The organization of the human cerebral cortex estimated by intrinsic functional connectivity

Individual suicide risk factors with resting-state brain functional connectivity patterns in bipolar disorder patients based on latent Dirichlet allocation model

BACKGROUND

The widespread problem of suicide and its severe burden in bipolar disorder (BD) necessitate the development of objective risk markers, aiming to enhance individual suicide risk prediction in BD.

METHODS

This study recruited 123 BD patients (61 patients with prior suicide attempted history (PSAs), 62 without (NSAs)) and 68 healthy controls (HEs). The Latent Dirichlet Allocation (LDA) model was used to decompose the resting state functional connectivity (RSFC) into multiple hyper/hypo-RSFC patterns. Thereafter, according to the quantitative results of individual heterogeneity over latent factor dimensions, the correlations were analyzed to test prediction ability.

RESULTS

Model constructed without introducing suicide-related labels yielded three latent factors with dissociable hyper/hypo-RSFC patterns. In the subsequent analysis, significant differences in the factor distributions of PSAs and NSAs showed biases on the default-mode network (DMN) hyper-RSFC factor (factor 3) and the salience network (SN) and central executive network (CEN) hyper-RSFC factor (factor 1), indicating predictive value. Correlation analysis of the individuals' expressions with their Nurses' Global Assessment of Suicide Risk (NGASR) revealed factor 3 positively correlated (r = 0.4180, p < 0.0001) and factor 1 negatively correlated (r = - 0.2492, p = 0.0055) with suicide risk. Therefore, it could be speculated that patterns more associated with suicide reflected hyper-connectivity in DMN and hypo-connectivity in SN, CEN.

CONCLUSIONS

This study provided individual suicide-associated risk factors that could reflect the abnormal RSFC patterns, and explored the suicide related brain mechanisms, which is expected to provide supports for clinical decision-making and timely screening and intervention for individuals at high risks of suicide.

Progressive alterations of resting-state hypothalamic dysconnectivity in schizophrenia

BACKGROUND

The hypothalamus may be involved in the pathogenesis of schizophrenia. Investigating hypothalamus dysfunction in schizophrenia and probing how it is related to symptoms and responds to antipsychotic medication is crucial for understanding the potential mechanism of hypothalamus dysfunction under the long-term illness.

METHODS

We recruited 216 patients with schizophrenia, including 140 antipsychotic-naïve first-episode patients (FES, including 44 patients with 1-year follow-up data), 76 chronically treated schizophrenia (CTS), and 210 healthy controls (HC). Hypothalamic seed-based functional connectivity (FC) was calculated and compared among the FES, CTS, and HC groups using analysis of covariance. Exploratory analysis was conducted between the FES patients at baseline and after 1-year follow-up. Significantly altered hypothalamic FCs were then related to clinical symptomology, while age- and illness-related regression analyses were also conducted and compared between diagnostic groups.

RESULTS

The FES patients showed decreased hypothalamic FCs with the midbrain and right thalamus, whereas the CTS patients showed more severe decreased hypothalamic FCs with the midbrain, right thalamus, left putamen, right caudate, and bilateral anterior cingulate cortex compared to HCs. These abnormalities were not correlated to the symptomology or illness duration, or not reversed by the antipsychotic treatment. Age-related hypothalamic FC decrease was also identified in the abovementioned regions, and a faster age-related decline of the hypothalamic FC was observed with the left putamen and bilateral anterior cingulate cortex.

CONCLUSION

Age-related hypothalamic FC decrease extends the functional alterations that characterize the neurodegenerative nature of schizophrenia. Future studies are required to further probe the hormonal or endocrinal underpinnings of such alterations and trace the precise progressive trajectories.

Functional connectome gradient predicts clinical symptoms of chronic insomnia disorder

Insomnia is the second most prevalent psychiatric disorder worldwide, but the understanding of the pathophysiology of insomnia remains fragmented. In this study, we calculated the connectome gradient in 50 chronic insomnia disorder (CID) patients and 38 healthy controls (HC) to assess changes due to insomnia and utilized these gradients in a connectome-based predictive modeling (CPM) to predict clinical symptoms associated with insomnia. The results suggested that insomnia led to significant alterations in the functional gradients of some brain areas. Specifically, the gradient scores in the middle frontal gyrus, superior anterior cingulate gyrus, and right nucleus accumbens were significantly higher in the CID patients than in the HC group, whereas the scores in the middle occipital gyrus, right fusiform gyrus, and right postcentral gyrus were significantly lower than in the HC group. Further correlation analysis revealed that the right middle frontal gyrus is positively correlated with the self-rating anxiety scale (r=0.3702). Additionally, the prediction model built with functional gradients could well predict the sleep quality (r=0.5858), anxiety (r=0.6150), and depression (r=0.4022) levels of insomnia patients. This offers an objective depiction of the clinical diagnosis of insomnia, yielding a beneficial impact on the identification of effective biomarkers and the comprehension of insomnia.

Resting-state dynamic functional connectivity in major depressive disorder: A systematic review

As a novel measure, dynamic functional connectivity (dFC) provides insight into the dynamic nature of brain networks and their interactions in resting-state, surpassing traditional static functional connectivity in pathological conditions such as depression. Since a comprehensive review is still lacking, we then reviewed forty-five eligible papers to explore pathological mechanisms of major depressive disorder (MDD) from perspectives including abnormal brain regions and functional networks, brain state, topological properties, relevant recognition, along with longitudinal studies. Though inconsistencies could be found, common findings are: (1) From different perspectives based on dFC, default-mode network (DMN) with its subregions exhibited a close relation to the pathological mechanism of MDD. (2) With a corrupted integrity within large-scale functional networks and imbalance between them, longer fraction time in a relatively weakly-connected state may be a possible property of MDD concerning its relation with DMN. Abnormal transition frequencies between states were correlated to the severity of MDD. (3) Including dynamic properties in topological network metrics enhanced recognition effect. In all, this review summarized its use for clinical diagnosis and treatment, elucidating the non-stationary of MDD patients' aberrant brain activity in the absence of stimuli and bringing new views into its underlying neuro mechanism.

Characterizing microstructural patterns within the cortico-striato-thalamo-cortical circuit in Parkinson's disease

PURPOSE

Parkinson's disease (PD) involves pathological alterations that include cortical impairments at levels of region and network. However, its microstructural abnormalities remain to be further elucidated via an appropriate diffusion neuroimaging approach. This study aimed to comprehensively demonstrate the microstructural patterns of PD as mapped by diffusion kurtosis imaging (DKI).

METHODS

The microstructure of grey matter in both the PD group and the matched healthy control group was quantified by a DKI metric (mean kurtosis). The intergroup difference and classification performance of global microstructural complexity were analyzed in a voxelwise manner and via a machine learning approach, respectively. The patterns of information flows were explored in terms of structural connectivity, network covariance and modular connectivity.

RESULTS

Patients with PD exhibited global microstructural impairments that served as an efficient diagnostic indicator. Disrupted structural connections between the striatum and cortices as well as between the thalamus and cortices were widely distributed in the PD group. Aberrant covariance of the striatocortical circuitry and thalamocortical circuitry was observed in patients with PD, who also showed disrupted modular connectivity within the striatum and thalamus as well as across structures of the cortex, striatum and thalamus.

CONCLUSION

These findings verified the potential clinical application of DKI for the exploration of microstructural patterns in PD, contributing complementary imaging features that offer a deeper insight into the neurodegenerative process.

Spatiotemporal discoordination of brain spontaneous activity in major depressive disorder

BACKGROUND

Major depressive disorder (MDD) is a widespread mental health issue, impacting spatial and temporal aspects of brain activity. The neural mechanisms behind MDD remain unclear. To address this gap, we introduce a novel measure, spatiotemporal topology (SPT), capturing both the hierarchy and dynamic attributes of brain activity in depressive disorder patients.

METHODS

We analyzed fMRI data from 285 MDD inpatients and 141 healthy controls (HC). SPT was assessed by coupling brain gradient measurement and time delay estimation. A nested machine learning process distinguished between MDD and HC using SPT. Person's correlation tested the link between SPT's and symptom severity, and another machine learning method predicted the gap between patients' chronological and brain age.

RESULTS

SPT demonstrated significant differences between patients and healthy controls (F = 2.944, p < 0.001). Machine learning approaches revealed SPT's ability to discriminate between patients and healthy controls (Accuracy = 0.65, Sensitivity = 0.67, Specificity = 0.64). Moreover, SPT correlated with the severity of depression symptom (r = 0.32. pFDR = 0.045) and predicted the gap between patients' chronological age and brain age (r = 0.756, p < 0.001).

LIMITATIONS

Evaluation of brain dynamics was constrained by MRI temporal resolution.

CONCLUSIONS

Our study introduces SPT as a promising metric to characterize the spatiotemporal signature of brain function, providing insights into deviant brain activity associated with depressive disorders and advancing our understanding of their psychopathological mechanisms.

Exploring the mediating role of the ventral attention network and somatosensory motor network in the association between childhood trauma and depressive symptoms in major depressive disorders

BACKGROUND

Childhood trauma is closely tied to adult depression, but the neurobiological mechanisms remain unclear. Previous studies suggested associations between depression and large-scale brain networks such as the Ventral Attention Network (VAN) and Somatosensory Motor Network (SMN). This study hypothesized that functional connectivity (FC) within and between these networks mediates the link between childhood trauma and adult depression.

METHODS

The Childhood Trauma Questionnaire (CTQ) assessed developmental experiences, and the Hamilton Rating Scale for Depression (HAMD-17) gauged depressive symptoms. Resting-state functional magnetic resonance imaging (fMRI) analyzed FC within and between the VAN and SMN.

RESULTS

Depression group exhibited significantly higher HAMD and CTQ scores, as well as elevated FC within the VAN and between the VAN and SMN (P < 0.05). Positive correlations were found between HAMD total score and FC within the VAN (P < 0.05, r = 0.35) and between the VAN and SMN (P < 0.05, r = 0.34), as well as with CTQ total score (P < 0.05, r = 0.27). Positive correlations were also observed between CTQ total score and FC within the VAN (P < 0.05, r = 0.31) and between the VAN and SMN (P < 0.05, r = 0.29). In the mediation model, FC within and between the VAN and SMN significantly mediated childhood trauma and depression.

LIMITATIONS

The cross-sectional design limits causal inference. The sample size for different trauma types is relatively small, urging caution in generalizing findings.

CONCLUSIONS

The study underscores the association between depression severity, VAN dysfunction, abnormal VAN-SMN FC, and childhood trauma. These findings contribute to understanding the neurobiological mechanisms underlying childhood trauma and depression.

Travelling beyond time: shared brain system for self-projection in the temporal, political and moral domains

Mental time travel (MTT), a cornerstone of human cognition, enables individuals to mentally project themselves into their past or future. It was shown that this self-projection may extend beyond the temporal domain to the spatial and social domains. What about higher cognitive domains? Twenty-eight participants underwent functional magnetic resonance imaging (fMRI) while self-projecting to different political, moral and temporal perspectives. For each domain, participants were asked to judge their relationship to various people (politicians, moral figures, personal acquaintances) from their actual or projected self-location. Findings showed slower, less accurate responses during self-projection across all domains. fMRI analysis revealed self-projection elicited brain activity at the precuneus, medial and dorsolateral prefrontal cortex, temporoparietal junction and anterior insula, bilaterally and right lateral temporal cortex. Notably, 23.5% of active voxels responded to all three domains and 27% to two domains, suggesting a shared brain system for self-projection. For ordinality judgement (self-reference), 52.5% of active voxels corresponded to the temporal domain specifically. Self-projection activity overlapped mostly with the frontoparietal control network, followed by the default mode network, while self-reference showed a reversed pattern, demonstrating MTT's implication in spontaneous brain activity. MTT may thus be regarded as a 'mental-experiential travel', with self-projection as a domain-general construct and self-reference related mostly to time. This article is part of the theme issue 'Elements of episodic memory: lessons from 40 years of research'.

Dynamic Functional Connectivity Correlates of Trait Mindfulness in Early Adolescence

Background

Trait mindfulness-the tendency to attend to present-moment experiences without judgment-is negatively correlated with adolescent anxiety and depression. Understanding the neural mechanisms that underlie trait mindfulness may inform the neural basis of psychiatric disorders. However, few studies have identified brain connectivity states that are correlated with trait mindfulness in adolescence, and they have not assessed the reliability of such states.

Methods

To address this gap in knowledge, we rigorously assessed the reliability of brain states across 2 functional magnetic resonance imaging scans from 106 adolescents ages 12 to 15 (50% female). We performed both static and dynamic functional connectivity analyses and evaluated the test-retest reliability of how much time adolescents spent in each state. For the reliable states, we assessed associations with self-reported trait mindfulness.

Results

Higher trait mindfulness correlated with lower anxiety and depression symptoms. Static functional connectivity (intraclass correlation coefficients 0.31-0.53) was unrelated to trait mindfulness. Among the dynamic brains states that we identified, most were unreliable within individuals across scans. However, one state, a hyperconnected state of elevated positive connectivity between networks, showed good reliability (intraclass correlation coefficient = 0.65). We found that the amount of time that adolescents spent in this hyperconnected state positively correlated with trait mindfulness.

Conclusions

By applying dynamic functional connectivity analysis on over 100 resting-state functional magnetic resonance imaging scans, we identified a highly reliable brain state that correlated with trait mindfulness. This brain state may reflect a state of mindfulness, or awareness and arousal more generally, which may be more pronounced in people who are higher in trait mindfulness.

A quantitative comparison of atlas parcellations on the human superior temporal sulcus

The superior temporal sulcus (STS) has a functional topography that has been difficult to characterize through traditional approaches. Automated atlas parcellations may be one solution while also being beneficial for both dimensional reduction and standardizing regions of interest, but they yield very different boundary definitions along the STS. Here we evaluate how well machine learning classifiers can correctly identify six social cognitive tasks from STS activation patterns dimensionally reduced using four popular atlases (Glasser et al., 2016; Gordon et al., 2016; Power et al., 2011 as projected onto the surface by Arslan et al., 2018; Schaefer et al., 2018). Functional data was summarized within each STS parcel in one of four ways, then subjected to leave-one-subject-out cross-validation SVM classification. We found that the classifiers could readily label conditions when data was parcellated using any of the four atlases, evidence that dimensional reduction to parcels did not compromise functional fingerprints. Mean activation for the social conditions was the most effective metric for classification in the right STS, whereas all the metrics classified equally well in the left STS. Interestingly, even atlases constructed from random parcellation schemes (null atlases) classified the conditions with high accuracy. We therefore conclude that the complex activation maps on the STS are readily differentiated at a coarse granular level, despite a strict topography having not yet been identified. Further work is required to identify what features have greatest potential to improve the utility of atlases in replacing functional localizers.

Acute Stress Increases Striatal Connectivity With Cortical Regions Enriched for μ and κ Opioid Receptors

BACKGROUND

Understanding the neurobiological effects of stress is critical for addressing the etiology of major depressive disorder (MDD). Using a dimensional approach involving individuals with differing degree of MDD risk, we investigated 1) the effects of acute stress on cortico-cortical and subcortical-cortical functional connectivity (FC) and 2) how such effects are related to gene expression and receptor maps.

METHODS

Across 115 participants (37 control, 39 remitted MDD, 39 current MDD), we evaluated the effects of stress on FC during the Montreal Imaging Stress Task. Using partial least squares regression, we investigated genes whose expression in the Allen Human Brain Atlas was associated with anatomical patterns of stress-related FC change. Finally, we correlated stress-related FC change maps with opioid and GABAA (gamma-aminobutyric acid A) receptor distribution maps derived from positron emission tomography.

RESULTS

Results revealed robust effects of stress on global cortical connectivity, with increased global FC in frontoparietal and attentional networks and decreased global FC in the medial default mode network. Moreover, robust increases emerged in FC of the caudate, putamen, and amygdala with regions from the ventral attention/salience network, frontoparietal network, and motor networks. Such regions showed preferential expression of genes involved in cell-to-cell signaling (OPRM1, OPRK1, SST, GABRA3, GABRA5), similar to previous genetic MDD studies.

CONCLUSIONS

Acute stress altered global cortical connectivity and increased striatal connectivity with cortical regions that express genes that have previously been associated with imaging abnormalities in MDD and are rich in μ and κ opioid receptors. These findings point to overlapping circuitry underlying stress response, reward, and MDD.

Precision Functional Mapping to Advance Developmental Psychiatry Research

Many psychiatric conditions have their roots in early development. Individual differences in prenatal brain function (which is influenced by a combination of genetic risk and the prenatal environment) likely interact with individual differences in postnatal experience, resulting in substantial variation in brain functional organization and development in infancy. Neuroimaging has been a powerful tool for understanding typical and atypical brain function and holds promise for uncovering the neurodevelopmental basis of psychiatric illness; however, its clinical utility has been relatively limited thus far. A substantial challenge in this endeavor is the traditional approach of averaging brain data across groups despite individuals varying in their brain organization, which likely obscures important clinically relevant individual variation. Precision functional mapping (PFM) is a neuroimaging technique that allows the capture of individual-specific and highly reliable functional brain properties. Here, we discuss how PFM, through its focus on individuals, has provided novel insights for understanding brain organization across the life span and its promise in elucidating the neural basis of psychiatric disorders. We first summarize the extant literature on PFM in normative populations, followed by its limited utilization in studying psychiatric conditions in adults. We conclude by discussing the potential for infant PFM in advancing developmental precision psychiatry applications, given that many psychiatric disorders start during early infancy and are associated with changes in individual-specific functional neuroanatomy. By exploring the intersection of PFM, development, and psychiatric research, this article underscores the importance of individualized approaches in unraveling the complexities of brain function and improving clinical outcomes across development.

Insights on cognitive reorganization after hemispherectomy in Rasmussen's encephalitis. A narrative review

Rasmussen's encephalitis is a rare neurological pathology affecting one cerebral hemisphere, therefore, posing unique challenges. Patients may undergo hemispherectomy, a surgical procedure after which cognitive development occurs in the isolated contralateral hemisphere. This rare situation provides an excellent opportunity to evaluate brain plasticity and cognitive recovery at a hemispheric level. This literature review synthesizes the existing body of research on cognitive recovery following hemispherectomy in Rasmussen patients, considering cognitive domains and modulatory factors that influence cognitive outcomes. While language function has traditionally been the focus of postoperative assessments, there is a growing acknowledgment of the need to broaden the scope of language investigation in interaction with other cognitive domains and to consider cognitive scaffolding in development and recovery. By synthesizing findings reported in the literature, we delineate how language functions may find support from the right hemisphere after left hemispherectomy, but also how, beyond language, global cognitive functioning is affected. We highlight the critical influence of several factors on postoperative cognitive outcomes, including the timing of hemispherectomy and the baseline preoperative cognitive status, pointing to early surgical intervention as predictive of better cognitive outcomes. However, further specific studies are needed to confirm this correlation. This review aims to emphasize a better understanding of mechanisms underlying hemispheric specialization and plasticity in humans, which are particularly important for both clinical and research advancements. This narrative review underscores the need for an integrative approach based on cognitive scaffolding to provide a comprehensive understanding of mechanisms underlying the reorganization in Rasmussen patients after hemispherectomy.

Processing of social closeness in the human brain

Healthy social life requires relationships in different levels of personal closeness. Based on ethological, sociological, and psychological evidence, social networks have been divided into five layers, gradually increasing in size and decreasing in personal closeness. Is this division also reflected in brain processing of social networks? During functional MRI, 21 participants compared their personal closeness to different individuals. We examined the brain volume showing differential activation for varying layers of closeness and found that a disproportionately large portion of this volume (80%) exhibited preference for individuals closest to participants, while separate brain regions showed preference for all other layers. Moreover, this bipartition reflected cortical preference for different sizes of physical spaces, as well as distinct subsystems of the default mode network. Our results support a division of the neurocognitive processing of social networks into two patterns depending on personal closeness, reflecting the unique role intimately close individuals play in our social lives.

Cognition and Ventral Attention Network Connectivity: Associations With Treatment Response in Posttraumatic Stress Disorder

OBJECTIVE

Posttraumatic stress disorder (PTSD) is a highly heterogeneous disorder, which makes it difficult to link clinical phenotypes with biomarkers to improve treatment outcomes. Findings from previous studies suggest that cognitive measures such as verbal memory or attention paired with within-ventral attention network (VAN) or salience network resting-state functional connectivity may predict treatment response among individuals with PTSD.

METHODS

In a sample comprising 20 individuals with PTSD and 10 healthy control group individuals, the investigators subtyped individuals by using both discriminant function analysis and standardized norms for a single measure of memory and neuropsychological batteries of memory, attention, and executive functioning; attempted to replicate previous findings of lower within-VAN connectivity among individuals with cognitive impairment; and explored whether within-VAN connectivity paired with cognitive impairment predicted treatment outcomes.

RESULTS

PTSD patients with cognitive impairment (defined by using a discriminant function analysis with verbal memory performance) had greater within-VAN resting-state functional connectivity compared with control group individuals and cognitively intact PTSD patients at a level that fell short of statistical significance (F=3.41; df=2, 21; ηp2=0.237). The interaction between verbal memory performance and within-VAN connectivity also predicted treatment-related change in PTSD symptoms at a level that also fell short of statistical significance (β=-0.442).

CONCLUSIONS

These findings somewhat support the clinical utility of identifying cognitive phenotypes within PTSD (by using discriminant function analysis and verbal memory performance) to predict treatment outcomes.

Deficient salience and default mode functional integration in high worry-proneness subject: a connectome-wide association study

Worry has been conceptualized as a relatively uncontrollable chain of thought that increases the risk of mental problems, such as anxiety disorders. Here, we examined the link between individual variation in the functional connectome and worry proneness, which remains unclear. A total of 32 high worry-proneness (HWP) subjects and 25 low worry-proneness (LWP) subjects were recruited. We conducted multivariate distance-based matrix regression to identify phenotypic relationships in high-dimensional brain resting-state functional connectivity data from HWP subjects. Multiple hub regions, including key brain nodes of the salience network (SN) and default mode network (DMN), were identified in HWP subjects. Follow-up analyses revealed that a high worry-proneness score was dominated by functional connectivity between the SN and the DMN. Moreover, HWP subjects showed hypoconnectivity between the cerebellum and the SN and DMN compared with LWP subjects. This cross-sectional study could not fully measure the causal relationships between changes in functional networks and worry proneness in healthy subjects. Functional changes in the cerebellum-cortical region might affect the modulation of external stimuli processing. Together, our results provide new insight into the role of key networks, including the SN, DMN and cerebellum, in understanding the potential mechanism underlying the high worry dimension in healthy subjects.

Prognostic model for predicting Alzheimer's disease conversion using functional connectome manifolds

BACKGROUND

Early detection of Alzheimer's disease (AD) is essential for timely management and consideration of therapeutic options; therefore, detecting the risk of conversion from mild cognitive impairment (MCI) to AD is crucial during neurodegenerative progression. Existing neuroimaging studies have mostly focused on group differences between individuals with MCI (or AD) and cognitively normal (CN), discarding the temporal information of conversion time. Here, we aimed to develop a prognostic model for AD conversion using functional connectivity (FC) and Cox regression suitable for conversion event modeling.

METHODS

We developed a prognostic model using a large-scale Alzheimer's Disease Neuroimaging Initiative dataset, and it was validated using external data obtained from the Open Access Series of Imaging Studies. We considered individuals who were initially CN or had MCI but progressed to AD and those with MCI with no progression to AD during the five-year follow-up period. As the exact conversion time to AD is unknown, we inferred this information using imputation approaches. We generated cortex-wide principal FC gradients using manifold learning techniques and computed subcortical-weighted manifold degrees from baseline functional magnetic resonance imaging data. A penalized Cox regression model with an elastic net penalty was adopted to define a risk score predicting the risk of conversion to AD, using FC gradients and clinical factors as regressors.

RESULTS

Our prognostic model predicted the conversion risk and confirmed the role of imaging-derived manifolds in the conversion risk. The brain regions that largely contributed to predicting AD conversion were the heteromodal association and visual cortices, as well as the caudate and hippocampus. Our risk score based on Cox regression was consistent with the expected disease trajectories and correlated with positron emission tomography tracer uptake and symptom severity, reinforcing its clinical usefulness. Our findings were validated using an independent dataset. The cross-sectional application of our model showed a higher risk for AD than that for MCI, which correlated with symptom severity scores in the validation dataset.

CONCLUSION

We proposed a prognostic model predicting the risk of conversion to AD. The associated risk score may provide insights for early intervention in individuals at risk of AD conversion.

Language proficiency is associated with neural representational dimensionality of semantic concepts

Previous studies suggest that semantic concepts are characterized by high-dimensional neural representations and that language proficiency affects semantic processing. However, it is not clear whether language proficiency modulates the dimensional representations of semantic concepts at the neural level. To address this question, the present study adopted principal component analysis (PCA) and representational similarity analysis (RSA) to examine the differences in representational dimensionalities (RDs) and in semantic representations between words in highly proficient (Chinese) and less proficient (English) language. PCA results revealed that language proficiency increased the dimensions of lexical representations in the left inferior frontal gyrus, temporal pole, inferior temporal gyrus, supramarginal gyrus, angular gyrus, and fusiform gyrus. RSA results further showed that these regions represented semantic information and that higher semantic representations were observed in highly proficient language relative to less proficient language. These results suggest that language proficiency is associated with the neural representational dimensionality of semantic concepts.

Optimizing Antidepressant Efficacy: Generalizable Multimodal Neuroimaging Biomarkers for Prediction of Treatment Response

Major depressive disorder (MDD) is a common and often severe condition that profoundly diminishes quality of life for individuals across ages and demographic groups. Unfortunately, current antidepressant and psychotherapeutic treatments exhibit limited efficacy and unsatisfactory response rates in a substantial number of patients. The development of effective therapies for MDD is hindered by the insufficiently understood heterogeneity within the disorder and its elusive underlying mechanisms. To address these challenges, we present a target-oriented multimodal fusion framework that robustly predicts antidepressant response by integrating structural and functional connectivity data (sertraline: R-squared = 0.31; placebo: R-squared = 0.22). Remarkably, the sertraline response biomarker is further tested on an independent escitalopram-medicated cohort of MDD patients, validating its generalizability (p = 0.01) and suggesting an overlap of psychopharmacological mechanisms across selective serotonin reuptake inhibitors. Through the model, we identify multimodal neuroimaging biomarkers of antidepressant response and observe that sertraline and placebo show distinct predictive patterns. We further decompose the overall predictive patterns into constitutive network constellations with generalizable structural-functional co-variation, which exhibit treatment-specific association with personality traits and behavioral/cognitive task performance. Our innovative and interpretable multimodal framework provides novel and reliable insights into the intricate neuropsychopharmacology of antidepressant treatment, paving the way for advances in precision medicine and development of more targeted antidepressant therapeutics.

Association of Normative and Non-Normative Brain Networks With Cognitive Function in Patients With Temporal Lobe Epilepsy

BACKGROUND AND OBJECTIVES

Despite their temporal lobe pathology, a significant subgroup of patients with temporal lobe epilepsy (TLE) is able to maintain normative cognitive functioning. In this study, we identify patients with TLE with intact vs impaired neurocognitive profiles and interrogate for the presence of both normative and highly individual intrinsic connectivity networks (ICNs)-all toward understanding the transition from impaired to intact neurocognitive status.

METHODS

This retrospective cross-sectional study included patients with TLE and matched healthy controls (HCs) from the Thomas Jefferson Comprehensive Epilepsy Center. Functional MRI data were decomposed using independent component analysis to obtain individualized ICNs. In this article, we calculated the degree of match between individualized ICNs and canonical ICNs (e.g., 17 resting-state networks by Yeo et al.) and divided each participant's ICNs into normative or non-normative status based on the degree of match.

RESULTS

100 patients with TLE (mean age 42.0 [SD: 13.7] years, 47 women) and 92 HCs were included in this study. We found that the individualized networks matched to the canonical networks less well in the cognitively impaired (n = 24) compared with the cognitively intact (n = 63) patients with TLE by 2-way mixed-measures analysis of variance (impaired vs intact mean difference [MD] -0.165 [-0.317, -0.013], p = 0.028). The cognitively impaired patients showed significant abnormalities in the profiles of both normative (impaired vs intact MD -0.537 [-0.998, -0.076], p = 0.017, intact vs HC MD -0.221 [-0.536, 0.924], p = 0.220, and impaired vs HC MD -0.759 [-1.200, -0.319], p < 0.001) and non-normative networks (impaired vs intact MD 0.484 [0.030, 0.937], p = 0.033, intact vs HC MD 0.369 [0.059, 0.678], p = 0.014, and impaired vs HC MD 0.853 [0.419, 1.286], p < 0.001) while the intact patients showed abnormalities only in non-normative networks. At the same time, we found that normative networks held a strong, positive association with the neuropsychological measures, with this association negative in non-normative networks.

DISCUSSION

Our data demonstrated that significant cognitive deficits are associated with the status of both canonical and highly individual ICNs, making clear that the transition from intact to impaired cognitive status is not simply the result of disruption to normative brain networks.

Neuro-Immune Communication at the Core of Craving-Associated Brain Structural Network Reconfiguration in Methamphetamine Users

Methamphetamine (MA) use disorder is a chronic neurotoxic brain disease characterized by a high risk of relapse driven by intense cravings. However, the neurobiological signatures of cravings remain unclear, limiting the effectiveness of various treatment methods. Diffusion MRI (dMRI) scans from 62 MA users and 57 healthy controls (HC) were used in this study. The MA users were longitudinally followed up during their period of long-term abstinence (duration of long-term abstinence: 347.52±99.25 days). We systematically quantified the control ability of each brain region for craving-associated state transitions using network control theory from a causal perspective. Craving-associated structural alterations (CSA) were investigated through multivariate group comparisons and biological relevance analysis. The neural mechanisms underlying CSA were elucidated using transcriptomic and neurochemical analyses. We observed that long-term abstinence-induced structural alterations significantly influenced the state transition energy involved in the cognitive control response to external information, which correlated with changes in craving scores (r ∼ 0.35, P <0.01). Our causal network analysis further supported the crucial role of the prefrontal cortex (PFC) in craving mechanisms. Notably, while the PFC is central to the craving, the CSAs were distributed widely across multiple brain regions (PFDR<0.05), with strong alterations in somatomotor regions (PFDR<0.05) and moderate alterations in high-level association networks (PFDR<0.05). Additionally, transcriptomic, chemical compounds, cell-type analyses, and molecular imaging collectively highlight the influence of neuro-immune communication on human craving modulation. Our results offer an integrative, multi-scale perspective on unraveling the neural underpinnings of craving and suggest that neuro-immune signaling may be a promising target for future human addiction therapeutics.

Locus coeruleus co-activation patterns at rest show higher state persistence in patients with dissociative seizures: A Pilot Study

OBJECTIVE

Dissociative seizures are paroxysmal disruptions of awareness and behavioral control in the context of affective arousal. Alterations in stress-related endocrine function have been demonstrated, but the timescale of dissociation suggests that the central locus coeruleus (LC) noradrenergic system is likely pivotal. Here, we investigate whether LC activation at rest is associated with altered brain network dynamics.

METHODS

A preliminary co-activation pattern (CAP) analysis of resting-state functional magnetic resonance imaging (fMRI) in 14 patients with dissociative seizures and 14 healthy controls was performed by using the LC as a seeding region. The red nucleus served as a control condition. Entry rates, durations, and state transition probabilities of identified CAPs were calculated. Analyses were corrected for demographic, technical, and clinical confounders including depression and anxiety.

RESULTS

Three LC-related CAPs were identified, with the dominant two showing inverse activations and deactivations of the default mode network and the attention networks, respectively. Analysis of transition probabilities between and within the three CAPs revealed higher state persistence in patients compared to healthy controls for both CAP2LC (Cohen's d = -0.55; p = 0.01) and CAP3LC (Cohen's d = -0.57; p = 0.01). The control analysis using the red nucleus as a seed yielded similar CAPs, but no significant between-group differences in transition probabilities.

SIGNIFICANCE

Higher state persistence of LC-CAPs in patients with dissociative seizures generates the novel hypothesis that arousal-related impairments of network switching might be a candidate neural mechanism of dissociation.

PLAIN LANGUAGE SUMMARY

Dissociative seizures often arise during high affective arousal. The locus coeruleus is a brain structure involved in managing such acute arousal states. We investigated whether the activity of the locus coeruleus correlates with activity in other regions of the brain (which we refer to as "brain states"), and whether those brain states were different between patients with dissociative seizures and healthy controls. We found that patients tended to stay in certain locus coeruleus-dependent brain states instead of switching between them. This might be related to the loss of awareness and disruptions of brain functions ("dissociation") that patients experience during seizures.

Heterogenous brain activations across individuals localize to a common network

Task functional magnetic resonance imaging research has generally shielded away from studying individuals due to the low reproducibility. Here, we propose that heterogeneous brain activations across individuals localize to a common network. To test this hypothesis, we use working memory (WM) as our example. First, we showed that discrete-brain-based reproducibility of brain activation during WM across individuals was low. Then, we used activation network mapping (ANM) technique to identify each individual's brain network of WM and found that network-based reproducibility was rather high. Prediction analyses using machine learning algorithms indicated that individual WM networks identified via ANM can predict WM behavioral performance. This predictive ability even outperformed that of brain activations. Our study provides a new explanation on the low reproducibility of brain activations across individuals. The results suggest that ANM can be used to identify individual brain networks of cognitive processes, thus promising broad potential applications.

Multilayer network analysis reveals instability of brain dynamics in untreated first-episode schizophrenia

Although aberrant static functional brain network activity has been reported in schizophrenia, little is known about how the dynamics of neural function are altered in first-episode schizophrenia and are modulated by antipsychotic treatment. The baseline resting-state functional magnetic resonance imaging data were acquired from 122 first-episode drug-naïve schizophrenia patients and 128 healthy controls (HCs), and 44 patients were rescanned after 1-year of antipsychotic treatment. Multilayer network analysis was applied to calculate the network switching rates between brain states. Compared to HCs, schizophrenia patients at baseline showed significantly increased network switching rates. This effect was observed mainly in the sensorimotor (SMN) and dorsal attention networks (DAN), and in temporal and parietal regions at the nodal level. Switching rates were reduced after 1-year of antipsychotic treatment at the global level and in DAN. Switching rates at baseline at the global level and in the inferior parietal lobule were correlated with the treatment-related reduction of negative symptoms. These findings suggest that instability of functional network activity plays an important role in the pathophysiology of acute psychosis in early-stage schizophrenia. The normalization of network stability after antipsychotic medication suggests that this effect may represent a systems-level mechanism for their therapeutic efficacy.

Default mode network electrophysiological dynamics and causal role in creative thinking

The default mode network (DMN) is a widely distributed, intrinsic brain network thought to play a crucial role in internally directed cognition. The present study employs stereo-EEG in 13 human patients, obtaining high resolution neural recordings across multiple canonical DMN regions during two processes that have been associated with creative thinking: spontaneous and divergent thought. We probe these two DMN-associated higher cognitive functions through mind wandering and alternate uses tasks, respectively. Our results reveal DMN recruitment during both tasks, as well as a task-specific dissociation in spatiotemporal response dynamics. When compared to the fronto-parietal network, DMN activity was characterized by a stronger increase in gamma band power (30-70 Hz) coupled with lower theta band power (4-8 Hz). The difference in activity between the two networks was especially strong during the mind wandering task. Within the DMN, we found that the tasks showed different dynamics, with the alternate uses task engaging the DMN more during the initial stage of the task, and mind wandering in the later stage. Gamma power changes were mainly driven by lateral DMN sites, while theta power displayed task-specific effects. During alternate uses task, theta changes did not show spatial differences within the DMN, while mind wandering was associated to an early lateral and late dorsomedial DMN engagement. Furthermore, causal manipulations of DMN regions using direct cortical stimulation preferentially decreased the originality of responses in the alternative uses task, without affecting fluency or mind wandering. Our results suggest that DMN activity is flexibly modulated as a function of specific cognitive processes and supports its causal role in divergent thinking. These findings shed light on the neural constructs supporting different forms of cognition and provide causal evidence for the role of DMN in the generation of original connections among concepts.

Intrinsic functional networks for distinct sources of error in visual working memory

Visual working memory (VWM) is a core cognitive function wherein visual information is stored and manipulated over short periods. Response errors in VWM tasks arise from the imprecise memory of target items, swaps between targets and nontargets, and random guesses. However, it remains unclear whether these types of errors are underpinned by distinct neural networks. To answer this question, we recruited 80 healthy adults to perform delayed estimation tasks and acquired their resting-state functional magnetic resonance imaging scans. The tasks required participants to reproduce the memorized visual feature along continuous scales, which, combined with mixture distribution modeling, allowed us to estimate the measures of memory precision, swap errors, and random guesses. Intrinsic functional connectivity within and between different networks, identified using a hierarchical clustering approach, was estimated for each participant. Our analyses revealed that higher memory precision was associated with increased connectivity within a frontal-opercular network, as well as between the dorsal attention network and an angular-gyrus-cerebellar network. We also found that coupling between the frontoparietal control network and the cingulo-opercular network contributes to both memory precision and random guesses. Our findings demonstrate that distinct sources of variability in VWM performance are underpinned by different yet partially overlapping intrinsic functional networks.

Integration patterns of functional brain networks can predict the response to abdominal acupuncture in patients with major depressive disorder

Abdominal acupuncture has definite efficacy for major depressive disorder (MDD). Our study examined how abdominal acupuncture regulates the integration within and between brain networks of MDD patients by neuroimaging and whether this functional integration can predict the efficacy. Forty-six female MDD patients were randomly divided into a fluoxetine + real acupuncture group (n = 22) and a fluoxetine + sham acupuncture group (n = 24). The differences in functional magnetic resonance imaging data in the intra- and inter-network functional connectivity (FC) of the default mode network (DMN), affective network (AN), salience network (SN), and cognitive control network (CCN) between the two groups were analyzed. The FCs in brain regions with the inter-group differences and support vector regression were used to predict the efficacy of abdominal acupuncture. Our results showed: that the intra- and inter-network FCs of DMN, AN, SN, and CCN could be changed by abdominal acupuncture. Using the baseline FCs within AN and DMN or AN-DMN as characteristics, combined with support vector regression, could better predict the efficacy of acupuncture. Our study suggests that abdominal acupuncture could treat MDD by regulating the integration of the functional networks DMN, AN, SN, and CCN. The baseline FCs within the DMN and AN or between them could be used as neural markers for predicting the efficacy of abdominal acupuncture.

Evolutionarily conserved fMRI network dynamics in the mouse, macaque, and human brain

Evolutionarily relevant networks have been previously described in several mammalian species using time-averaged analyses of fMRI time-series. However, fMRI network activity is highly dynamic and continually evolves over timescales of seconds. Whether the dynamic organization of resting-state fMRI network activity is conserved across mammalian species remains unclear. Using frame-wise clustering of fMRI time-series, we find that intrinsic fMRI network dynamics in awake male macaques and humans is characterized by recurrent transitions between a set of 4 dominant, neuroanatomically homologous fMRI coactivation modes (C-modes), three of which are also plausibly represented in the male rodent brain. Importantly, in all species C-modes exhibit species-invariant dynamic features, including preferred occurrence at specific phases of fMRI global signal fluctuations, and a state transition structure compatible with infraslow coupled oscillator dynamics. Moreover, dominant C-mode occurrence reconstitutes the static organization of the fMRI connectome in all species, and is predictive of ranking of corresponding fMRI connectivity gradients. These results reveal a set of species-invariant principles underlying the dynamic organization of fMRI networks in mammalian species, and offer novel opportunities to relate fMRI network findings across the phylogenetic tree.

Intersubject Dynamic Conditional Correlation: A Novel Method to Track the Framewise Network Implication during Naturalistic Stimuli

Background: Naturalistic stimuli have become increasingly popular in modern cognitive neuroscience. These stimuli have high ecological validity due to their rich and multilayered features. However, their complexity also presents methodological challenges for uncovering neural network reconfiguration. Dynamic functional connectivity using the sliding-window technique is commonly used but has several limitations. In this study, we introduce a new method called intersubject dynamic conditional correlation (ISDCC). Method: ISDCC uses intersubject analysis to remove intrinsic and non-neuronal signals, retaining only intersubject-consistent stimuli-induced signals. It then applies dynamic conditional correlation (DCC) based on the generalized autoregressive conditional heteroskedasticity to calculate the framewise functional connectivity. To validate ISDCC, we analyzed simulation data with known network reconfiguration patterns and two publicly available narrative functional Magnetic Resonance Imaging (fMRI) datasets. Results: (1) ISDCC accurately unveiled the underlying network reconfiguration patterns in simulation data, demonstrating greater sensitivity than DCC; (2) ISDCC identified synchronized network reconfiguration patterns across listeners; (3) ISDCC effectively differentiated between stimulus types with varying temporal coherence; and (4) network reconfigurations unveiled by ISDCC were significantly correlated with listener engagement during narrative comprehension. Conclusion: ISDCC is a precise and dynamic method for tracking network implications in response to naturalistic stimuli.

An improved spectral clustering method for accurate detection of brain resting-state networks

This paper proposes a data-driven analysis method to accurately partition large-scale resting-state functional brain networks from fMRI data. The method is based on a spectral clustering algorithm and combines eigenvector direction selection with Pearson correlation clustering in the spectral space. The method is an improvement on available spectral clustering methods, capable of robustly identifying active brain networks consistent with those from model-driven methods at different noise levels, even at the noise level of real fMRI data.

Contribution of resting-state functional connectivity of the subgenual anterior cingulate to prediction of antidepressant efficacy in patients with major depressive disorder

This study investigated how resting-state functional connectivity (rsFC) of the subgenual anterior cingulate cortex (sgACC) predicts antidepressant response in patients with major depressive disorder (MDD). Eighty-seven medication-free MDD patients underwent baseline resting-state functional MRI scans. After 12 weeks of escitalopram treatment, patients were classified into remission depression (RD, n = 42) and nonremission depression (NRD, n = 45) groups. We conducted two analyses: a voxel-wise rsFC analysis using sgACC as a seed to identify group differences, and a prediction model based on the sgACC rsFC map to predict treatment efficacy. Haufe transformation was used to interpret the predictive rsFC features. The RD group showed significantly higher rsFC between the sgACC and regions in the fronto-parietal network (FPN), including the bilateral dorsolateral prefrontal cortex (DLPFC) and bilateral inferior parietal lobule (IPL), compared to the NRD group. These sgACC rsFC measures correlated positively with symptom improvement. Baseline sgACC rsFC also significantly predicted treatment response after 12 weeks, with a mean accuracy of 72.64% (p < 0.001), mean area under the curve of 0.74 (p < 0.001), mean specificity of 0.82, and mean sensitivity of 0.70 in 10-fold cross-validation. The predictive voxels were mainly within the FPN. The rsFC between the sgACC and FPN is a valuable predictor of antidepressant response in MDD patients. These findings enhance our understanding of the neurobiological mechanisms underlying treatment response and could help inform personalized treatment strategies for MDD.

Changes in the structure of spontaneous speech predict the disruption of hierarchical brain organization in first-episode psychosis

Psychosis implicates changes across a broad range of cognitive functions. These functions are cortically organized in the form of a hierarchy ranging from primary sensorimotor (unimodal) to higher-order association cortices, which involve functions such as language (transmodal). Language has long been documented as undergoing structural changes in psychosis. We hypothesized that these changes as revealed in spontaneous speech patterns may act as readouts of alterations in the configuration of this unimodal-to-transmodal axis of cortical organization in psychosis. Results from 29 patients with first-episodic psychosis (FEP) and 29 controls scanned with 7 T resting-state fMRI confirmed a compression of the cortical hierarchy in FEP, which affected metrics of the hierarchical distance between the sensorimotor and default mode networks, and of the hierarchical organization within the semantic network. These organizational changes were predicted by graphs representing semantic and syntactic associations between meaningful units in speech produced during picture descriptions. These findings unite psychosis, language, and the cortical hierarchy in a single conceptual scheme, which helps to situate language within the neurocognition of psychosis and opens the clinical prospect for mental dysfunction to become computationally measurable in spontaneous speech.

Ventral attention network connectivity is linked to cortical maturation and cognitive ability in childhood

The human brain experiences functional changes through childhood and adolescence, shifting from an organizational framework anchored within sensorimotor and visual regions into one that is balanced through interactions with later-maturing aspects of association cortex. Here, we link this profile of functional reorganization to the development of ventral attention network connectivity across independent datasets. We demonstrate that maturational changes in cortical organization link preferentially to within-network connectivity and heightened degree centrality in the ventral attention network, whereas connectivity within network-linked vertices predicts cognitive ability. This connectivity is associated closely with maturational refinement of cortical organization. Children with low ventral attention network connectivity exhibit adolescent-like topographical profiles, suggesting that attentional systems may be relevant in understanding how brain functions are refined across development. These data suggest a role for attention networks in supporting age-dependent shifts in cortical organization and cognition across childhood and adolescence.

Association of physical function with connectivity in the sensorimotor and dorsal attention networks: why examining specific components of physical function matters

Declining physical function with aging is associated with structural and functional brain network organization. Gaining a greater understanding of network associations may be useful for targeting interventions that are designed to slow or prevent such decline. Our previous work demonstrated that the Short Physical Performance Battery (eSPPB) score and body mass index (BMI) exhibited a statistical interaction in their associations with connectivity in the sensorimotor cortex (SMN) and the dorsal attention network (DAN). The current study examined if components of the eSPPB have unique associations with these brain networks. Functional magnetic resonance imaging was performed on 192 participants in the BNET study, a longitudinal and observational trial of community-dwelling adults aged 70 or older. Functional brain networks were generated for resting state and during a motor imagery task. Regression analyses were performed between eSPPB component scores (gait speed, complex gait speed, static balance, and lower extremity strength) and BMI with SMN and DAN connectivity. Gait speed, complex gait speed, and lower extremity strength significantly interacted with BMI in their association with SMN at rest. Gait speed and complex gait speed were interacted with BMI in the DAN at rest while complex gait speed, static balance, and lower extremity strength interacted with BMI in the DAN during motor imagery. Results demonstrate that different components of physical function, such as balance or gait speed and BMI, are associated with unique aspects of brain network organization. Gaining a greater mechanistic understanding of the associations between low physical function, body mass, and brain physiology may lead to the development of treatments that not only target specific physical function limitations but also specific brain networks.

A meta-analysis of cognitive flexibility in aging: Perspective from functional network and lateralization

Cognitive flexibility, the ability to switch between mental processes to generate appropriate behavioral responses, is reduced with typical aging. Previous studies have found that age-related declines in cognitive flexibility are often accompanied by variations in the activation of multiple regions. However, no meta-analyses have examined the relationship between cognitive flexibility in aging and age-related variations in activation within large-scale networks. Here, we conducted a meta-analysis employing multilevel kernel density analysis to identify regions with different activity patterns between age groups, and determined how these regions fall into functional networks. We also employed lateralization analysis to explore the spatial distribution of regions exhibiting group differences in activation. The permutation tests based on Monte Carlo simulation were used to determine the significance of the activation and lateralization results. The results showed that cognitive flexibility in aging was associated with both decreased and increased activation in several functional networks. Compared to young adults, older adults exhibited increased activation in the default mode, dorsal attention, ventral attention, and somatomotor networks, while displayed decreased activation in the visual network. Moreover, we found a global-level left lateralization for regions with decreased activation, but no lateralization for regions with higher activation in older adults. At the network level, the regions with decreased activation were left-lateralized, while the regions with increased activation showed varying lateralization patterns within different networks. To sum up, we found that networks that support various mental functions contribute to age-related variations in cognitive flexibility. Additionally, the aging brain exhibited network-dependent activation and lateralization patterns in response to tasks involving cognitive flexibility. We highlighted that the comprehensive meta-analysis in this study offered new insights into understanding cognitive flexibility in aging from a network perspective.

Reassessing the Functional Significance of Blood Oxygen Level Dependent Signal Variability

BOLD signal variability (SDBOLD) has emerged as a unique measure of the adaptive properties of neural systems that facilitate fast, stable responding, based on claims that SDBOLD is independent of mean BOLD signal (meanBOLD) and is a powerful predictor of behavioral performance. We challenge these two claims. First, the apparent independence of SDBOLD and meanBOLD may reflect the presence of deactivations; we hypothesize that although SDBOLD may not be related to raw meanBOLD, it will be linearly related to "absolute" meanBOLD. Second, the observed relationship between SDBOLD and performance may be an artifact of using fixed-length trials longer than RTs. Such designs provide opportunities to toggle between on- and off-task states, and fast responders likely engage in more frequent state-switching, thereby artificially elevating SDBOLD. We hypothesize that SDBOLD will be higher and more strongly related to performance when using such fixed-length trials relative to self-paced trials that terminate upon a response. We test these two hypotheses in an fMRI study using blocks of fixed-length or self-paced trials. Results confirmed both hypotheses: (1) SDBOLD was robustly related with absolute meanBOLD, and (2) toggling between on- and off-task states during fixed-length trials reliably contributed to SDBOLD. Together, these findings suggest that a reappraisal of the functional significance of SDBOLD as a unique marker of cognitive performance is warranted.

An Adaptively Weighted Averaging Method for Regional Time Series Extraction of fMRI-Based Brain Decoding

Brain decoding that classifies cognitive states using the functional fluctuations of the brain can provide insightful information for understanding the brain mechanisms of cognitive functions. Among the common procedures of decoding the brain cognitive states with functional magnetic resonance imaging (fMRI), extracting the time series of each brain region after brain parcellation traditionally averages across the voxels within a brain region. This neglects the spatial information among the voxels and the requirement of extracting information for the downstream tasks. In this study, we propose to use a fully connected neural network that is jointly trained with the brain decoder to perform an adaptively weighted average across the voxels within each brain region. We perform extensive evaluations by cognitive state decoding, manifold learning, and interpretability analysis on the Human Connectome Project (HCP) dataset. The performance comparison of the cognitive state decoding presents an accuracy increase of up to 5% and stable accuracy improvement under different time window sizes, resampling sizes, and training data sizes. The results of manifold learning show that our method presents a considerable separability among cognitive states and basically excludes subject-specific information. The interpretability analysis shows that our method can identify reasonable brain regions corresponding to each cognitive state. Our study would aid the improvement of the basic pipeline of fMRI processing.

Topological cluster statistic (TCS): Toward structural connectivity-guided fMRI cluster enhancement

Functional magnetic resonance imaging (fMRI) studies most commonly use cluster-based inference to detect local changes in brain activity. Insufficient statistical power and disproportionate false-positive rates reportedly hinder optimal inference. We propose a structural connectivity-guided clustering framework, called topological cluster statistic (TCS), that enhances sensitivity by leveraging white matter anatomical connectivity information. TCS harnesses multimodal information from diffusion tractography and functional imaging to improve task fMRI activation inference. Compared to conventional approaches, TCS consistently improves power over a wide range of effects. This improvement results in a 10%-50% increase in local sensitivity with the greatest gains for medium-sized effects. TCS additionally enables inspection of underlying anatomical networks and thus uncovers knowledge regarding the anatomical underpinnings of brain activation. This novel approach is made available in the PALM software to facilitate usability. Given the increasing recognition that activation reflects widespread, coordinated processes, TCS provides a way to integrate the known structure underlying widespread activations into neuroimaging analyses moving forward.

Altered Structural Connectivity and Functional Brain Dynamics in Individuals With Heavy Alcohol Use Elucidated via Network Control Theory

BACKGROUND

Heavy alcohol use and its associated conditions, such as alcohol use disorder, impact millions of individuals worldwide. While our understanding of the neurobiological correlates of alcohol use has evolved substantially, we still lack models that incorporate whole-brain neuroanatomical, functional, and pharmacological information under one framework.

METHODS

Here, we utilized diffusion and functional magnetic resonance imaging to investigate alterations to brain dynamics in 130 individuals with a high amount of current alcohol use. We compared these alcohol-using individuals to 308 individuals with minimal use of any substances.

RESULTS

We found that individuals with heavy alcohol use had less dynamic and complex brain activity, and through leveraging network control theory, had increased control energy to complete transitions between activation states. Furthermore, using separately acquired positron emission tomography data, we deployed an in silico evaluation demonstrating that decreased D2 receptor levels, as found previously in individuals with alcohol use disorder, may relate to our observed findings.

CONCLUSIONS

This work demonstrates that whole-brain, multimodal imaging information can be combined under a network control framework to identify and evaluate neurobiological correlates and mechanisms of heavy alcohol use.

Prediction of depressive symptoms severity based on sleep quality, anxiety, and gray matter volume: a generalizable machine learning approach across three datasets

BACKGROUND

Depressive symptoms are rising in the general population, but their associated factors are unclear. Although the link between sleep disturbances and depressive symptoms severity (DSS) is reported, the predictive role of sleep on DSS and the impact of anxiety and the brain on their relationship remained obscure.

METHODS

Using three population-based datasets (N = 1813), we trained the machine learning models in the primary dataset (N = 1101) to assess the predictive role of sleep quality, anxiety problems, and brain structural (and functional) measurements on DSS, then we tested our models' performance in two independent datasets (N = 378, N = 334) to test the generalizability of our findings. Furthermore, we applied our model to a smaller longitudinal subsample (N = 66). In addition, we performed a mediation analysis to identify the role of anxiety and brain measurements on the sleep quality and DSS association.

FINDINGS

Sleep quality could predict individual DSS (r = 0.43, R2 = 0.18, rMSE = 2.73), and adding anxiety, contrary to brain measurements, strengthened its prediction performance (r = 0.67, R2 = 0.45, rMSE = 2.25). Importantly, out-of-cohort validations in other cross-sectional datasets and a longitudinal subsample provided robust similar results. Furthermore, anxiety scores, contrary to brain measurements, mediated the association between sleep quality and DSS.

INTERPRETATION

Poor sleep quality could predict DSS at the individual subject level across three datasets. Anxiety scores not only increased the predictive model's performance but also mediated the link between sleep quality and DSS.

FUNDING

The study is supported by Helmholtz Imaging Platform grant (NimRLS, ZTI-PF-4-010), the Deutsche Forschungsgemeinschaft (DFG, GE 2835/2-1, GE 2835/4-1), the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)-Project-ID 431549029-SFB 1451, the programme "Profilbildung 2020" (grant no. PROFILNRW-2020-107-A), an initiative of the Ministry of Culture and Science of the State of Northrhine Westphalia.

Developmental differences in canonical cortical networks: Insights from microstructure-informed tractography

In response to a growing interest in refining brain connectivity assessments, this study focuses on integrating white matter fiber-specific microstructural properties into structural connectomes. Spanning ages 8-19 years in a developmental sample, it explores age-related patterns of microstructure-informed network properties at both local and global scales. First, the diffusion-weighted signal fraction associated with each tractography-reconstructed streamline was constructed. Subsequently, the convex optimization modeling for microstructure-informed tractography (COMMIT) approach was employed to generate microstructure-informed connectomes from diffusion MRI data. To complete the investigation, network characteristics within eight functionally defined networks (visual, somatomotor, dorsal attention, ventral attention, limbic, fronto-parietal, default mode, and subcortical networks) were evaluated. The findings underscore a consistent increase in global efficiency across child and adolescent development within the visual, somatomotor, and default mode networks (p < 0.005). Additionally, mean strength exhibits an upward trend in the somatomotor and visual networks (p < 0.001). Notably, nodes within the dorsal and ventral visual pathways manifest substantial age-dependent changes in local efficiency, aligning with existing evidence of extended maturation in these pathways. The outcomes strongly support the notion of a prolonged developmental trajectory for visual association cortices. This study contributes valuable insights into the nuanced dynamics of microstructure-informed brain connectivity throughout different developmental stages.

The magic, memory, and curiosity fMRI dataset of people viewing magic tricks

Videos of magic tricks offer lots of opportunities to study the human mind. They violate the expectations of the viewer, causing prediction errors, misdirect attention, and elicit epistemic emotions. Herein we describe and share the Magic, Memory, and Curiosity (MMC) Dataset where 50 participants watched 36 magic tricks filmed and edited specifically for functional magnetic imaging (fMRI) experiments. The MMC Dataset includes a contextual incentive manipulation, curiosity ratings for the magic tricks, and incidental memory performance tested a week later. We additionally measured individual differences in working memory and constructs relevant to motivated learning. fMRI data were acquired before, during, and after learning. We show that both behavioural and fMRI data are of high quality, as indicated by basic validation analysis, i.e., variance decomposition as well as intersubject correlation and seed-based functional connectivity, respectively. The richness and complexity of the MMC Dataset will allow researchers to explore dynamic cognitive and motivational processes from various angles during task and rest.

Large-scale meta-analyses and network analyses of neural substrates underlying human escalated aggression

Escalated aggression represents a frequent and severe form of violence, sometimes manifesting as antisocial behavior. Driven by the pressures of modern life, escalated aggression is of particular concern due to its rising prevalence and its destructive impact on both individual well-being and socioeconomic stability. However, a consistent neural circuitry underpinning it remains to be definitively identified. Here, we addressed this issue by comparing brain alterations between individuals with escalated aggression and those without such behavioral manifestations. We first conducted a meta-analysis to synthesize previous neuroimaging studies on functional and structural alterations of escalated aggression (325 experiments, 2997 foci, 16,529 subjects). Following-up network and functional decoding analyses were conducted to provide quantitative characterizations of the identified brain regions. Our results revealed that brain regions constantly involved in escalated aggression were localized in the subcortical network (amygdala and lateral orbitofrontal cortex) associated with emotion processing, the default mode network (dorsal medial prefrontal cortex and middle temporal gyrus) associated with mentalizing, and the salience network (anterior cingulate cortex and anterior insula) associated with cognitive control. These findings were further supported by additional meta-analyses on emotion processing, mentalizing, and cognitive control, all of which showed conjunction with the brain regions identified in the escalated aggression. Together, these findings advance the understanding of the risk biomarkers of escalated aggressive populations and refine theoretical models of human aggression.

Individual variability in neural representations of mind-wandering

Mind-wandering is a frequent, daily mental activity, experienced in unique ways in each person. Yet neuroimaging evidence relating mind-wandering to brain activity, for example in the default mode network (DMN), has relied on population- rather than individual-based inferences owing to limited within-person sampling. Here, three densely sampled individuals each reported hundreds of mind-wandering episodes while undergoing multi-session functional magnetic resonance imaging. We found reliable associations between mind-wandering and DMN activation when estimating brain networks within individuals using precision functional mapping. However, the timing of spontaneous DMN activity relative to subjective reports, and the networks beyond DMN that were activated and deactivated during mind-wandering, were distinct across individuals. Connectome-based predictive modeling further revealed idiosyncratic, whole-brain functional connectivity patterns that consistently predicted mind-wandering within individuals but did not fully generalize across individuals. Predictive models of mind-wandering and attention that were derived from larger-scale neuroimaging datasets largely failed when applied to densely sampled individuals, further highlighting the need for personalized models. Our work offers novel evidence for both conserved and variable neural representations of self-reported mind-wandering in different individuals. The previously unrecognized interindividual variations reported here underscore the broader scientific value and potential clinical utility of idiographic approaches to brain-experience associations.

Local and global effects of sedation in resting-state fMRI: a randomized, placebo-controlled comparison between etifoxine and alprazolam

TSPO ligands are promising alternatives to benzodiazepines in the treatment of anxiety, as they display less pronounced side effects such as sedation, cognitive impairment, tolerance development and abuse potential. In a randomized double-blind repeated-measures study we compare a benzodiazepine (alprazolam) to a TSPO ligand (etifoxine) by assessing side effects and acquiring resting-state fMRI data from 34 healthy participants after 5 days of taking alprazolam, etifoxine or a placebo. To study the effects of the pharmacological interventions in fMRI in detail and across different scales, we combine in our study complementary analysis strategies related to whole-brain functional network connectivity, local connectivity analysis expressed in regional homogeneity, fluctuations in low-frequency BOLD amplitudes and coherency of independent resting-state networks. Participants reported considerable adverse effects such as fatigue, sleepiness and concentration impairments, related to the administration of alprazolam compared to placebo. In resting-state fMRI we found a significant decrease in functional connection density, network efficiency and a decrease in the networks rich-club coefficient related to alprazolam. While observing a general decrease in regional homogeneity in high-level brain networks in the alprazolam condition, we simultaneously could detect an increase in regional homogeneity and resting-state network coherence in low-level sensory regions. Further we found a general increase in the low-frequency compartment of the BOLD signal. In the etifoxine condition, participants did not report any significant side effects compared to the placebo, and we did not observe any corresponding modulations in our fMRI metrics. Our results are consistent with the idea that sedation globally disconnects low-level functional networks, but simultaneously increases their within-connectivity. Further, our results point towards the potential of TSPO ligands in the treatment of anxiety and depression.

Longitudinal inference of multiscale markers in psychosis: from hippocampal centrality to functional outcome

Multiscale neuroscience conceptualizes mental illness as arising from aberrant interactions across and within multiple biopsychosocial scales. We leverage this framework to propose a multiscale disease progression model of psychosis, in which hippocampal-cortical dysconnectivity precedes impairments in episodic memory and social cognition, which lead to more severe negative symptoms and lower functional outcome. As psychosis represents a heterogeneous collection of biological and behavioral alterations that evolve over time, we further predict this disease progression for a subtype of the patient sample, with other patients showing normal-range performance on all variables. We sampled data from two cross-sectional datasets of first- and multi-episode psychosis, resulting in a sample of 163 patients and 119 non-clinical controls. To address our proposed disease progression model and evaluate potential heterogeneity, we applied a machine-learning algorithm, SuStaIn, to the patient data. SuStaIn uniquely integrates clustering and disease progression modeling and identified three patient subtypes. Subtype 0 showed normal-range performance on all variables. In comparison, Subtype 1 showed lower episodic memory, social cognition, functional outcome, and higher negative symptoms, while Subtype 2 showed lower hippocampal-cortical connectivity and episodic memory. Subtype 1 deteriorated from episodic memory to social cognition, negative symptoms, functional outcome to bilateral hippocampal-cortical dysconnectivity, while Subtype 2 deteriorated from bilateral hippocampal-cortical dysconnectivity to episodic memory and social cognition, functional outcome to negative symptoms. This first application of SuStaIn in a multiscale psychiatric model provides distinct disease trajectories of hippocampal-cortical connectivity, which might underlie the heterogeneous behavioral manifestations of psychosis.

Additive effects of depression and obesity on neural correlates of inhibitory control

BACKGROUND

Depression and obesity are associated with impaired inhibitory control. Behavioral evidence indicates an exacerbating additive effect when both conditions co-occur. However, the underlying neural mechanisms remain unclear. Moreover, systemic inflammation affects neurocognitive performance in both individuals with depression and obesity. Here, we investigate additive effects of depression and obesity on neural correlates of inhibitory control, and examine inflammation as a connecting pathway.

METHODS

We assessed inhibitory control processing in 64 individuals with obesity and varying degrees of depressed mood by probing neural activation and connectivity during an fMRI Stroop task. Additionally, we explored associations of altered neural responses with individual differences in systemic inflammation. Data were collected as part of the BARICO (Bariatric surgery Rijnstate and Radboudumc neuroimaging and Cognition in Obesity) study.

RESULTS

Concurrent depression and obesity were linked to increased functional connectivity between the supplementary motor area and precuneus and between the inferior occipital and inferior parietal gyrus. Exploratory analysis revealed that circulating inflammation markers, including plasma leptin, IL-6, IL-8, and CCL-3 correlated with the additive effect of depression and obesity on altered functional connectivity.

LIMITATIONS

The observational design limits causal inferences. Future research employing longitudinal or intervention designs is required to validate these findings and elucidate causal pathways.

CONCLUSION

These findings suggest increased neural crosstalk underlying impaired inhibitory control in individuals with concurrent obesity and depressed mood. Our results support a model of an additive detrimental effect of concurrent depression and obesity on neurocognitive functioning, with a possible role of inflammation.

Structure-function coupling in macroscale human brain networks

Precisely how the anatomical structure of the brain gives rise to a repertoire of complex functions remains incompletely understood. A promising manifestation of this mapping from structure to function is the dependency of the functional activity of a brain region on the underlying white matter architecture. Here, we review the literature examining the macroscale coupling between structural and functional connectivity, and we establish how this structure-function coupling (SFC) can provide more information about the underlying workings of the brain than either feature alone. We begin by defining SFC and describing the computational methods used to quantify it. We then review empirical studies that examine the heterogeneous expression of SFC across different brain regions, among individuals, in the context of the cognitive task being performed, and over time, as well as its role in fostering flexible cognition. Last, we investigate how the coupling between structure and function is affected in neurological and psychiatric conditions, and we report how aberrant SFC is associated with disease duration and disease-specific cognitive impairment. By elucidating how the dynamic relationship between the structure and function of the brain is altered in the presence of neurological and psychiatric conditions, we aim to not only further our understanding of their aetiology but also establish SFC as a new and sensitive marker of disease symptomatology and cognitive performance. Overall, this Review collates the current knowledge regarding the regional interdependency between the macroscale structure and function of the human brain in both neurotypical and neuroatypical individuals.

Neurodevelopmental subtypes of functional brain organization in the ABCD study using a rigorous analytic framework

The current study demonstrates that an individual's resting-state functional connectivity (RSFC) is a dependable biomarker for identifying differential patterns of cognitive and emotional functioning during late childhood. Using baseline RSFC data from the Adolescent Brain Cognitive Development (ABCD) study, which includes children aged 9-11, we identified four distinct RSFC subtypes. We introduce an integrated methodological pipeline for testing the reliability and importance of these subtypes. In the Identification phase, Leiden Community Detection defined RSFC subtypes, with their reproducibility confirmed through a split-sample technique in the Validation stage. The Evaluation phase showed that distinct cognitive and mental health profiles are associated with each subtype, with the Predictive phase indicating that subtypes better predict various cognitive and mental health characteristics than individual RSFC connections. The Replication stage employed bootstrapping and down-sampling methods to substantiate the reproducibility of these subtypes further. This work allows future explorations of developmental trajectories of these RSFC subtypes.

The "limbic network," comprising orbitofrontal and anterior temporal cortex, is part of an extended default network: Evidence from multi-echo fMRI

Resting-state functional magnetic resonance imaging (fMRI) investigations have provided a view of the default network (DN) as composed of a specific set of frontal, parietal, and temporal cortical regions. This spatial topography is typically defined with reference to an influential network parcellation scheme that designated the DN as one of seven large-scale networks (Yeo et al., 2011). However, the precise functional organization of the DN is still under debate, with studies arguing for varying subnetwork configurations and the inclusion of subcortical regions. In this vein, the so-called limbic network-defined as a distinct large-scale network comprising the bilateral temporal poles, ventral anterior temporal lobes, and orbitofrontal cortex-is of particular interest. A large multi-modal and multi-species literature on the anatomical, functional, and cognitive properties of these regions suggests a close relationship to the DN. Notably, these regions have poor signal quality with conventional fMRI acquisition, likely obscuring their network affiliation in most studies. Here, we leverage a multi-echo fMRI dataset with high temporal signal-to-noise and whole-brain coverage, including orbitofrontal and anterior temporal regions, to examine the large-scale network resting-state functional connectivity of these regions and assess their associations with the DN. Consistent with our hypotheses, our results support the inclusion of the majority of the orbitofrontal and anterior temporal cortex as part of the DN and reveal significant heterogeneity in their functional connectivity. We observed that left-lateralized regions within the temporal poles and ventral anterior temporal lobes, as well as medial orbitofrontal regions, exhibited the greatest resting-state functional connectivity with the DN, with heterogeneity across DN subnetworks. Overall, our findings suggest that, rather than being a functionally distinct network, the orbitofrontal and anterior temporal regions comprise part of a larger, extended default network.