Functional Organization Of Brain Research Articles

Effect of dopamine on limbic network connectivity at rest in Parkinson's disease patients with freezing of gait.

Freezing of gait (FOG) in Parkinson's disease (PD) has a poorly understood pathophysiology, which hinders treatment development. Recent work showed a dysfunctional fronto-striato-limbic circuitry at rest in PD freezers compared to non-freezers in the dopamine "OFF" state. While other studies found that dopaminergic replacement therapy alters functional brain organization in PD, the specific effect of dopamine medication on fronto-striato-limbic functional connectivity in freezers remains unclear. To evaluate how dopamine therapy alters resting state functional connectivity (rsFC) of the fronto-striato-limbic circuitry in PD freezers, and whether the degree of connectivity change is related to freezing severity and anxiety. Twenty-three PD FOG patients underwent MRI at rest (rsfMRI) in their clinically defined "OFF" and "ON" dopaminergic medication states. A seed-to-seed based analysis was performed between a priori defined limbic circuitry ROIs. Functional connectivity was compared between OFF and ON states. A secondary correlation analyses evaluated the relationship between Hospital Anxiety and Depression Scale (HADS)-Anxiety) and FOG Questionnaire with changes in rsFC from OFF to ON. PD freezers' OFF compared to ON showed increased functional coupling between the right hippocampus and right caudate nucleus, and between the left putamen and left posterior parietal cortex (PPC). A negative association was found between HADS-Anxiety and the rsFC change from OFF to ON between the left amygdala and left prefrontal cortex, and left putamen and left PPC. These findings suggest that dopaminergic medication partially modulates the frontoparietal-limbic-striatal circuitry in PD freezers, and that the influence of medication on the amygdala, may be related to clinical anxiety in freezer.

Dimensions of early life adversity and their associations with functional brain organisation

Abstract Early life adversity is associated with differences in brain function and an elevated risk for poor mental health. Using data from children aged 10 (N = 5,798) from the Adolescent Brain Cognitive Development (ABCD) cohort, we investigated how adversity relates to functional brain organisation using a network neuroscience approach. We derived four data-driven categories of adversity by fitting a mixed graphical model: household/community instability, physical/sexual abuse, parental neglect, and financial difficulties. Analyses revealed that multiple forms of adversity were associated with reduced clustering and increased assortativity across the entire brain and that these local measures of organisation captured greater adversity-related variance than mesoscale measures like modularity. The most pronounced effects were in the somatosensory and subcortical networks. Financial difficulties showed the strongest and most widespread associations with brain organisation, with evidence of a mediating effect of assortativity on the association between financial difficulties and internalising symptoms. Adding race as a covariate attenuated most brain-adversity relationships, suggesting that experiences of adversity are strongly related to race/ethnicity in the ABCD sample. These results demonstrate that different forms of adversity are associated with both shared and unique variations in functional brain organisation, highlighting its potential significance for explaining individual differences in mental health outcomes following early life adversity.

Accelerated epigenetic age is associated with whole-brain functional connectivity and impaired cognitive performance in older adults

While chronological age is a strong predictor for health-related risk factors, it is an incomplete metric that fails to fully characterize the unique aging process of individuals with different genetic makeup, neurodevelopment, and environmental experiences. Recent advances in epigenomic array technologies have made it possible to generate DNA methylation-based biomarkers of biological aging, which may be useful in predicting a myriad of cognitive abilities and functional brain network organization across older individuals. It is currently unclear which cognitive domains are negatively correlated with epigenetic age above and beyond chronological age, and it is unknown if functional brain organization is an important mechanism for explaining these associations. In this study, individuals with accelerated epigenetic age (i.e. AgeAccelGrim) performed worse on tasks that spanned a wide variety of cognitive faculties including both fluid and crystallized intelligence (N = 103, average age = 68.98 years, 73 females, 30 males). Additionally, fMRI connectome-based predictive models suggested a mediating mechanism of functional connectivity on epigenetic age acceleration-cognition associations primarily in medial temporal lobe and limbic structures. This research highlights the important role of epigenetic aging processes on the development and maintenance of healthy cognitive capacities and function of the aging brain.

Robust and replicable functional brain signatures of 22q11.2 deletion syndrome and associated psychosis: a deep neural network-based multi-cohort study.

A major genetic risk factor for psychosis is 22q11.2 deletion (22q11.2DS). However, robust and replicable functional brain signatures of 22q11.2DS and 22q11.2DS-associated psychosis remain elusive due to small sample sizes and a focus on small single-site cohorts. Here, we identify functional brain signatures of 22q11.2DS and 22q11.2DS-associated psychosis, and their links with idiopathic early psychosis, using one of the largest multi-cohort data to date. We obtained multi-cohort clinical phenotypic and task-free fMRI data from 856 participants (101 22q11.2DS, 120 idiopathic early psychosis, 101 idiopathic autism, 123 idiopathic ADHD, and 411 healthy controls) in a case-control design. A novel spatiotemporal deep neural network (stDNN)-based analysis was applied to the multi-cohort data to identify functional brain signatures of 22q11.2DS and 22q11.2DS-associated psychosis. Next, stDNN was used to test the hypothesis that the functional brain signatures of 22q11.2DS-associated psychosis overlap with idiopathic early psychosis but not with autism and ADHD. stDNN-derived brain signatures distinguished 22q11.2DS from controls, and 22q11.2DS-associated psychosis with very high accuracies (86-94%) in the primary cohort and two fully independent cohorts without additional training. Robust distinguishing features of 22q11.2DS-associated psychosis emerged in the anterior insula node of the salience network and the striatum node of the dopaminergic reward pathway. These features also distinguished individuals with idiopathic early psychosis from controls, but not idiopathic autism or ADHD. Our results reveal that individuals with 22q11.2DS exhibit a highly distinct functional brain organization compared to controls. Additionally, the brain signatures of 22q11.2DS-associated psychosis overlap with those ofidiopathic early psychosis in the salience network and dopaminergic reward pathway, providing substantial empirical support for the theoretical aberrant salience-based model of psychosis. Collectively, our findings, replicated across multiple independent cohorts, advance the understanding of 22q11.2DS and associated psychosis, underscoring the value of 22q11.2DS as a genetic model for probing the neurobiological underpinnings of psychosis and its progression.

Interpretable Cognitive Ability Prediction: A Comprehensive Gated Graph Transformer Framework for Analyzing Functional Brain Networks.

Graph convolutional deep learning has emerged as a promising method to explore the functional organization of the human brain in neuroscience research. This paper presents a novel framework that utilizes the gated graph transformer (GGT) model to predict individuals' cognitive ability based on functional connectivity (FC) derived from fMRI. Our framework incorporates prior spatial knowledge and uses a random-walk diffusion strategy that captures the intricate structural and functional relationships between different brain regions. Specifically, our approach employs learnable structural and positional encodings (LSPE) in conjunction with a gating mechanism to efficiently disentangle the learning of positional encoding (PE) and graph embeddings. Additionally, we utilize the attention mechanism to derive multi-view node feature embeddings and dynamically distribute propagation weights between each node and its neighbors, which facilitates the identification of significant biomarkers from functional brain networks and thus enhances the interpretability of the findings. To evaluate our proposed model in cognitive ability prediction, we conduct experiments on two large-scale brain imaging datasets: the Philadelphia Neurodevelopmental Cohort (PNC) and the Human Connectome Project (HCP). The results show that our approach not only outperforms existing methods in prediction accuracy but also provides superior explainability, which can be used to identify important FCs underlying cognitive behaviors.

Multivariate association between psychosocial environment, behaviors, and brain functional networks in adolescent depression

BackgroundAdolescent depression shows high clinical heterogeneity. Brain functional networks serve as a powerful tool for investigating neural mechanisms underlying depression profiles. A key challenge is to characterize how variation in brain functional organization links to behavioral features and psychosocial environmental influences. MethodsWe recruited 80 adolescents with major depressive disorder (MDD) and 42 healthy controls (HCs). First, we estimated the differences in functional connectivity of resting-state networks (RSN) between the two groups. Then, we used sparse canonical correlation analysis to characterize patterns of associations between RSN connectivity and symptoms, cognition, and psychosocial environmental factors in MDD adolescents. Clustering analysis was applied to stratify patients into homogenous subtypes according to these brain-behavior-environment associations. ResultsMDD adolescents showed significantly hyperconnectivity between the ventral attention and cingulo-opercular networks compared with HCs. We identified one reliable pattern of covariation between RSN connectivity and clinical/environmental features in MDD adolescents. In this pattern, psychosocial factors, especially the interpersonal and family relationships, were major contributors to variation in connectivity of salience, cingulo-opercular, ventral attention, subcortical and somatosensory-motor networks. Based on this association, we categorized patients into two subgroups which showed different environment and symptoms characteristics, and distinct connectivity alterations. These differences were covered up when the patients were taken as a whole group. ConclusionThis study identified the environmental exposures associated with specific functional networks in MDD youths. Our findings emphasize the importance of the psychosocial context in assessing brain function alterations in adolescent depression and have the potential to promote targeted treatment and precise prevention.

Higher handgrip strength is linked to higher salience ventral attention functional network segregation in older adults

Converging evidence suggests that handgrip strength is linked to cognition in older adults, and this may be subserved by shared age-related changes in brain function and structure. However, the interplay among handgrip strength, brain functional connectivity, and cognitive function remains poorly elucidated. Hence, our study sought to examine these relationships in 148 community-dwelling older adults. Specifically, we examined functional segregation, a measure of functional brain organization sensitive to ageing and cognitive decline, and its associations with handgrip strength and cognitive function. We showed that higher handgrip strength was related to better processing speed, attention, and global cognition. Further, higher handgrip strength was associated with higher segregation of the salience/ventral attention network, driven particularly by higher salience/ventral attention intra-network functional connectivity of the right anterior insula to the left posterior insula/frontal operculum and right midcingulate/medial parietal cortex. Importantly, these handgrip strength-related inter-individual differences in salience/ventral attention network functional connectivity were linked to cognitive function, as revealed by functional decoding and brain-cognition association analyses. Our findings thus highlight the importance of the salience/ventral attention network in handgrip strength and cognition, and suggest that inter-individual differences in salience/ventral attention network segregation and intra-network connectivity could underpin the handgrip strength-cognition relationship in older adults.

Deep learning models reveal replicable, generalizable, and behaviorally relevant sex differences in human functional brain organization

Sex plays a crucial role in human brain development, aging, and the manifestation of psychiatric and neurological disorders. However, our understanding of sex differences in human functional brain organization and their behavioral consequences has been hindered by inconsistent findings and a lack of replication. Here, we address these challenges using a spatiotemporal deep neural network (stDNN) model to uncover latent functional brain dynamics that distinguish male and female brains. Our stDNN model accurately differentiated male and female brains, demonstrating consistently high cross-validation accuracy (>90%), replicability, and generalizability across multisession data from the same individuals and three independent cohorts (N ~ 1,500 young adults aged 20 to 35). Explainable AI (XAI) analysis revealed that brain features associated with the default mode network, striatum, and limbic network consistently exhibited significant sex differences (effect sizes > 1.5) across sessions and independent cohorts. Furthermore, XAI-derived brain features accurately predicted sex-specific cognitive profiles, a finding that was also independently replicated. Our results demonstrate that sex differences in functional brain dynamics are not only highly replicable and generalizable but also behaviorally relevant, challenging the notion of a continuum in male-female brain organization. Our findings underscore the crucial role of sex as a biological determinant in human brain organization, have significant implications for developing personalized sex-specific biomarkers in psychiatric and neurological disorders, and provide innovative AI-based computational tools for future research.

Modifying modularity: aerobic exercise improves functional connectivity in breast cancer survivors

IntroductionAerobic exercise has been shown to improve cancer-associated cognitive decline (CACD) in breast cancer survivors (BCS), and recent findings suggest that one mechanism by which exercise may reduce cognitive decline is through alteration of the brain's functional organization. Many cognitive abilities and measures of functional brain organization change with age and disease, typically reflected in cognitive decline and reduced differentiation of brain networks, or “modularity.” Although previous research has identified associations between lifestyle interventions, such as exercise, and increased modularity, no studies have examined these relationships in cancer populations. The primary aim of this study was to investigate the preliminary effects of a 12-week aerobic exercise program on changes in brain network modularity in BCS. As a secondary aim, we explored correlations between changes in modularity with moderate-to-vigorous physical activity (MVPA) and cognitive function. Data were exploratory and used for hypothesis generation for a future, larger study.MethodsParticipants included a subsample of 10 BCS (M age = 65.9 ± 9.3 years) from a larger pilot study (N = 30 BCS) who were randomized to a 12-week aerobic exercise program (AE) or usual care (UC). The present study collected brain magnetic resonance imaging, Actigraph accelerometry, and cognitive task performance at baseline and 3-month follow-up (i.e., post-intervention; n = 4 AE, n = 6 UC). Intervention effects on modularity, MVPA, and cognition were quantified as magnitude of change between groups (Cohen's d). Changes in modularity were further explored via paired t-tests within groups. Associations between changes in modularity, MVPA, and cognitive performance were explored using Spearman's correlations.ResultsThe magnitude of changes in modularity between groups were small-to-moderate and favored the AE group (d = 0.23 to d = 0.67 across thresholds). Paired t-tests revealed a significant increase in modularity in the AE group from baseline to 3-month follow-up (t = 3.08, p = 0.03, d = 1.17), but not in the UC group. The correlation between changes in MVPA and changes in modularity were not statistically significant (r = 0.36, p = 0.39), and correlations between modularity and cognitive performance yielded mixed effects by cognitive domain.DiscussionFindings suggest that aerobic exercise may influence functional brain network organization and cognition in BCS. These data warrant further investigation in larger exercise trials.

Methods for decoding cortical gradients of functional connectivity

Abstract Macroscale gradients have emerged as a central principle for understanding functional brain organization. Previous studies have demonstrated that a principal gradient of connectivity in the human brain exists, with unimodal primary sensorimotor regions situated at one end and transmodal regions associated with the default mode network and representative of abstract functioning at the other. The functional significance and interpretation of macroscale gradients remains a central topic of discussion in the neuroimaging community, with some studies demonstrating that gradients may be described using meta-analytic functional decoding techniques. However, additional methodological development is necessary to fully leverage available meta-analytic methods and resources and quantitatively evaluate their relative performance. Here, we conducted a comprehensive series of analyses to investigate and improve the framework of data-driven, meta-analytic methods, thereby establishing a principled approach for gradient segmentation and functional decoding. We found that a two-segment solution determined by a k-means segmentation approach and an LDA-based meta-analysis combined with the NeuroQuery database was the optimal combination of methods for decoding functional connectivity gradients. Finally, we proposed a method for decoding additional components of the gradient decomposition. The current work aims to provide recommendations on best practices and flexible methods for gradient-based functional decoding of fMRI data.

Altered Static and Dynamic Brain Functional Topological Organization in Patients with Dysthyroid Optic Neuropathy.

Dysthyroid optic neuropathy (DON) is a serious vision-threatening complication of thyroid-associated ophthalmopathy (TAO). Exploration of the underlying mechanisms of DON is critical for its timely clinical diagnosis. We hypothesized that TAO patients with DON may have altered brain functional networks. We aimed to explore the alterations of static and dynamic functional connectomes in patients with and without DON using resting-state functional MRI with graph theory method. A cross-sectional study. Grade A tertiary hospital. Sixty-six TAO patients (28 DON and 38 non-DON) and 30 healthy controls (HCs). Topological properties of functional networks. For static properties, DON patients exhibited lower global efficiency (Eg), local efficiency, normalized clustering coefficient, small-worldness (σ), and higher characteristic path length (Lp) than HCs. Both DON and non-DON patients exhibited varying degrees of abnormalities in nodal properties. Meanwhile, compared with non-DON, DON patients exhibited abnormalities in nodal properties in orbitofrontal cortex and visual network (VN). For dynamic properties, DON group exhibited higher variance in Eg and Lp than non-DON and HC groups. A strengthened subnetwork with VN as the core was identified in DON cohort. Significant correlations were found between network properties and clinical variables. For distinguishing DON, the combination of static and dynamic network properties exhibited optimal diagnostic performance. Functional network alterations were observed in both DON and non-DON patients, providing novel insights into the underlying neural mechanisms of disease. Functional network properties may be potential biomarkers for reflecting the progression of TAO from non-DON to DON.

Structural connectome alterations between individuals with autism and neurotypical controls using feature representation learning

Autism spectrum disorder is one of the most common neurodevelopmental conditions associated with sensory and social communication impairments. Previous neuroimaging studies reported that atypical nodal- or network-level functional brain organization in individuals with autism was associated with autistic behaviors. Although dimensionality reduction techniques have the potential to uncover new biomarkers, the analysis of whole-brain structural connectome abnormalities in a low-dimensional latent space is underinvestigated. In this study, we utilized autoencoder-based feature representation learning for diffusion magnetic resonance imaging-based structural connectivity in 80 individuals with autism and 61 neurotypical controls that passed strict quality controls. We generated low-dimensional latent features using the autoencoder model for each group and adopted an integrated gradient approach to assess the contribution of the input data for predicting latent features during the encoding process. Subsequently, we compared the integrated gradient values between individuals with autism and neurotypical controls and observed differences within the transmodal regions and between the sensory and limbic systems. Finally, we identified significant associations between integrated gradient values and communication abilities in individuals with autism. Our findings provide insights into the whole-brain structural connectome in autism and may help identify potential biomarkers for autistic connectopathy.

Reorganization of Brain Resting-state Functional Connectivity Following 14 Days of Elbow Immobilization in Young Females

Limb immobilization is known to cause significant decreases in muscle strength and muscle mass as early as two days following the onset of immobilization. However, the decline in strength surpasses the decline in muscle mass, suggesting that factors in addition to muscle loss, such as neuroplasticity, contribute to the decrease in force production. However, little is known regarding immobilization-induced neural changes, although sensorimotor regions seem to be the most affected. The present study aimed to determine whether brain functional organization is altered following 14 days of unilateral elbow immobilization. Functional organization was quantified using resting-state functional connectivity, a measure of the synchronicity of the spontaneous discharge of different brain regions at rest. Data was obtained from twelve healthy young females before and after completing the immobilization period. A seed-to-voxel analysis was performed using seeds associated with cortical, subcortical, and cerebellar sensorimotor regions of the brain. The results showed changes predominantly involving cerebellar connectivity. For example, the immobilization period caused a decrease in connectivity between the motor cerebellar region of the immobilized arm and the left temporal lobe, and an increase between the same cerebellar region and the supplementary motor area. Overall, changes in connectivity occurred in regions typically associated with error detection and motor learning, suggesting a potential functional reorganization of the brain within 14 days of elbow immobilization.

Improving preclinical to clinical translation of cognitive function for aging-related disorders: the utility of comprehensive touchscreen testing batteries in common marmosets

Concerns about poor animal to human translation have come increasingly to the fore, in particular with regards to cognitive improvements in rodent models, which have failed to translate to meaningful clinical benefit in humans. This problem has been widely acknowledged, most recently in the field of Alzheimer’s disease, although this issue pervades the spectrum of central nervous system (CNS) disorders, including neurodevelopmental, neuropsychiatric, and neurodegenerative diseases. Consequently, recent efforts have focused on improving preclinical to clinical translation by incorporating more clinically analogous outcome measures of cognition, such as touchscreen-based assays, which can be employed across species, and have great potential to minimize the translational gap. For aging-related research, it also is important to incorporate model systems that facilitate the study of the long prodromal phase in which cognitive decline begins to emerge and which is a major limitation of short-lived species, such as laboratory rodents. We posit that to improve translation of cognitive function and dysfunction, nonhuman primate models, which have conserved anatomical and functional organization of the primate brain, are necessary to move the field of translational research forward and to bridge the translational gaps. The present studies describe the establishment of a comprehensive battery of touchscreen-based tasks that capture a spectrum of domains sensitive to detecting aging-related cognitive decline, which will provide the greatest benefit through longitudinal evaluation throughout the prolonged lifespan of the marmoset.

Individual differences in the neural architecture in semantic processing.

Neural mechanisms underlying semantic processing have been extensively studied by using functional magnetic resonance imaging, nevertheless, the individual differences of it are yet to be unveiled. To further our understanding of functional and anatomical brain organization underlying semantic processing to the level of individual humans, we used out-of-scanner language behavioral data, T1, resting-state, and story comprehension task-evoked functional image data in the Human Connectome Project, to investigate individual variability in the task-evoked semantic processing network, and attempted to predict individuals' language skills based on task and intrinsic functional connectivity of highly variable regions, by employing a machine-learning framework. Our findings first confirmed that individual variability in both functional and anatomical markers were heterogeneously distributed throughout the semantic processing network, and that the variability increased towards higher levels in the processing hierarchy. Furthermore, intrinsic functional connectivities among these highly variable regions were found to contribute to predict individual reading decoding abilities. The contributing nodes in the overall network were distributed in the left superior, inferior frontal, and temporo-parietal cortices. Our results suggested that the individual differences of neurobiological markers were heterogeneously distributed in the semantic processing network, and that neurobiological markers of highly variable areas are not only linked to individual variability in language skills, but can predict language skills at the individual level.

Abnormal Static and Dynamic Local Functional Connectivity in First-Episode Schizophrenia: A Resting-State fMRI Study.

Dynamic functional connectivity (FC) analyses have provided ample information on the disturbances of global functional brain organization in patients with schizophrenia. However, our understanding about the dynamics of local FC in never-treated first episode schizophrenia (FES) patients is still rudimentary. Dynamic Regional Phase Synchrony (DRePS), a newly developed dynamic local FC analysis method that could quantify the instantaneous phase synchronization in local spatial scale, overcomes the limitations of commonly used sliding-window methods. The current study performed a comprehensive examination on both the static and dynamic local FC alterations in FES patients (N = 74) from healthy controls (HCs, N = 41) with resting-state functional magnetic resonance imaging using DRePS, and compared the static local FC metrics derived from DRePS with those calculated from two commonly used regional homogeneity (ReHo) analysis methods that are defined based on Kendall's coefficient of concordance (KCC-ReHo) and frequency coherence (Cohe-ReHo). Symptom severities of FES patients were assessed with a set of clinical scales. Cognitive functions of FES patients and HCs were assessed with the MATRICS consensus cognitive battery. Group-level analysis revealed that compared with HCs, FES patients exhibited increased static local FC in right superior, middle temporal gyri, hippocampus, parahippocampal gyrus, putamen, and bilateral caudate nucleus. Nonetheless, the dynamic local FC metrics did not show any significant differences between the two groups. The associations between all local FC metrics and clinical characteristics manifested scores were explored using a relevance vector machine. Results showed that the Global Assessment of Functioning score highest in past year and the Brief Visuospatial Memory Test-Revised task score were statistically significantly predicted by a combination of all static and dynamic features. The diagnostic abilities of different local FC metrics and their combinations were compared by the classification performance of linear support vector machine classifiers. Results showed that the inclusion of zero crossing ratio of DRePS, one of the dynamic local FC metrics, alongside static local FC metrics improved the classification accuracy compared to using static metrics alone. These results enrich our understanding of the neurocognitive mechanisms underlying schizophrenia, and demonstrate the potential of developing diagnostic biomarker for schizophrenia based on DRePS.

The effect of using group-averaged or individualized brain parcellations when investigating connectome dysfunction in psychosis.

Functional magnetic resonance imaging (fMRI) is widely used to investigate functional coupling (FC) disturbances in a range of clinical disorders. Most analyses performed to date have used group-based parcellations for defining regions of interest (ROIs), in which a single parcellation is applied to each brain. This approach neglects individual differences in brain functional organization and may inaccurately delineate the true borders of functional regions. These inaccuracies could inflate or underestimate group differences in case-control analyses. We investigated how individual differences in brain organization influence group comparisons of FC using psychosis as a case study, drawing on fMRI data in 121 early psychosis patients and 57 controls. We defined FC networks using either a group-based parcellation or an individually tailored variant of the same parcellation. Individualized parcellations yielded more functionally homogeneous ROIs than did group-based parcellations. At the level of individual connections, case-control FC differences were widespread, but the group-based parcellation identified approximately 7.7% more connections as dysfunctional than the individualized parcellation. When considering differences at the level of functional networks, the results from both parcellations converged. Our results suggest that a substantial fraction of dysconnectivity previously observed in psychosis may be driven by the parcellation method, rather than by a pathophysiological process related to psychosis.

Evaluating functional brain organization in individuals and identifying contributions to network overlap

Abstract Individual differences in the spatial organization of resting-state networks have received increased attention in recent years. Measures of individual-specific spatial organization of brain networks and overlapping network organization have been linked to important behavioral and clinical traits and are therefore potential biomarker targets for personalized psychiatry approaches. To better understand individual-specific spatial brain organization, this paper addressed three key goals. First, we determined whether it is possible to reliably estimate weighted (non-binarized) resting-state network maps using data from only a single individual, while also maintaining maximum spatial correspondence across individuals. Second, we determined the degree of spatial overlap between distinct networks, using test-retest and twin data. Third, we systematically tested multiple hypotheses (spatial mixing, temporal switching, and coupling) as candidate explanations for why networks overlap spatially. To estimate weighted network organization, we adopt the Probabilistic Functional Modes (PROFUMO) algorithm, which implements a Bayesian framework with hemodynamic and connectivity priors to supplement optimization for spatial sparsity/independence. Our findings showed that replicable individual-specific estimates of weighted resting-state networks can be derived using high-quality fMRI data within individual subjects. Network organization estimates using only data from each individual subject closely resembled group-informed network estimates (which was not explicitly modeled in our individual-specific analyses), suggesting that cross-subject correspondence was largely maintained. Furthermore, our results confirmed the presence of spatial overlap in network organization, which was replicable across sessions within individuals and in monozygotic twin pairs. Intriguingly, our findings provide evidence that overlap between 2-network pairs is indicative of coupling. These results suggest that regions of network overlap concurrently process information from both contributing networks, potentially pointing to the role of overlapping network organization in the integration of information across multiple brain systems.

Cross‐scale functional connectivity patterns of the aging brain learned from the multi‐cohort iSTAGing study

AbstractBackgroundBrain functional connectivity (FC) measures derived from resting‐state fMRI (rsfMRI) data have advanced our understanding of the brain organization. However, most existing studies investigate the brain functional networks (FNs) at a specific scale, ignoring potentially complementary FC information at different levels of spatial granularity as well as the cross‐scale information. Given that the brain is a multi‐scale, hierarchical system, FCs between FNs at different spatial scales may provide complementary information to those measured within‐scales for characterizing the aging brain.MethodWe computed multiscale FNs to capture their FC measures across multiple scales and investigated the associations between cross‐scale FCs and the brain aging based on rsfMRI data of a diverse cohort (n = 4186, aged 22 to 97 years) from seven sites of the iSTAGING study [1]. For each subject, we computed the FNs at seven scales, yielding seven sets of k (k = 17, 25, 50, 75, 100, 125 and 150) FNs, same as our previous study [2‐3]. We estimated FC measures between the 542 FNs using Pearson’s correlation, yielding the cross‐scale FC matrices that were harmonized across sites in the tangent space using ComBat‐GAM [4] with sex and age as covariates. The harmonized FC measures were used as features to build an age prediction model using Ridge regression as illustrated in Figure 1. The brain age prediction model was compared with that built on harmonized FC measures between FNs of the same scales, with the performance estimated with a nested five‐fold cross‐validation.ResultThe cross‐scale FC measures performed significantly better than the within‐scale FC measures for the brain age prediction, as summarized in Table 1. Figure 2 shows the most informative FC measures for the brain age prediction. Specifically, FC measures computed both cross‐ and within‐scales between the visual, somatomotor, and frontoparietal networks provided relatively more useful information for the brain age prediction than FC measures between other FNs.ConclusionOur study demonstrated that the cross‐scale FC measures provided complementary information to the within‐scale FC measures for predicting the brain age, facilitating enhanced understanding of aging related changes of the brain functional organization.

The individualized neural tuning model: Precise and generalizable cartography of functional architecture in individual brains

Abstract Quantifying how brain functional architecture differs from person to person is a key challenge in human neuroscience. Current individualized models of brain functional organization are based on brain regions and networks, limiting their use in studying fine-grained vertex-level differences. In this work, we present the individualized neural tuning (INT) model, a fine-grained individualized model of brain functional organization. The INT model is designed to have vertex-level granularity, to capture both representational and topographic differences, and to model stimulus-general neural tuning. Through a series of analyses, we demonstrate that (a) our INT model provides a reliable individualized measure of fine-grained brain functional organization, (b) it accurately predicts individualized brain response patterns to new stimuli, and (c) for many benchmarks, it requires only 10–20 minutes of data for good performance. The high reliability, specificity, precision, and generalizability of our INT model affords new opportunities for building brain-based biomarkers based on naturalistic neuroimaging paradigms.