive disorder (MD), and healthy controls (HC). Using the Monetary Incentive Delay paradigm, we acquired functional magnetic resonance imaging data sets in 227 participants (18-65 years), including patients with SZ (n = 44), BD (n = 47), MD (n = 56), and HC (n = 80). To capture anhedonia, three items of the Symptom Checklist-90-R were entered into exploratory factor analysis, which resulted in a single anhedonia factor. Associations between anhedonia and neural activation were assessed within a striatal region-of-interest and exploratorily across the whole brain (pFWE < .05). Self-reported anhedonia was high in MD, low in HC, and intermediate in SZ and BD. During reward anticipation, anhedonia correlated with reduced striatal activation; however, the correlation depended on diagnostic group. Specifically, the effect was driven by a negative relationship between anhedonia and dorsal striatal (putamen) activity within the MD group; for reward consumption, no correlations were found. Our results indicate that anticipatory anhedonia in MD may relate to reduced behavioral motivation via disrupted encoding of motor plans in the dorsal striatum. Future transdiagnostic research should stratify participants by anhedonia levels to achieve more homogeneous samples in terms of underlying neurobiology.