fMRIPrep: a robust preprocessing pipeline for functional MRI

Functional brain network dynamics of brooding in depression: Insights from real-time fMRI neurofeedback

BACKGROUND

Brooding is a critical symptom and prognostic factor of major depressive disorder (MDD), which involves passively dwelling on self-referential dysphoria and related abstractions. The neurobiology of brooding remains under characterized. We aimed to elucidate neural dynamics underlying brooding, and explore their responses to neurofeedback intervention in MDD.

METHODS

We investigated functional MRI (fMRI) dynamic functional network connectivity (dFNC) in 36 MDD subjects and 26 healthy controls (HCs) during rest and brooding. Rest was measured before and after fMRI neurofeedback (MDD-active/sham: n = 18/18, HC-active/sham: n = 13/13). Baseline brooding severity was recorded using Ruminative Response Scale - Brooding subscale (RRS-B).

RESULTS

Four recurrent dFNC states were identified. Measures of time spent were not significantly different between MDD and HC for any of these states during brooding or rest. RRS-B scores in MDD showed significant negative correlation with measures of time spent in dFNC state 3 during brooding (r = -0.4, p = 0.002, FDR-significant). This state comprises strong connections spanning several brain systems involved in sensory, attentional and cognitive processing. Time spent in this anti-brooding dFNC state significantly increased following neurofeedback only in the MDD active group (z = -2.09, FWE-p = 0.034).

LIMITATIONS

The sample size was small and imbalanced between groups. Brooding condition was not examined post-neurofeedback.

CONCLUSION

We identified a densely connected anti-brooding dFNC brain state in MDD. MDD subjects spent significantly longer time in this state after active neurofeedback intervention, highlighting neurofeedback's potential for modulating dysfunctional brain dynamics to treat MDD.

Comprehensive evaluation of pipelines for classification of psychiatric disorders using multi-site resting-state fMRI datasets

Objective classification biomarkers that are developed using resting-state functional magnetic resonance imaging (rs-fMRI) data are expected to contribute to more effective treatment for psychiatric disorders. Unfortunately, no widely accepted biomarkers are available at present, partially because of the large variety of analysis pipelines for their development. In this study, we comprehensively evaluated analysis pipelines using a large-scale, multi-site fMRI dataset for major depressive disorder (MDD). We explored combinations of options in four sub-processes of the analysis pipelines: six types of brain parcellation, four types of functional connectivity (FC) estimations, three types of site-difference harmonization, and five types of machine-learning methods. A total of 360 different MDD classification biomarkers were constructed using the SRPBS dataset acquired with unified protocols (713 participants from four sites) as the discovery dataset, and datasets from other projects acquired with heterogeneous protocols (449 participants from four sites) were used for independent validation. We repeated the procedure after swapping the roles of the two datasets to identify superior pipelines, regardless of the discovery dataset. The classification results of the top 10 biomarkers showed high similarity, and weight similarity was observed between eight of the biomarkers, except for two that used both data-driven parcellation and FC computation. We applied the top 10 pipelines to the datasets of other psychiatric disorders (autism spectrum disorder and schizophrenia), and eight of the biomarkers exhibited sufficient classification performance for both disorders. Our results will be useful for establishing a standardized pipeline for classification biomarkers.

Brain network connectivity underlying neuropsychiatric symptoms in prodromal Lewy body dementia

Neuropsychiatric symptoms (NPS) are prevalent, emerge early, and are associated with poorer outcomes in Lewy body dementia (LBD). Research suggests NPS may reflect LBD-related dysfunction in distributed neuronal networks. This study investigated NPS neural correlates in prodromal LBD using resting-state functional MRI. Fifty-seven participants were included with mild cognitive impairment (MCI) with Lewy bodies (MCI-LB, n = 28) or Parkinson's disease (PD-MCI, n = 29). Functional MRI assessed connectivity within five resting-state networks: primary visual, dorsal attention, salience, limbic, and default mode networks. NPS were measured using the Neuropsychiatric Inventory. Principal component analyses identified three neuropsychiatric factors: affective disorder (apathy, depression), psychosis (delusions, hallucinations) and anxiety. Seed-to-voxel connectivity maps were analysed to determine associations between NPS and network connectivity. In PD-MCI, affective symptoms and anxiety were associated with greater connectivity between limbic orbitofrontal cortex and default mode areas, including medial prefrontal cortex, subgenual cingulate and precuneus, and weaker connectivity between limbic orbitofrontal cortex and the brainstem and between the salience network and medial prefrontal cortex (all pFWE<0.001). Psychosis severity in PD-MCI correlated with connectivity across multiple networks (all pFWE<0.001). In MCI-LB, no significant correlations were found between NPS severity and network connectivity. However, participants with anxiety demonstrated a trend towards greater connectivity within medial prefrontal areas than those without (pFWE=0.046). Altered connectivity within and between networks associated with mood disorders may explain affective and anxiety symptoms in PD-MCI. Neural correlates of NPS in MCI-LB, however, remain unclear, highlighting the need for research in larger, more diverse LBD populations to identify symptomatic treatment targets.

Neural dynamics of social anxiety during and after anxiety-provoking and relaxation-inducing: A task and resting-state fMRI study

BACKGROUND

Social anxiety disorder (SAD) is marked by intense fear of social situations and negative evaluation. This study investigated neural effects of SAD-specific imagery scripts and their relationships with Brief Fear of Negative Evaluation (BFNE).

METHODS

Thirty-six SAD and 32 healthy controls underwent four five-minute fMRI runs: anxiety-provoking imagery, rest, relaxing imagery, and rest. The order of imageries was counterbalanced. Functional connectivity analysis and connectome-based predictive modeling with respect to BFNE were performed using six seed regions, including the bilateral amygdala, left hypothalamus, bilateral dorsomedial prefrontal cortex (DMPFC), left ventromedial PFC (VMPFC), and left posterior cingulate cortex (PCC).

RESULTS

Group × task interaction effects were found in connectivity of left amygdala-right cerebellum, left PCC-bilateral superior frontal gyrus, and left PCC-right posterior middle temporal gyrus, and group × engagement effects were discovered in left hypothalamus-bilateral DMPFC and left VMPFC-right DMPFC couplings. Group × task × engagement interactions highlighted aberrant functional connections of right amygdala-left VMPFC, DMPFC-left DLPFC, and left VMPFC-bilateral supplementary motor area in SAD. Patterns of connectivity predicted the BFNE scores in various segments of imagery conditions.

LIMITATIONS

Patient's medication, physiological measures were not considered. Noisy nature of fMRI could have interfered participants from focusing.

CONCLUSIONS

Our results revealed disrupted functional connections associated with emotion dysregulation and overly self-referent thinking in SAD. Markedly, patients showed maladaptive responses related to relaxation-inducing blocks, challenging the expected relaxation response. Overall findings emphasized inappropriate engagements of various processes in relaxing circumstances that do not overtly involve social anxiety to be associated with symptomatology.

Altering subjective time perception leads to correlated changes in neural activity and delay discounting

Several accounts of delay discounting suggest that subjective time perception contributes to individually varying discount rates. That is, one may seem impatient if their subjective perception of delay is longer than others' perception of it. Here we build upon the behavioral and neural research on time perception, and we investigate the effects of manipulating an individual's subjective time perception on their discount rates and neural activity. Using a novel time-counting task, we found that participants' discount rates are affected by our manipulation of time perception and that neural activity also correlates with our manipulation in brain regions, such as the anterior insula and the superior temporal gyri, which have been implicated in time perception. We link these behavioral and neural findings together by showing that the degree of neural activity change in response to our manipulation is predictive of the degree of change in the participants' discount rates.

BOLD amplitude correlates of preclinical Alzheimer's disease

Alzheimer's disease (AD) is characterized by a long preclinical stage during which molecular markers of amyloid beta and tau pathology rise, but there is minimal neurodegeneration or cognitive decline. Previous literature suggests that measures of brain function might be more sensitive to neuropathologic burden during the preclinical stage of AD than conventional measures of macrostructure, such as cortical thickness. Among studies that used resting-state functional Magnetic Resonance Imaging (fMRI) acquisitions with Blood Oxygenation Level Dependent (BOLD) contrast, most employed connectivity-based analytic approaches. Consequently, little is known about the effects of amyloid and tau pathology on amplitude of intrinsic BOLD signal fluctuations. To address this knowledge gap, we characterized the effects of preclinical and prodromal AD on the amplitude of low-frequency fluctuations (ALFF) of the BOLD signal both at the whole-brain level and at a more granular level focused on subregions of the medial temporal lobe. We observed reduced ALFF in both preclinical and prodromal AD. In preclinical AD, amyloid positivity was associated with a spatially diffuse ALFF reduction in the frontal, medial parietal, and lateral temporal association cortices. In contrast, tau pathology was negatively associated with ALFF in the entorhinal cortex. These ALFF effects were observed in the absence of observable macrostructural changes in preclinical AD and remained after adjusting for structural atrophy in prodromal AD, indicating that ALFF offers additional sensitivity to early disease processes beyond what is provided by traditional structural imaging biomarkers of neurodegeneration. We conclude that ALFF may be a promising imaging-based biomarker in preclinical AD.

Disruption of macroscale functional network organisation in patients with frontotemporal dementia

Neurodegenerative dementias have a profound impact on higher-order cognitive and behavioural functions. Investigating macroscale functional networks through cortical gradients provides valuable insights into the neurodegenerative dementia process and overall brain function. This approach allows for the exploration of unimodal-multimodal differentiation and the intricate interplay between functional brain networks. We applied cortical gradients mapping to resting-state functional MRI data of patients with frontotemporal dementia (FTD) (behavioural-bvFTD, non-fluent and semantic) and healthy controls. In healthy controls, the principal gradient maximally distinguished sensorimotor from default-mode network (DMN) and the secondary gradient visual from salience network (SN). In all FTD variants, the principal gradient's unimodal-multimodal differentiation was disrupted. The secondary gradient, however, showed widespread disruptions impacting the interactions among all networks specifically in bvFTD, while semantic and non-fluent variants exhibited more focal alterations in limbic and sensorimotor networks. Additionally, the visual network showed responsive and/or compensatory changes in all patients. Importantly, these disruptions extended beyond atrophy distribution and related to symptomatology in patients with bvFTD. In conclusion, optimal brain function requires networks to operate in a segregated yet collaborative manner. In FTD, our findings indicate a collapse and loss of differentiation between networks not solely explained by atrophy. These specific cortical gradients' fingerprints could serve as a functional signature for identifying early changes in neurodegenerative diseases or potential compensatory processes.

Le Petit Prince (LPP) multi-talker: Naturalistic 7 T fMRI and EEG dataset

Prior neuroimaging datasets using naturalistic listening paradigms have predominantly focused on single-talker scenarios. While these studies have been invaluable for advancing our understanding of speech and language processing in the brain, they do not capture the complexities of real-world multi-talker environments. Here, we introduce the "Le Petit Prince (LPP) Multi-talker Dataset", a high-quality, naturalistic neuroimaging dataset featuring 40 minutes of electroencephalogram (EEG) and 7 T functional magnetic resonance imaging (fMRI) recordings from 26 native Mandarin Chinese speakers as they listened to both single-talker and multi-talker speech streams. Validation analyses conducted on both EEG and fMRI data demonstrate the dataset's high quality and robustness. Additionally, the dataset includes detailed transcriptions and prosodic and linguistic annotations of the speech stimuli, enabling fine-grained analyses of neural responses to specific linguistic and acoustic features. The LPP Multi-talker Dataset is well-suited for addressing a wide range of research questions in cognitive neuroscience, including selective attention, auditory stream segregation, and working memory processes in naturalistic listening contexts.

Microbiota alterations leading to amino acid deficiency contribute to depression in children and adolescents

BACKGROUND

Major depressive disorder (MDD) in children and adolescents is a growing global public health concern. Metabolic alterations in the microbiota-gut-brain (MGB) axis have been implicated in MDD pathophysiology, but their specific role in pediatric populations remains unclear.

RESULTS

We conducted a multi-omics study on 256 MDD patients and 307 healthy controls in children and adolescents, integrating plasma metabolomics, fecal metagenomics, and resting-state functional magnetic resonance imaging (rs-fMRI) of the brain. KEGG enrichment analysis of 360 differential expressed metabolites (DEMs) indicated significant plasma amino acid (AA) metabolism deficiencies (p-value < 0.0001). We identified 58 MDD-enriched and 46 MDD-depleted strains, as well as 6 altered modules in amino acid metabolism in fecal metagenomics. Procrustes analysis revealed the association between the altered gut microbiome and circulating AA metabolism (p-value = 0.001, M2 = 0.932). Causal analyses suggested that plasma AAs might mediate the impact of altered gut microbiota on depressive and anxious symptoms. Additionally, rs-fMRI revealed that connectivity deficits in the frontal lobe are associated with depression and 22 DEMs in AA metabolism. Furthermore, transplantation of fecal microbiota from MDD patients to adolescent rats induced depressive-like behaviors and 14 amino acids deficiency in the prefrontal cortex (PFC). Moreover, the dietary lysine restriction increased depression susceptibility in adolescent rats by reducing the expression of excitatory amino acid transporters in the PFC.

CONCLUSIONS

Our findings highlight that gut microbiota alterations contribute to AAs deficiency, particularly lysine, which plays a crucial role in MDD pathogenesis in children and adolescents. Targeting AA metabolism may offer novel therapeutic strategies for pediatric depression. Video Abstract.

How to measure functional connectivity using resting-state fMRI? A comprehensive empirical exploration of different connectivity metrics

BACKGROUND

Functional connectivity in the context of functional magnetic resonance imaging is typically quantified by Pearson´s or partial correlation between regional time series of the blood oxygenation level dependent signal. However, a recent interdisciplinary methodological work proposes >230 different metrics to measure similarity between different types of time series.

OBJECTIVE

Hence, we systematically evaluated how the results of typical research approaches in functional neuroimaging vary depending on the functional connectivity metric of choice. We further explored which metrics most accurately detect presumed reductions in connectivity related to age and malignant brain tumors, aiming to initiate a debate on the best approaches for assessing brain connectivity in functional neuroimaging research.

METHODS

We addressed both research questions using four independent neuroimaging datasets, comprising multimodal data from a total of 1187 individuals. We analyzed resting-state functional sequences to calculate functional connectivity using 20 representative metrics from four distinct mathematical domains. We further used T1- and T2-weighted images to compute regional brain volumes, diffusion-weighted imaging data to build structural connectomes, and pseudo-continuous arterial spin labeling to measure regional brain perfusion.

RESULTS

First, our findings demonstrate that the results of typical functional neuroimaging approaches differ fundamentally depending on the functional connectivity metric of choice. Second, we show that correlational and distance metrics are most appropriate to cover reductions in connectivity linked to aging. In this context, partial correlation performs worse than other correlational metrics. Third, our findings suggest that the FC metric of choice depends on the utilized scanning parameters, the regions of interest, and the individual investigated. Lastly, beyond the major objective of this study, we provide evidence in favor of brain perfusion measured via pseudo-continuous arterial spin labeling as a robust neural entity mirroring age-related neural and cognitive decline.

CONCLUSION

Our empirical evaluation supports a recent theoretical functional connectivity framework. Future functional imaging studies need to comprehensively define the study-specific theoretical property of interest, the methodological property to assess the theoretical property, and the confounding property that may bias the conclusions.

The relationships of resting-state brain entropy (BEN), ovarian hormones and behavioral inhibition and activation systems (BIS/BAS)

Brain entropy (BEN) quantifies irregularity, disorder and uncertainty of brain activity. Recent studies have linked BEN, derived from resting-state functional magnetic resonance imaging (rs-fMRI), to cognition, task activation, neuromodulation, and pharmacological interventions. However, it remains unknown whether BEN can reflect the effects of hormonal fluctuations. Furthermore, ovarian hormones are known to modulate behavioral traits, such as inhibitory control and impulsivity, as measured by the Behavioral Inhibition and Activation Systems (BIS/BAS). In this study, we investigated how ovarian hormones influence BEN and BIS/BAS in young adult women. The forty-four participants (mean age = 22.61 ± 2.14 years) were obtained from OpenNeuro in the study. Ovarian hormones including estradiol (E2), progesterone (PROG) and BIS/BAS were acquired before scanning. The voxel-wise BEN maps were calculated from the preprocessed rs-fMRI images. Pearson's correlation and mediation analyses were used to assess the relationships between BEN and ovarian hormones as well as BIS/BAS. Our results revealed a negative correlation between BEN and PROG in frontoparietal network (FPN), including the dorsolateral prefrontal cortex (DLPFC) and posterior parietal cortex (PPC), as well as in the limbic network, encompassing the amygdala, hippocampus, and parahippocampal cortex. In contrast, BEN showed a positive correlation with impulsivity traits measured by the BAS-drive subscale of BAS in the left DLPFC. Additionally, PROG was negatively correlated with impulsivity traits measured by BAS-drive. Results from mediation analysis demonstrated that PROG reduces impulsivity, as measured by BAS-drive, by decreasing BEN in the left DLPFC and subsequently increasing functional connectivity (FC) within this region. These findings provide the first evidence that BEN reflects the influence of PROG on brain function and behavior. Furthermore, they elucidate the neural mechanisms through which PROG modulates impulsivity traits measured by BAS-drive: PROG enhances the temporal coherence (decreased entropy) of neural activity in the left DLPFC, which in turn increases temporal synchronization (increased FC) within this region during resting-state, and then enhances executive control functions, thereby negatively regulating impulsivity. These findings provide new insights into our understanding of the effects of ovarian hormones on the brain and behavior in women.

Neurobiologically interpretable causal connectome for predicting young adult depression: A graph neural network study

BACKGROUND

There is a surprising lack of neuroimaging studies of depression that not only identify the whole brain causal connectivity features but also explore whether these features have neurobiological correlates.

METHODS

Three graph neural networks (GNN) models were applied to three types of causal connectomes (CCs): granger causality, regression DCM (rDCM), and TwoStep, obtained from a total of 1296 young adult participants in three large-scale datasets.

RESULTS

GNN models showed better performance for predicting depression when using causal connectomes such as TwoStep (average precision score, 0.882), granger causality (0.878), or rDCM (0.853) compared with using functional connectomes like Pearson's (0.850) and partial (0.823) correlation. Notably, nodal features derived only from rDCM and TwoStep showed spatial associations with positron emission tomography measures of receptors for neurotransmitters such as dopamine and serotonin. Further analysis revealed the shared directed edges among the subject's edge features, which included cortical causal connections in networks such as the default mode, control, dorsal attention, peripheral visual, and parietofrontal networks.

LIMITATIONS

The classification performance of leave-one-site-out cross-validation did not achieve a similar level with that of 10-fold cross-validation.

CONCLUSIONS

Our findings suggest that the connectomes derived from CCs using GNN, rather than functional connectomes, provide more accurate and neurobiologically relevant information for depression. Moreover, the observed spatial heterogeneity of this relevance and subject-specific edge features emphasizes the complexity of depression. These results have the potential to advance our understanding of depression's nature and potentially contribute to precision psychiatry by aiding in its diagnosis and treatment.

Unpacking the V1 map: Differential covariation of visual properties across spatial dimensions

Primary visual cortex (V1) has played a key role in understanding the organization of cerebral cortex. Both structural and functional properties vary sharply throughout the human V1 map. Despite large variation, underlying constancies computed from the covariation pattern of V1 properties have been proposed. Such constancies would imply that V1 is composed of multiple identical units whose visual properties differ only due to differences in their inputs. To test this, we used fMRI to investigate how V1 cortical magnification and preferred spatial frequency covary across eccentricity and polar angle, measured in 40 observers. V1 cortical magnification and preferred spatial frequency were strongly correlated across eccentricity and around polar angle, however their relation differed between these dimensions: they were proportional across eccentricity but not polar angle. The constant ratio of cortical magnification to preferred spatial frequency when measured as a function of eccentricity suggests a shared underlying cause of variation in the two properties, e.g., the gradient of retinal ganglion cell density across eccentricity. In contrast, the deviation from proportionality around polar angle implies that cortical variation differs from that in retina along this dimension. Thus, a constancy hypothesis is supported for one of the two spatial dimensions of V1, highlighting the importance of examining the full 2D-map to understand how V1 is organized.

Shared orbitofrontal dynamics to a drug-themed movie track craving and recovery in heroin addiction

Movies captivate groups of individuals (the audience), especially if they contain themes of common motivational interest to the group. In drug addiction, a key mechanism is maladaptive motivational salience attribution whereby drug cues outcompete other reinforcers within the same environment or context. We predicted that while watching a drug-themed movie, where cues for drugs and other stimuli share a continuous narrative context, functional MRI responses in individuals with heroin use disorder (iHUD) will preferentially synchronize during drug scenes. Thirty inpatient iHUD (24 male) and 25 healthy controls (16 male) watched a drug-themed movie at baseline and at follow-up after 15 weeks. Results revealed such drug-biased synchronization in the orbitofrontal cortex (OFC), ventromedial and ventrolateral prefrontal cortex, and insula. After 15 weeks during ongoing inpatient treatment, there was a significant reduction in this drug-biased shared response in the OFC, which correlated with a concomitant reduction in dynamically-measured craving, suggesting synchronized OFC responses to a drug-themed movie as a neural marker of craving and recovery in iHUD.

The neural basis of intuitive approximate number system in board game Go (Baduk) experts

Studies have shown that newborns and nonhuman animals innately estimate quantities using the approximate number system (ANS), raising questions about whether the ANS is a precursor to advanced computational abilities or an independent cognitive function. Professional board game Go players, who can quickly judge territory sizes without explicit calculations, provide a unique insight into the ANS. Using fMRI, we investigated the neural correlates of the approximate number system in professional Go players. Results showed that during the difficult task, professional Go players exhibited significantly increased activity in the right cerebellum compared to the controls, while several parts of the cerebrum were activated during the easy task. The observed activation in the right cerebellum was inversely correlated with the number of years of training required to become professional players. The findings indicate that the ANS is either facilitated by training or reflects an inherent, exceptional ability in certain individuals, suggesting a cerebellar-based alternative to the computational role of the cerebral cortex.

Salience network resting-state functional connectivity predicts self-controlled decision-making

Salience network functional integration with the central executive network and the default mode network at rest has been shown to predict real-life self-control. It has been proposed that a network interaction index reflecting stronger functional integration of the salience network with the central executive network and reduced functional connectivity of the salience network with the default mode network represents a trait neural correlate of successful self-control exertion. Here, we attempted to replicate this result using data from our own study where 121 participants completed an fMRI self-control task comprising real-life scenarios and data from a second study (N = 79) retrieved from OpenNeuro (dataset ID: ds002643) where participants completed an fMRI food choice task. We could not replicate the proposed role of salience network resting-state functional connectivity in self-controlled decision-making in either of those data sets. Instead, we found evidence for the exact opposite effect, specifically a negative association between self-control performance and the network interaction index. The role of analysis pipelines, appropriate network ROIs, and the measurement of self-control are discussed in the context of our findings.

Insight predicts subsequent memory via cortical representational change and hippocampal activity

The neural mechanisms driving creative problem-solving, including representational change and its relation to memory, still remain largely unknown. We focus on the creative process of insight, wherein rapid knowledge reorganization and integration-termed representational change-yield solutions that evoke suddenness, certainty, positive emotion, and enduring memory. We posit that this process is associated with stronger shifts in activation patterns within brain regions housing solution-relevant information, including the visual cortex for visual problems, alongside regions linked to feelings of emotion, suddenness and subsequent memory. To test this, we collect participants' brain activity while they solve visual insight problems in the MRI. Our findings substantiate these hypotheses, revealing stronger representational changes in visual cortex, coupled with activations in the amygdala and hippocampus-forming an interconnected network. Importantly, representational change and hippocampal effects are positively associated with subsequent memory. This study provides evidence of an integrated insight mechanism influencing memory.

Structural signatures of language reorganization after left hemispherotomy in patients with Rasmussen's encephalitis

Rasmussen's encephalitis (RE) is a rare neurological disorder affecting a single cerebral hemisphere, often requiring hemispherotomy as a curative treatment. While significant brain plasticity occurs due to the pathology and surgical intervention, the mechanisms underlying cognitive functioning in the remaining hemisphere remain poorly understood. This multiple-case study longitudinally investigates neurocognitive reorganization in childhood after left hemispherotomy for RE and identifies structural patterns in the right hemisphere associated with language recovery. Indeed, the mechanisms that allow the right hemisphere to support language, after left hemispherotomy remain unclear. Cognitive trajectories were analyzed in three RE patients, and their cortical thickness (CT) changes were compared with data from a publicly available cohort of 393 healthy subjects. Language neuropsychological scores and T1-weighted MRI data were assessed in the healthy right hemisphere before hemispherotomy, one year, and five years post-surgery. Specifically, principal component analysis, structural covariance, and graph theory approaches were employed to investigate language network organization in patients and controls. Results reveal diverse language recovery trajectories among the three patients. Regarding CT, three potential signatures associated with favorable language outcomes were identified: (1) normal or below-normal CT values in cortical regions; (2) a more associative and integrative organization of the language network; and (3) increased global efficiency. These preliminary longitudinal findings provide novel insights into the mechanisms of neurocognitive reorganization following left hemispherotomy in childhood. By emphasizing structural patterns linked to favorable postoperative language recovery, this study highlights their value for guiding future research and clinical interventions.

Age-related differences in trust decisions: when memory fails and appearances prevail

Older adults are frequent victims of scams, possibly due to biases in how they decide whom to trust. Indeed, older adults' decisions are more likely to be influenced by how generous a person looks and less so by their memory for how this person behaved. Here, we leverage functional magnetic resonance imaging data to clarify the mechanism by which this age-dependent difference emerges. Eighty-six participants learned how much of a $10 endowment an individual shared in a dictator game, and then made decisions about whom to play another round with. As we hypothesized, older adults did not reliably prefer to re-engage with people who had proven themselves to be generous. This bias was driven by a combination of worse associative memory for how much each person shared, linked to decreased medial temporal lobe activity during encoding, and decreased inhibition of irrelevant facial features, linked to reduced activity in the inferior frontal gyrus. Taken together, our findings highlight 'age-related differences' in the ability to both encode relevant information and adaptively deploy it in service of social decisions.

Repetitive Transcranial Magnetic Stimulation (rTMS) Treatment Reduces Variability in Brain Function in Schizophrenia: Data From a Double-Blind, Randomized, Sham-Controlled Trial

BACKGROUND/HYPOTHESIS

There is increasing awareness of interindividual variability in brain function, with potentially major implications for repetitive transcranial magnetic stimulation (rTMS) efficacy. We perform a secondary analysis using data from a double-blind randomized controlled 4-week trial of 20 Hz active versus sham rTMS to dorsolateral prefrontal cortex (DLPFC) during a working memory task in participants with schizophrenia. We hypothesized that rTMS would change local functional activity and variability in the active group compared with sham.

STUDY DESIGN

83 participants were randomized in the original trial, and offered neuroimaging pre- and post-treatment. Of those who successfully completed both scans (n = 57), rigorous quality control left n = 42 (active/sham: n = 19/23), who were included in this analysis. Working memory-evoked activity during an N-Back (3-Back vs 1-Back) task was contrasted. Changes in local brain activity were examined from an 8 mm ROI around the rTMS coordinates. Individual variability was examined as the mean correlational distance (MCD) in brain activity pattern from each participant to others within the same group.

RESULTS

We observed an increase in task-evoked left DLPFC activity in the active group compared with sham (F1,36 = 5.83, False Discovery Rate (FDR))-corrected P = .04). Although whole-brain activation patterns were similar in both groups, active rTMS reduced the MCD in activation pattern compared with sham (F1,36 = 32.57, P < .0001). Reduction in MCD was associated with improvements in attention performance (F1,16 = 14.82, P = .0014, uncorrected).

CONCLUSIONS

Active rTMS to DLPFC reduces individual variability of brain function in people with schizophrenia. Given that individual variability is typically higher in schizophrenia patients compared with controls, such reduction may "normalize" brain function during higher-order cognitive processing.

Cortical representational geometry of diverse tasks reveals subject-specific and subject-invariant cognitive structures

The variability in brain function forms the basis for our uniqueness. Prior studies indicate smaller individual differences and larger inter-subject correlation (ISC) in sensorimotor areas than in the association cortex. These studies, deriving information from brain activity, leave individual differences in cognitive structures based on task similarity relations unexplored. This study quantitatively evaluates these differences by integrating ISC, representational similarity analysis, and vertex-wise encoding models using functional magnetic resonance imaging across 25 cognitive tasks. ISC based on cognitive structures enables subject identification with 100% accuracy using at least 14 tasks. ISC is larger in the fronto-parietal association and higher-order visual cortices, suggesting subject-invariant cognitive structures in these regions. Principal component analysis reveals different cognitive structure configurations within these regions. This study provides evidence of individual variability and similarity in abstract cognitive structures.

Molecular mechanisms explaining sex-specific functional connectivity changes in chronic insomnia disorder

BACKGROUND

This study investigates the hypothesis that chronic insomnia disorder (CID) is characterized by sex-specific changes in resting-state functional connectivity (rsFC), with certain molecular mechanisms potentially influencing CID's pathophysiology by altering rsFC in relevant networks.

METHODS

Utilizing a resting-state functional magnetic resonance imaging (fMRI) dataset of 395 participants, including 199 CID patients and 196 healthy controls, we examined sex-specific rsFC effects, particularly in the default mode network (DMN) and five insomnia-genetically vulnerable regions of interest (ROIs). By integrating gene expression data from the Allen Human Brain Atlas, we identified genes linked to these sex-specific rsFC alterations and conducted enrichment analysis to uncover underlying molecular mechanisms. Additionally, we simulated the impact of sex differences in rsFC with different sex compositions in our dataset and employed machine learning classifiers to distinguish CID from healthy controls based on sex-specific rsFC data.

RESULTS

We identified both shared and sex-specific rsFC changes in the DMN and the five genetically vulnerable ROIs, with gene expression variations associated with these sex-specific connectivity differences. Enrichment analysis highlighted genes involved in synaptic signaling, ion channels, and immune function as potential contributors to CID pathophysiology through their influence on connectivity. Furthermore, our findings demonstrate that different sex compositions significantly affect study outcomes and higher diagnostic performance in sex-specific rsFC data than combined sex.

CONCLUSIONS

This study uncovered both shared and sex-specific connectivity alterations in CID, providing molecular insights into its pathophysiology and suggesting considering sex differences in future fMRI-based diagnostic and treatment strategies.

Brain connectome gradient dysfunction in patients with end-stage renal disease and its association with clinical phenotype and cognitive deficits

A cortical hierarchical architecture is vital for encoding and integrating sensorimotor-to-cognitive information. However, whether this gradient structure is disrupted in end-stage renal disease (ESRD) patients and how this disruption provides valuable information for potential clinical symptoms remain unknown. We prospectively enrolled 77 ESRD patients and 48 healthy controls. Using resting-state functional magnetic resonance imaging, we studied ESRD-related hierarchical alterations. The Neurosynth platform and machine-learning models with 10-fold cross-validation were applied. ESRD patients had abnormal gradient metrics in core regions of the default mode network, sensorimotor network, and frontoparietal network. These changes correlated with creatinine, depression, and cognitive functions. A logistic regression classifier achieved a maximum performance of 84.8% accuracy and 0.901 area under the ROC curve (AUC). Our results highlight hierarchical imbalances in ESRD patients that correlate with diverse cognitive deficits, which may be used as potential neuroimaging markers for clinical symptoms.

Object representations drive emotion schemas across a large and diverse set of daily-life scenes

The rapid emotional evaluation of objects and events is essential in daily life. While visual scenes reliably evoke emotions, it remains unclear whether emotion schemas evoked by daily-life scenes depend on object processing systems or are extracted independently. To explore this, we collected emotion ratings for 4913 daily-life scenes from 300 participants, and predicted these ratings from representations in deep neural networks and functional magnetic resonance imaging (fMRI) activity patterns in visual cortex. AlexNet, an object-based model, outperformed EmoNet, an emotion-based model, in predicting emotion ratings for daily-life scenes, while EmoNet excelled for explicitly evocative scenes. Emotion information was processed hierarchically within the object recognition system, consistent with the visual cortex's organization. Activity patterns in the lateral occipital complex (LOC), an object-selective region, reliably predicted emotion ratings and outperformed other visual regions. These findings suggest that the emotional evaluation of daily-life scenes is mediated by visual object processing, with additional mechanisms engaged when object content is uninformative.

Healthy aging delays and dedifferentiates high-level visual representations

Healthy aging impacts visual information processing with consequences for subsequent high-level cognition and everyday behavior, but the underlying neural changes in visual representations remain unknown. Here, we investigate the nature of representations underlying object recognition in older compared to younger adults by tracking them in time using electroencephalography (EEG), across space using functional magnetic resonance imaging (fMRI), and by probing their behavioral relevance using similarity judgments. Applying a multivariate analysis framework to combine experimental assessments, four key findings about how brain aging impacts object recognition emerge. First, aging selectively delays the formation of object representations, profoundly changing the chronometry of visual processing. Second, the delay in the formation of object representations emerges in high-level rather than low- and mid-level ventral visual cortex, supporting the theory that brain areas developing last deteriorate first. Third, aging reduces content selectivity in the high-level ventral visual cortex, indicating age-related neural dedifferentiation as the mechanism of representational change. Finally, we demonstrate that the identified representations of the aging brain are behaviorally relevant, ascertaining ecological relevance. Together, our results reveal the impact of healthy aging on the visual brain.

High-Order Graphical Topology Analysis of Brain Functional Connectivity Networks Using fMRI

The brain connectivity network can be represented as a graph to reveal its intrinsic topological properties. While classical graph theory provides a powerful framework for examining brain connectivity patterns, it often focuses on low-order graphical indicators and pays less attention to high-order topological metrics, which are crucial to the comprehensive understanding of brain topology. In this paper, we capture high-order topological features via a graphical topology analysis framework for brain connectivity networks derived from functional Magnetic Resonance Imaging (fMRI). Several high-order metrics are examined across varying sparsity levels of binary graphs to trace the evolution of brain networks. Topological phase transitions are primarily investigated that reflect brain criticality, and a novel indicator called "redundant energy" is proposed to measure the chaos level of the brain. Extensive experiments on diverse datasets from healthy controls validate the reproducibility and generalizability of our framework. The results demonstrate that around critical points, classical graph theoretical indicators change sharply, driven by crucial brain regions that have high node curvatures. Further investigations on fMRI of subjects with and without Parkinson's disease uncover significant alterations in high-order topological features which are further associated with the severity of the disease. This study provides a fresh perspective on studying topological architectures of the brain, with the potential to expand our comprehension on brain function in both healthy and diseased states.

Total recall: Detecting autobiographical memory retrieval in the absence of behaviour

Functional neuroimaging has fundamentally changed our understanding of disorders of consciousness (DoC). While many DoC patients exhibit minimal to no behavioural responsiveness, a significant minority show neural evidence of awareness and preserved cognitive functioning. Although several cognitive functions have been explored in DoC patients, autobiographical memory -- the ability to form and retrieve personal memories -- has yet to be investigated. To address this gap, we used functional magnetic resonance imaging (fMRI) to investigate autobiographical memory in one DoC patient. The patient viewed video clips across three conditions: (1) Own - clips recorded from their perspective during a recent mall visit; (2) Other - clips from a healthy control's visit to the same mall; and (3) Bookstore - novel clips from an entirely different store that had not been visited. We trained a linear support vector classifier to associate fMRI activity in canonical autobiographical memory regions with each condition using data from twelve healthy participants. We then applied the trained model to the patient's data to 'decode' which condition their fMRI activity predicted. The model accurately distinguished between Own, Other, and Bookstore conditions in the patient (Balanced Accuracy = 0.448, p = .032), with performance within the control group range (p = .068). Similarly, the model distinguished between the Own and Other conditions above chance (Balanced Accuracy = 0.609, p = .032) and within the control group's distribution (p = .620), suggesting that the patient was still able to differentiate personal experiences from visually similar scenes, despite being behaviourally unable to report that this was the case. These findings provide preliminary evidence that autobiographical memory processes, critical to conscious awareness and identity, remain intact in some DoC patients, shedding further light on their covert capabilities and inner experiences.

Sequential decreases in basolateral amygdala response to threat predict failure to recover from PTSD

Amygdala hyperreactivity early-post trauma has been a demonstrable neurobiological correlate of future posttraumautic stress disorder (PTSD). The basolateral amygdala (BLA) particularly is vital for fear memory and threat processing, but BLA functional dynamics following a traumatic event are unexplored. BLA reactivity to threat may be a trait that can predict PTSD and persist over time. Alternatively, BLA responsivity to threat cues may change over time and be related to PTSD severity. As part of a larger, multisite study, AURORA, participants 18-75 years old were enrolled in an emergency department (ED) within 72 h of a traumatic event (N = 304, 199 female). At 2-weeks and 6-months post-trauma, PTSD symptoms, BLA responses to threat (fearful>neutral faces), and functional connectivity (FC) during fMRI were assessed. Generalizability of findings was assessed in an external replication sample of ED patients (n = 33). Two weeks post-trauma right BLA reactivity positively predicted later PTSD severity. However, left BLA reactivity to threat at 6 months post-trauma was negatively associated with PTSD severity at that timepoint (ΔPseudo-R2 = 0.04, IRR = 0.38, p < 0.001). In addition, a decrease in BLA reactivity from 2-weeks to 6-months predicted greater PTSD severity at 6 months (ΔPseudo-R2 = 0.03, IRR = 0.58, p < 0.001). This replicated in the external sample. A reduction in left BLA FC with the dorsal attention network predicted increased PTSD severity over time. These findings support a shift in BLA function within the first 6 months post-trauma that predicts PTSD pathology and stand in contrast to prior conceptualizations of amygdala hyperreactivity as a trait-like PTSD risk factor.

Brain activation in older adults during odor identification is related to ApoE, t-tau/Aβ1-42, and hippocampal volume

Despite altered odor identification preceding and predicting Alzheimer's disease (AD) cognitive decline, an inadequate understanding of how AD pathology affects odor memory functions limits its use as a preclinical biomarker. Multivariate linear regression was applied to whole-brain blood-oxygen-level-dependent (BOLD) activations during odor identification task (OID) responses in older adults without dementia (N = 36, 44.4 % ε4 carriers, MAge= 76.61). Apolipoprotein-E ε4 allele status, cerebrospinal fluid levels of total-tau to Amyloid-β1-42, and MRI-derived hippocampal volume measures were used as predictors. The predictors described significant BOLD variation in regions that are associated with necessary OID functions and affected by AD neurodegeneration during OID responses; moreover, all predictors were associated with significant (P < .001) negative BOLD effects in essential task regions during at least one response condition. This evidence suggests significant pathological effects of AD biomarkers on OID-response neural activity in older adults without dementia and should motivate future combined-biomarker investigations of OID functions in preclinical populations.

Intensive task-switching training and single-task training differentially affect behavioral and neural manifestations of cognitive control in children

The ability to flexibly switch between tasks develops during childhood. Children's task-switching performance improves with practice, but the underlying processes remain unclear. We used functional magnetic resonance imaging to examine how 9 weeks of task-switching training affect performance and task-related activation and functional connectivity. Children (8-11 years) were assigned to one of three groups: intensive task switching (SW; n = 72), intensive single tasking (SI; n = 74), and passive control (n = 41). While mixing costs decreased in both training groups initially, only the SW group maintained these training-related improvements at the end of training. Activation in the dorsolateral prefrontal cortex decreased with training, but again only the SW group maintained these activation decreases at the end of training. Condition-specific connectivity increases with task switching became less pronounced with training, especially in the SI group. Lower costs of task switching along with decreased task-related activations suggest increased processing efficiency in frontoparietal regions with training. Intensive task-switching training was associated with sustained changes, possibly facilitated by a greater mismatch between processing supplies and environmental demands. Our findings suggest that experience-dependent changes with intensive task-switching training do not mirror maturational processes but rather facilitate performance via more efficient task processing.

Interpretable modality-specific and interactive graph convolutional network on brain functional and structural connectomes

Both brain functional connectivity (FC) and structural connectivity (SC) provide distinct neural mechanisms for cognition and neurological disease. In addition, interactions between SC and FC within distributed association regions are related to alterations in cognition or neurological diseases, considering the inherent linkage between neural function and structure. However, there is a scarcity of existing learning-based methods that leverage both modality-specific characteristics and high-order interactions between the two modalities for regression or classification. Hence, this study proposes an interpretable modality-specific and interactive graph convolutional network (MS-Inter-GCN) that incorporates modality-specific information, reflecting the unique neural mechanism for each modality, and structure-function interactions, capturing the underlying foundation provided by white-matter fiber tracts for high-level brain function. In MS-Inter-GCN, we generate modality-specific task-relevant embeddings separately from both FC and SC using a graph convolutional encoder-decoder module. Subsequently, we learn the interactive weights between corresponding regions of FC and SC, reflecting the coupling strength, by employing an interactive module on the embeddings of both modalities. A novel graph structure is constructed, which uses modality-specific task-relevant embeddings and inserts the interactive weights as edges connecting corresponding regions of two modalities, and then is used for the regression or classification task. Finally, a post-hoc explainable technology - GNNExplainer- is used to identify salient regions and connections of each modality as well as salient interactions between FC and SC associated with tasks. We apply the proposed framework to fluid cognition prediction, Parkinson's disease (PD), Alzheimer's disease (AD), and schizophrenia (SZ) classification. Experimental results demonstrate that our method outperforms the other ten state-of-the-art methods on multi-modal brain features on all tasks. The GNNExplainer identifies salient structural and functional regions and connections for fluid cognition, PD, AD, and SZ. It confirms that strong structure-function coupling within the executive and control networks, combined with weak coupling within the motor network, is associated with fluid cognition. Moreover, structure-function decoupling in specific brain regions serves as a marker for different diseases: decoupling of the prefrontal, superior parietal, and superior occipital cortices is a marker of PD; decoupling of the middle frontal and lateral parietal cortices, temporal pole, and subcortical regions is indicative of AD; and decoupling of the prefrontal, parietal, and temporal cortices, as well as the cerebellum, contributes to SZ.

The role of sex in the association between cannabis use disorder and resting-state functional connectivity

While Cannabis use disorder (CUD) is twice as prevalent in males, females transition more quickly from heavy use to CUD and experience more severe withdrawal. These clinically relevant sex differences contrast the lack of knowledge about the underlying brain mechanisms. This study investigated the relationship between CUD and resting-state functional brain connectivity (RSFC), assessing potential sex differences herein. RSFC of the Salience Network (SN), Basal Ganglia Network (BGN), Executive Control Network (ECN), and Default Mode Network (DMN) was compared between 152 individuals (76 males) with CUD and 114 matched controls (47 males). Within the CUD group, relationships between RSFC and heaviness of cannabis use, age of onset, and CUD symptom severity, along with their associations with sex, were investigated. CUD and control groups showed similar RSFC across all networks, regardless of sex. In the CUD group, heavier cannabis use correlated with higher RSFC across all networks and earlier age of onset was related to higher RSFC in the anterior SN, BGN, left ECN, and dorsal DMN. These associations were similar for males and females. CUD severity was related to higher RSFC in the anterior SN, which was moderated by sex, with a positive association seen only in males. In conclusion, CUD may not necessarily be associated with altered RSFC. Individual use characteristics such age of onset and severity of use may determine the potential impact of cannabis use on RSFC in a largely similar way in males and females.

Brain structural and functional magnetic resonance imaging alterations in individuals with convergence insufficiency

PURPOSE

Individuals with convergence insufficiency (CI) encounter challenges in turning their eyes inward during near work. It is unclear how this relates to brain structural and functional alterations. This study aimed to explore the neural mechanism underlying CI using multimodal brain magnetic resonance imaging (MRI).

METHODS

Thirty-four CI participants and 35 healthy controls (HC) were recruited, who underwent visual examinations and brain MRI scanning. Structural MRI data were analysed to calculate cortical thickness, volume and surface area. Fractional amplitude of low-frequency fluctuation (fALFF) and seed-based functional connectivity were obtained from resting-state functional MRI data. The brain structural and functional metrics were compared between the two groups followed by correlation analyses between clinical measurements and significant brain features.

RESULTS

Relative to HC, individuals with CI had lower grey matter volume (GMV) and surface area in the right frontal eye fields, parietal eye fields and left medial orbitofrontal cortex, higher GMV and surface area in the right middle frontal and inferior temporal gyri and higher fALFF of the left cerebellum and functional connection between bilateral cerebellums. GMV of the right middle frontal gyrus and fALFF in the left cerebellum were positively correlated with the near point of convergence in all participants.

CONCLUSIONS

Lower structural metrics in the visual and oculomotor cortices and higher functional activity in the cerebellum may underpin convergence dysfunction and visual fatigue, while higher structural metrics in the right middle frontal and inferior temporal gyri reflect partial compensation for the visual and oculomotor cortex defects, thereby maintaining attention and parallax information processing. This study may enhance understanding of the neural mechanism of CI by revealing the impact of abnormal visual experiences of CI on the brain with disassociated structural and functional alterations in the vergence system.

Left parietal structural connectivity mediates typical and atypical language laterality in temporal lobe epilepsy

OBJECTIVE

Subjects with left temporal lobe epilepsy may either show altered hemispheric language lateralization or retain typical, left lateralization. Examining the integrity of white matter pathways involved in the adaptation or maintenance of language lateralization in these patients could have important clinical implications for preserving or potentiating compensatory language mechanisms.

METHODS

We combined task functional magnetic resonance imaging and structural diffusion metrics to determine the dependency of lobe-based language laterality on white matter integrity in healthy participants and left temporal lobe epilepsy (TLE) patients. We tested for differences between individuals who expressed typical, left hemisphere laterality compared to those with atypical laterality patterns (bilateral or right hemisphere biased).

RESULTS

A total of 41 left TLE patients and 51 sex- and age-matched healthy participants (HPs) were enrolled. In left temporal lobe epilepsy, typical patterns of frontal and temporal lateralities were less conditioned by the language-related white matter connections of the left temporal lobe. In typically organized epilepsy subjects, temporal lobe language laterality was dependent upon the structural connectivities of the left parietal lobe. Among atypically organized individuals, compared to HPs, TLE patients displayed frontal and parietal language lateralities mediated by the structural connectivities of the left parietal lobe.

SIGNIFICANCE

Language-related left parietal lobe connections were critical both for maintaining typical left hemisphere-biased language processing in the temporal lobe and for the formation of noncanonical, potentially adaptive language processing asymmetries in the frontal and parietal lobes. Assessments of the laterality and integrity of language skills in left temporal lobe epilepsy will require modeling white matter structural influences.

Multivariate patterns among multimodal neuroimaging and clinical, cognitive, and daily functioning characteristics in bipolar disorder

Individuals with bipolar disorder (BD) show heterogeneity in clinical, cognitive, and daily functioning characteristics, which challenges accurate diagnostics and optimal treatment. A key goal is to identify brain-based biomarkers that inform patient stratification and serve as treatment targets. The objective of the present study was to apply a data-driven, multivariate approach to quantify the relationship between multimodal imaging features and behavioral phenotypes in BD. We pooled structural, task and resting-state functional magnetic resonance imaging (MRI), and clinical, cognitive, and functioning data from 167 fully or partly remitted patients with BD from three studies conducted at the same site. We performed canonical correlation analysis (CCA) to investigate multivariate relations among the 56 imaging and 23 behavioral features in patients. Data from 46 matched healthy controls were included for covariate-adjusted standardization of patients' scores and for group comparisons. The imaging and behavioral data sets showed a strong canonical correlation (r = 0.84, p = .004). Among the behavioral variables, cognitive test scores across psychomotor speed, verbal memory, and verbal fluency were associated with the multimodal imaging variate comprising task activation within the dorsolateral prefrontal cortex and supramarginal gyrus, also when other clinical and daily functioning variables were considered. Task activation within the dorsal prefrontal and parietal cognitive control areas constitutes a potential pro-cognitive treatment target.

Subcortical nuclei of the human ascending arousal system encode anticipated reward but do not predict subsequent memory

Subcortical nuclei of the ascending arousal system (AAS) play an important role in regulating brain and cognition. However, functional MRI (fMRI) of these nuclei in humans involves unique challenges due to their size and location deep within the brain. Here, we used ultra-high-field MRI and other methodological advances to investigate the activity of 6 subcortical nuclei during reward anticipation and memory encoding: the locus coeruleus (LC), basal forebrain, median and dorsal raphe nuclei, substantia nigra, and ventral tegmental area. Participants performed a monetary incentive delay task, which successfully induced a state of reward anticipation, and a 24-h delayed surprise memory test. Region-of-interest analyses revealed that activity in all subcortical nuclei increased in anticipation of potential rewards as opposed to neutral outcomes. In contrast, activity in none of the nuclei predicted memory performance 24 h later. These findings provide new insights into the cognitive functions that are supported by the human AAS.

Long-Term Exposure to Traffic-Related Air Pollution and Noise and Dynamic Brain Connectivity across Adolescence

BACKGROUND

Traffic-related exposures, such as air pollution and noise, show long-term associations with brain alterations in children and adolescents. The associations with functional connectivity have been studied using static approaches of resting-state functional magnetic resonance imaging (rs-fMRI) (i.e., average connectivity between regions across the scanning session).

OBJECTIVES

Our aim was to investigate the long-term association of traffic air pollution and noise during pregnancy and childhood with functional connectivity across adolescence using a dynamic approach, which captures different connectivity patterns across the scanning session.

METHODS

We used data from the Generation R population-based birth cohort. We estimated levels of 14 air pollutants and traffic noise at home addresses during pregnancy and childhood. We acquired rs-fMRI data at the age-10 y and age-14 y visits. We included participants with rs-fMRI data in at least one visit and either air pollution data (n = 3,588 ) or noise data (n = 2,642 ). We used k-means clustering to identify five connectivity patterns, called "states," that reoccur over time and across subjects and visits. We calculated the mean time spent in each state for each participant and visit. We performed multi- and single-pollutant mixed effects models adjusted for socioeconomic and lifestyle variables, including the individual as random effect to test the associations between the exposures and the mean time spent in each state.

RESULTS

Exposure to nitrogen oxides, particulate matter (PM), and road-traffic noise was related to differences in the time spent in the connectivity states, both in the multi- and single-pollutant models. For instance, higher levels of exposure to PM with aerodynamic diameter between 2.5 μ m and 10 μ m (PM COARSE ) during pregnancy and higher noise exposure during childhood were associated with more time spent in a state in which the default-mode network, related to self-referential processes and mind-wandering, shows high connectivity.

DISCUSSION

Traffic-related exposures might be related to long-term alterations in brain functional network organization in adolescents. Further research should explore the potential impact of these differences on cognition and psychopathology. https://doi.org/10.1289/EHP14525.

CLIMATE BRAIN - Questionnaires, Tasks and the Neuroimaging Dataset

Climate change threatens human populations and ecosystems worldwide. Neuroscience research on this topic is emerging, but validated questionnaires, stimuli, and fMRI tasks remain scarce. Here, we present the CLIMATE BRAIN dataset, a multimodal collection of questionnaire, behavioral, and neuroimaging data from 160 young, healthy Polish individuals. Designed to advance research on climate emotions and pro-environmental behavior, the dataset includes individuals with moderate climate change concern. Participants read anger and hope-evoking stories about climate change and made pro-environmental decisions. The dataset includes data from (1) various questionnaire measures, including the Inventory of Climate Emotions (ICE); (2) a neuroimaging task for measuring emotional reactions to standardized Emotional Climate Change Stories (ECCS); and (3) a neuroimaging task based on Carbon Emission Task (CET) to measure climate action-taking. For technical validation, we provide image quality metrics and show the evidence for the effectiveness of the tasks consistent with prior studies. To our knowledge, the proposed multimodal dataset is currently the only publicly available resource specifically designed to investigate human brain responses to climate change.

Neural Patterns of Social Pain in the Brain-Wide Representations Across Social Contexts

Empathy can be elicited by physiological pain, as well as in social contexts. Although physiological and different social contexts induce a strong subjective experience of empathy, the general and context-specific neural representations remain elusive. Here, it is combined fMRI with multivariate pattern analysis (MVPA) to establish neurofunctional models for social pain triggered by observing social exclusion and separation naturistic stimuli. The findings revealed that both social contexts engaged the empathy and social function networks. Notably, the intensity of pain empathy elicited by these two social stimuli does not significantly differentiate the neural representations of social exclusion and separation, suggesting context-specific neural representations underlying these experiences. Furthermore, this study established a model that traces the progression from physiological pain to social pain empathy. In conclusion, this study revealed the neural pathological foundations and interconnectedness of empathy induced by social and physiological stimuli and provide robust neuromarkers to precisely evaluate empathy across physiological and social domains.

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.

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.

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.

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.