Spatial Topography of Individual-Specific Cortical Networks Predicts Human Cognition, Personality, and Emotion

Dissociation of Reliability, Heritability, and Predictivity in Coarse- and Fine-Scale Functional Connectomes during Development

The functional connectome supports information transmission through the brain at various spatial scales, from exchange between broad cortical regions to finer-scale, vertex-wise connections that underlie specific information processing mechanisms. In adults, while both the coarse- and fine-scale functional connectomes predict cognition, the fine scale can predict up to twice the variance as the coarse-scale functional connectome. Yet, past brain-wide association studies, particularly using large developmental samples, focus on the coarse connectome to understand the neural underpinnings of individual differences in cognition. Using a large cohort of children (age 9-10 years; n = 1,115 individuals; both sexes; 50% female, including 170 monozygotic and 219 dizygotic twin pairs and 337 unrelated individuals), we examine the reliability, heritability, and behavioral relevance of resting-state functional connectivity computed at different spatial scales. We use connectivity hyperalignment to improve access to reliable fine-scale (vertex-wise) connectivity information and compare the fine-scale connectome with the traditional parcel-wise (coarse scale) functional connectomes. Though individual differences in the fine-scale connectome are more reliable than those in the coarse-scale, they are less heritable. Further, the alignment and scale of connectomes influence their ability to predict behavior, whereby some cognitive traits are equally well predicted by both connectome scales, but other, less heritable cognitive traits are better predicted by the fine-scale connectome. Together, our findings suggest there are dissociable individual differences in information processing represented at different scales of the functional connectome which, in turn, have distinct implications for heritability and cognition.

Individualized functional connectivity markers associated with motor and mood symptoms of Parkinson's disease

Parkinson's disease (PD) is a complex neurological disorder characterized by many motor and non-motor symptoms. While most studies focus on the motor symptoms of the disease, it is important to identify markers that underlie different facets of the disease. In this case-control study, we sought to discover reliable, individualized functional connectivity markers associated with both motor and mood symptoms of PD. Using functional MRI, we extensively sampled 166 patients with PD (64 women, 102 men; mean age=61.8 years, SD=7.81) and 51 healthy control participants (32 women, 19 men; mean age=55.68 years, SD=7.62). We found that a model consisting of 44 functional connections predicted both motor (UPDRS-III: Pearson r=0.21, FDR-adjusted p=0.006) and mood symptoms (HAMD: Pearson r=0.23, FDR-adjusted p=0.006; HAMA: Pearson r=0.21, FDR-adjusted p=0.006). Two sets of connections contributed differentially to these predictions. Between-network connections, mainly connecting the sensorimotor and visual large-scale functional networks, substantially contributed to the prediction of motor measures, while within-network connections in the insula and sensorimotor network contributed more so to mood prediction. The middle to posterior insula region played a particularly important role in predicting depression and anxiety scores. We successfully replicated and generalized our findings in two independent PD datasets. Taken together, our findings indicate that sensorimotor and visual network markers are indicative of PD brain pathology, and that distinct subsets of markers are associated with motor and mood symptoms of PD.

Functional magnetic resonance imaging in schizophrenia: current evidence, methodological advances, limitations and future directions

Functional neuroimaging emerged with great promise and has provided fundamental insights into the neurobiology of schizophrenia. However, it has faced challenges and criticisms, most notably a lack of clinical translation. This paper provides a comprehensive review and critical summary of the literature on functional neuroimaging, in particular functional magnetic resonance imaging (fMRI), in schizophrenia. We begin by reviewing research on fMRI biomarkers in schizophrenia and the clinical high risk phase through a historical lens, moving from case-control regional brain activation to global connectivity and advanced analytical approaches, and more recent machine learning algorithms to identify predictive neuroimaging features. Findings from fMRI studies of negative symptoms as well as of neurocognitive and social cognitive deficits are then reviewed. Functional neural markers of these symptoms and deficits may represent promising treatment targets in schizophrenia. Next, we summarize fMRI research related to antipsychotic medication, psychotherapy and psychosocial interventions, and neurostimulation, including treatment response and resistance, therapeutic mechanisms, and treatment targeting. We also review the utility of fMRI and data-driven approaches to dissect the heterogeneity of schizophrenia, moving beyond case-control comparisons, as well as methodological considerations and advances, including consortia and precision fMRI. Lastly, limitations and future directions of research in the field are discussed. Our comprehensive review suggests that, in order for fMRI to be clinically useful in the care of patients with schizophrenia, research should address potentially actionable clinical decisions that are routine in schizophrenia treatment, such as which antipsychotic should be prescribed or whether a given patient is likely to have persistent functional impairment. The potential clinical utility of fMRI is influenced by and must be weighed against cost and accessibility factors. Future evaluations of the utility of fMRI in prognostic and treatment response studies may consider including a health economics analysis.

Altered stability of dynamic brain functional architecture in primary open-angle glaucoma: a surface-based resting-state fMRI study

Delineating the neuropathological characteristics of primary open-angle glaucoma (POAG) is critical for understanding its pathophysiology. While temporal stability represents a crucial aspect of the brain's functional architecture, the specific patterns underlying its contribution to POAG remain unclear. This study aims to analyze the brain functional abnormalities in POAG using functional stability, a dynamic functional connectivity (DFC) approach based on resting-state functional magnetic resonance imaging (rs-fMRI). Seventy patients with POAG and forty-five healthy controls underwent rs-fMRI and ophthalmological examinations. The stability of DFC was calculated as the concordance of DFC over time using a sliding-window approach, and the differences in stability between the two groups were compared. Subsequently, Spearman's correlation analyses were conducted to examine the relationship between functional stability and clinical indicators. Compared with healthy controls, patients with POAG exhibited significantly decreased functional stability in the visual network, including the early visual center, ventral and dorsal stream visual cortex in both hemispheres. Conversely, stability values increased in the bilateral inferior parietal gyrus and right inferior frontal cortex. In POAG patients, the dynamic stability of the left early visual cortex and ventral stream visual cortex correlated with the mean deviation of visual field defects (r = 0.251, p = 0.037). The evidence from this study suggests that functional stability may provide a new understanding of brain alterations in the progression of POAG.

The Commonality and Individuality of Human Brains When Performing Tasks

It is imperative to study individual brain functioning toward understanding the neural bases responsible for individual behavioral and clinical traits. The complex and dynamic brain activity varies from area to area and from time to time across the entire brain, and BOLD-fMRI measures this spatiotemporal activity at large-scale systems level. We present a novel method to investigate task-evoked whole brain activity that varies not only from person to person but also from task trial to trial within each task type, offering a means of characterizing the individuality of human brains when performing tasks. For each task trial, the temporal correlation of task-evoked ideal time signal with the time signal of every point in the brain yields a full spatial map that characterizes the whole brain's functional co-activity (FC) relative to the task-evoked ideal response. For any two task trials, regardless of whether they are the same task or not, the spatial correlation of their corresponding two FC maps over the entire brain quantifies the similarity between these two maps, offering a means of investigating the variation in the whole brain activity trial to trial. The results demonstrated a substantially varied whole brain activity from trial to trial for each task category. The degree of this variation was task type-dependent and varied from subject to subject, showing a remarkable individuality of human brains when performing tasks. It demonstrates the potential of using the presented method to investigate the relationship of the whole brain activity with individual behavioral and clinical traits.

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 due to limited within-individual 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 inter-individual variations reported here underscore the broader scientific value and potential clinical utility of idiographic approaches to brain-experience associations.

Social cognitive regions of human association cortex are selectively connected to the amygdala

Reasoning about someone's thoughts and intentions - i.e., forming a theory of mind - is an important aspect of social cognition that relies on association areas of the brain that have expanded disproportionately in the human lineage. We recently showed that these association zones comprise parallel distributed networks that, despite occupying adjacent and interdigitated regions, serve dissociable functions. One network is selectively recruited by theory of mind processes. What circuit properties differentiate these parallel networks? Here, we show that social cognitive association areas are intrinsically and selectively connected to regions of the anterior medial temporal lobe that are implicated in emotional learning and social behaviors, including the amygdala at or near the basolateral complex and medial nucleus. The results suggest that social cognitive functions emerge through coordinated activity between amygdala circuits and a distributed association network, and indicate the medial nucleus may play an important role in social cognition in humans.

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.

A precision neuroscience approach to estimating reliability of neural responses during emotion processing: Implications for task-fMRI

Recent work demonstrating low test-retest reliability of neural activation during fMRI tasks raises questions about the utility of task-based fMRI for the study of individual variation in brain function. Two possible sources of the instability in task-based BOLD signal over time are noise or measurement error in the instrument, and meaningful variation across time within-individuals in the construct itself-brain activation elicited during fMRI tasks. Examining the contribution of these two sources of test-retest unreliability in task-evoked brain activity has far-reaching implications for cognitive neuroscience. If test-retest reliability largely reflects measurement error, it suggests that task-based fMRI has little utility in the study of either inter- or intra-individual differences. On the other hand, if task-evoked BOLD signal varies meaningfully over time, it would suggest that this tool may yet be well suited to studying intraindividual variation. We parse these sources of variance in BOLD signal in response to emotional cues over time and within-individuals in a longitudinal sample with 10 monthly fMRI scans. Test-retest reliability was low, reflecting a lack of stability in between-person differences across scans. In contrast, within-person, within-session internal consistency of the BOLD signal was higher, and within-person fluctuations across sessions explained almost half the variance in voxel-level neural responses. Additionally, monthly fluctuations in neural response to emotional cues were associated with intraindividual variation in mood, sleep, and exposure to stressors. Rather than reflecting trait-like differences across people, neural responses to emotional cues may be more reflective of intraindividual variation over time. These patterns suggest that task-based fMRI may be able to contribute to the study of individual variation in brain function if more attention is given to within-individual variation approaches, psychometrics-beginning with improving reliability beyond the modest estimates observed here, and the validity of task fMRI beyond the suggestive associations reported here.

Should one go for individual- or group-level brain parcellations? A deep-phenotyping benchmark

The analysis and understanding of brain characteristics often require considering region-level information rather than voxel-sampled data. Subject-specific parcellations have been put forward in recent years, as they can adapt to individual brain organization and thus offer more accurate individual summaries than standard atlases. However, the price to pay for adaptability is the lack of group-level consistency of the data representation. Here, we investigate whether the good representations brought by individualized models are merely an effect of circular analysis, in which individual brain features are better represented by subject-specific summaries, or whether this carries over to new individuals, i.e., whether one can actually adapt an existing parcellation to new individuals and still obtain good summaries in these individuals. For this, we adapt a dictionary-learning method to produce brain parcellations. We use it on a deep-phenotyping dataset to assess quantitatively the patterns of activity obtained under naturalistic and controlled-task-based settings. We show that the benefits of individual parcellations are substantial, but that they vary a lot across brain systems.

Variations in Cortical Functional Gradients Relate to Dimensions of Psychopathology in Preschool Children

OBJECTIVE

It is unclear how the functional brain hierarchy is organized in preschool-aged children, and whether alterations in the brain organization are linked to mental health in this age group. Here, we assessed whether preschool-aged children exhibit a brain organizational structure similar to that of older children, how this structure might change over time, and whether it might reflect mental health.

METHOD

This study derived functional gradients using diffusion embedding from resting state functional magnetic resonance imaging data of 4.5-year-old children (N = 100, 42 male participants) and 6.0-year-old children (N = 133, 62 male participants) from the longitudinal Growing Up in Singapore Towards healthy Outcomes (GUSTO) cohort. We then conducted partial least-squares correlation analyses to identify the association between the impairment ratings of different mental disorders and network gradient values.

RESULTS

The main organizing axis of functional connectivity (ie, principal gradient) separated the visual and somatomotor regions (ie, unimodal) in preschool-aged children, whereas the second axis delineated the unimodal-transmodal gradient. This pattern of organization was stable from 4.5 to 6 years of age. The second gradient separating the high- and low-order networks exhibited a diverging pattern across mental health severity, differentiating dimensions related to attention-deficit/hyperactivity disorder and phobic disorders.

CONCLUSION

This study characterized, for the first time, the functional brain hierarchy in preschool-aged children. A divergence in functional gradient pattern across different disease dimensions was found, highlighting how perturbations in functional brain organization can relate to the severity of different mental health disorders.

Reshaping the Cortical Connectivity Gradient by Long-Term Cognitive Training During Development

The organization of the brain follows a topological hierarchy that changes dynamically during development. However, it remains unknown whether and how cognitive training administered over multiple years during development can modify this hierarchical topology. By measuring the brain and behavior of school children who had carried out abacus-based mental calculation (AMC) training for five years (starting from 7 years to 12 years old) in pre-training and post-training, we revealed the reshaping effect of long-term AMC intervention during development on the brain hierarchical topology. We observed the development-induced emergence of the default network, AMC training-promoted shifting, and regional changes in cortical gradients. Moreover, the training-induced gradient changes were located in visual and somatomotor areas in association with the visuospatial/motor-imagery strategy. We found that gradient-based features can predict the math ability within groups. Our findings provide novel insights into the dynamic nature of network recruitment impacted by long-term cognitive training during development.

Can hubs of the human connectome be identified consistently with diffusion MRI?

Recent years have seen a surge in the use of diffusion MRI to map connectomes in humans, paralleled by a similar increase in processing and analysis choices. Yet these different steps and their effects are rarely compared systematically. Here, in a healthy young adult population (n = 294), we characterized the impact of a range of analysis pipelines on one widely studied property of the human connectome: its degree distribution. We evaluated the effects of 40 pipelines (comparing common choices of parcellation, streamline seeding, tractography algorithm, and streamline propagation constraint) and 44 group-representative connectome reconstruction schemes on highly connected hub regions. We found that hub location is highly variable between pipelines. The choice of parcellation has a major influence on hub architecture, and hub connectivity is highly correlated with regional surface area in most of the assessed pipelines (ρ > 0.70 in 69% of the pipelines), particularly when using weighted networks. Overall, our results demonstrate the need for prudent decision-making when processing diffusion MRI data, and for carefully considering how different processing choices can influence connectome organization.

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.

Specialization of the Human Hippocampal Long Axis Revisited

The hippocampus possesses anatomical differences along its long axis. Here the functional specialization of the human hippocampal long axis was explored using network-anchored precision functional MRI (N = 11) paired with behavioral analyses (N=266). Functional connectivity analyses demonstrated that the anterior hippocampus was preferentially correlated with a cerebral network associated with remembering, while the posterior hippocampus was correlated with a distinct network associated with behavioral salience. Seed regions placed within the hippocampus recapitulated the distinct cerebral networks. Functional characterization using task data within the same intensively sampled individuals discovered a functional double dissociation between the anterior and posterior hippocampal regions. The anterior hippocampal region was sensitive to remembering and imagining the future, specifically tracking the process of scene construction, while the posterior hippocampal region displayed transient responses to targets in an oddball detection task and to transitions between task blocks. These findings suggest specialization along the long axis of the hippocampus with differential responses reflecting the functional properties of the partner cerebral networks.

Reduced Lateralization of Multiple Functional Brain Networks in Autistic Males

Background

Autism spectrum disorder has been linked to a variety of organizational and developmental deviations in the brain. One such organizational difference involves hemispheric lateralization, which may be localized to language-relevant regions of the brain or distributed more broadly.

Methods

In the present study, we estimated brain hemispheric lateralization in autism based on each participant's unique functional neuroanatomy rather than relying on group-averaged data. Additionally, we explored potential relationships between the lateralization of the language network and behavioral phenotypes including verbal ability, language delay, and autism symptom severity. We hypothesized that differences in hemispheric asymmetries in autism would be limited to the language network, with the alternative hypothesis of pervasive differences in lateralization. We tested this and other hypotheses by employing a cross-sectional dataset of 118 individuals (48 autistic, 70 neurotypical). Using resting-state fMRI, we generated individual network parcellations and estimated network asymmetries using a surface area-based approach. A series of multiple regressions were then used to compare network asymmetries for eight significantly lateralized networks between groups.

Results

We found significant group differences in lateralization for the left-lateralized Language (d = -0.89), right-lateralized Salience/Ventral Attention-A (d = 0.55), and right-lateralized Control-B (d = 0.51) networks, with the direction of these group differences indicating less asymmetry in autistic individuals. These differences were robust across different datasets from the same participants. Furthermore, we found that language delay stratified language lateralization, with the greatest group differences in language lateralization occurring between autistic individuals with language delay and neurotypical individuals.

Limitations

The generalizability of our findings is restricted due to the male-only sample and greater representation of individuals with high verbal and cognitive performance.

Conclusions

These findings evidence a complex pattern of functional lateralization differences in autism, extending beyond the Language network to the Salience/Ventral Attention-A and Control-B networks, yet not encompassing all networks, indicating a selective divergence rather than a pervasive one. Furthermore, a differential relationship was identified between Language network lateralization and specific symptom profiles (namely, language delay) of autism.

Personalized functional brain network topography is associated with individual differences in youth cognition

Individual differences in cognition during childhood are associated with important social, physical, and mental health outcomes in adolescence and adulthood. Given that cortical surface arealization during development reflects the brain's functional prioritization, quantifying variation in the topography of functional brain networks across the developing cortex may provide insight regarding individual differences in cognition. We test this idea by defining personalized functional networks (PFNs) that account for interindividual heterogeneity in functional brain network topography in 9-10 year olds from the Adolescent Brain Cognitive Development℠ Study. Across matched discovery (n = 3525) and replication (n = 3447) samples, the total cortical representation of fronto-parietal PFNs positively correlates with general cognition. Cross-validated ridge regressions trained on PFN topography predict cognition in unseen data across domains, with prediction accuracy increasing along the cortex's sensorimotor-association organizational axis. These results establish that functional network topography heterogeneity is associated with individual differences in cognition before the critical transition into adolescence.

Test-retest reliability and predictive utility of a macroscale principal functional connectivity gradient

Mapping individual differences in brain function has been hampered by poor reliability as well as limited interpretability. Leveraging patterns of brain-wide functional connectivity (FC) offers some promise in this endeavor. In particular, a macroscale principal FC gradient that recapitulates a hierarchical organization spanning molecular, cellular, and circuit level features along a sensory-to-association cortical axis has emerged as both a parsimonious and interpretable measure of individual differences in behavior. However, the measurement reliabilities of this FC gradient have not been fully evaluated. Here, we assess the reliabilities of both global and regional principal FC gradient measures using test-retest data from the young adult Human Connectome Project (HCP-YA) and the Dunedin Study. Analyses revealed that the reliabilities of principal FC gradient measures were (1) consistently higher than those for traditional edge-wise FC measures, (2) higher for FC measures derived from general FC (GFC) in comparison with resting-state FC, and (3) higher for longer scan lengths. We additionally examined the relative utility of these principal FC gradient measures in predicting cognition and aging in both datasets as well as the HCP-aging dataset. These analyses revealed that regional FC gradient measures and global gradient range were significantly associated with aging in all three datasets, and moderately associated with cognition in the HCP-YA and Dunedin Study datasets, reflecting contractions and expansions of the cortical hierarchy, respectively. Collectively, these results demonstrate that measures of the principal FC gradient, especially derived using GFC, effectively capture a reliable feature of the human brain subject to interpretable and biologically meaningful individual variation, offering some advantages over traditional edge-wise FC measures in the search for brain-behavior associations.

Evidence for a Compensatory Relationship between Left- and Right-Lateralized Brain Networks

The two hemispheres of the human brain are functionally asymmetric. At the network level, the language network exhibits left-hemisphere lateralization. While this asymmetry is widely replicated, the extent to which other functional networks demonstrate lateralization remains a subject of Investigation. Additionally, it is unknown how the lateralization of one functional network may affect the lateralization of other networks within individuals. We quantified lateralization for each of 17 networks by computing the relative surface area on the left and right cerebral hemispheres. After examining the ecological, convergent, and external validity and test-retest reliability of this surface area-based measure of lateralization, we addressed two hypotheses across multiple datasets (Human Connectome Project = 553, Human Connectome Project-Development = 343, Natural Scenes Dataset = 8). First, we hypothesized that networks associated with language, visuospatial attention, and executive control would show the greatest lateralization. Second, we hypothesized that relationships between lateralized networks would follow a dependent relationship such that greater left-lateralization of a network would be associated with greater right-lateralization of a different network within individuals, and that this pattern would be systematic across individuals. A language network was among the three networks identified as being significantly left-lateralized, and attention and executive control networks were among the five networks identified as being significantly right-lateralized. Next, correlation matrices, an exploratory factor analysis, and confirmatory factor analyses were used to test the second hypothesis and examine the organization of lateralized networks. We found general support for a dependent relationship between highly left- and right-lateralized networks, meaning that across subjects, greater left lateralization of a given network (such as a language network) was linked to greater right lateralization of another network (such as a ventral attention/salience network) and vice versa. These results further our understanding of brain organization at the macro-scale network level in individuals, carrying specific relevance for neurodevelopmental conditions characterized by disruptions in lateralization such as autism and schizophrenia.

Multilayer meta-matching: translating phenotypic prediction models from multiple datasets to small data

Resting-state functional connectivity (RSFC) is widely used to predict phenotypic traits in individuals. Large sample sizes can significantly improve prediction accuracies. However, for studies of certain clinical populations or focused neuroscience inquiries, small-scale datasets often remain a necessity. We have previously proposed a "meta-matching" approach to translate prediction models from large datasets to predict new phenotypes in small datasets. We demonstrated large improvement of meta-matching over classical kernel ridge regression (KRR) when translating models from a single source dataset (UK Biobank) to the Human Connectome Project Young Adults (HCP-YA) dataset. In the current study, we propose two meta-matching variants ("meta-matching with dataset stacking" and "multilayer meta-matching") to translate models from multiple source datasets across disparate sample sizes to predict new phenotypes in small target datasets. We evaluate both approaches by translating models trained from five source datasets (with sample sizes ranging from 862 participants to 36,834 participants) to predict phenotypes in the HCP-YA and HCP-Aging datasets. We find that multilayer meta-matching modestly outperforms meta-matching with dataset stacking. Both meta-matching variants perform better than the original "meta-matching with stacking" approach trained only on the UK Biobank. All meta-matching variants outperform classical KRR and transfer learning by a large margin. In fact, KRR is better than classical transfer learning when less than 50 participants are available for finetuning, suggesting the difficulty of classical transfer learning in the very small sample regime. The multilayer meta-matching model is publicly available at GITHUB_LINK.

Towards a biologically annotated brain connectome

The brain is a network of interleaved neural circuits. In modern connectomics, brain connectivity is typically encoded as a network of nodes and edges, abstracting away the rich biological detail of local neuronal populations. Yet biological annotations for network nodes - such as gene expression, cytoarchitecture, neurotransmitter receptors or intrinsic dynamics - can be readily measured and overlaid on network models. Here we review how connectomes can be represented and analysed as annotated networks. Annotated connectomes allow us to reconceptualize architectural features of networks and to relate the connection patterns of brain regions to their underlying biology. Emerging work demonstrates that annotated connectomes help to make more veridical models of brain network formation, neural dynamics and disease propagation. Finally, annotations can be used to infer entirely new inter-regional relationships and to construct new types of network that complement existing connectome representations. In summary, biologically annotated connectomes offer a compelling way to study neural wiring in concert with local biological features.

Side-by-side regions in dorsolateral prefrontal cortex estimated within the individual respond differentially to domain-specific and domain-flexible processes

A recurring debate concerns whether regions of primate prefrontal cortex (PFC) support domain-flexible or domain-specific processes. Here we tested the hypothesis with functional MRI (fMRI) that side-by-side PFC regions, within distinct parallel association networks, differentially support domain-flexible and domain-specialized processing. Individuals (N = 9) were intensively sampled, and all effects were estimated within their own idiosyncratic anatomy. Within each individual, we identified PFC regions linked to distinct networks, including a dorsolateral PFC (DLPFC) region coupled to the medial temporal lobe (MTL) and an extended region associated with the canonical multiple-demand network. We further identified an inferior PFC region coupled to the language network. Exploration in separate task data, collected within the same individuals, revealed a robust functional triple dissociation. The DLPFC region linked to the MTL was recruited during remembering and imagining the future, distinct from juxtaposed regions that were modulated in a domain-flexible manner during working memory. The inferior PFC region linked to the language network was recruited during sentence processing. Detailed analysis of the trial-level responses further revealed that the DLPFC region linked to the MTL specifically tracked processes associated with scene construction. These results suggest that the DLPFC possesses a domain-specialized region that is small and easily confused with nearby (larger) regions associated with cognitive control. The newly described region is domain specialized for functions traditionally associated with the MTL. We discuss the implications of these findings in relation to convergent anatomical analysis in the monkey.NEW & NOTEWORTHY Competing hypotheses link regions of prefrontal cortex (PFC) to domain-flexible or domain-specific processes. Here, using a precision neuroimaging approach, we identify a domain-specialized region in dorsolateral PFC, coupled to the medial temporal lobe and recruited for scene construction. This region is juxtaposed to, but distinct from, broader PFC regions recruited flexibly for cognitive control. Region distinctions align with broader network differences, suggesting that PFC regions gain dissociable processing properties via segregated anatomical projections.

Probing neurodynamics of experienced emotions-a Hitchhiker's guide to film fMRI

Film functional magnetic resonance imaging (fMRI) has gained tremendous popularity in many areas of neuroscience. However, affective neuroscience remains somewhat behind in embracing this approach, even though films lend themselves to study how brain function gives rise to complex, dynamic and multivariate emotions. Here, we discuss the unique capabilities of film fMRI for emotion research, while providing a general guide of conducting such research. We first give a brief overview of emotion theories as these inform important design choices. Next, we discuss films as experimental paradigms for emotion elicitation and address the process of annotating them. We then situate film fMRI in the context of other fMRI approaches, and present an overview of results from extant studies so far with regard to advantages of film fMRI. We also give an overview of state-of-the-art analysis techniques including methods that probe neurodynamics. Finally, we convey limitations of using film fMRI to study emotion. In sum, this review offers a practitioners' guide to the emerging field of film fMRI and underscores how it can advance affective neuroscience.

Network and State Specificity in Connectivity-Based Predictions of Individual Behavior

Predicting individual behavior from brain functional connectivity (FC) patterns can contribute to our understanding of human brain functioning. This may apply in particular if predictions are based on features derived from circumscribed, a priori defined functional networks, which improves interpretability. Furthermore, some evidence suggests that task-based FC data may yield more successful predictions of behavior than resting-state FC data. Here, we comprehensively examined to what extent the correspondence of functional network priors and task states with behavioral target domains influences the predictability of individual performance in cognitive, social, and affective tasks. To this end, we used data from the Human Connectome Project for large-scale out-of-sample predictions of individual abilities in working memory (WM), theory-of-mind cognition (SOCIAL), and emotion processing (EMO) from FC of corresponding and non-corresponding states (WM/SOCIAL/EMO/resting-state) and networks (WM/SOCIAL/EMO/whole-brain connectome). Using root mean squared error and coefficient of determination to evaluate model fit revealed that predictive performance was rather poor overall. Predictions from whole-brain FC were slightly better than those from FC in task-specific networks, and a slight benefit of predictions based on FC from task versus resting state was observed for performance in the WM domain. Beyond that, we did not find any significant effects of a correspondence of network, task state, and performance domains. Together, these results suggest that multivariate FC patterns during both task and resting states contain rather little information on individual performance levels, calling for a reconsideration of how the brain mediates individual differences in mental abilities.

Functional brain networks are associated with both sex and gender in children

Sex and gender are associated with human behavior throughout the lifespan and across health and disease, but whether they are associated with similar or distinct neural phenotypes is unknown. Here, we demonstrate that, in children, sex and gender are uniquely reflected in the intrinsic functional connectivity of the brain. Unimodal networks are more strongly associated with sex while heteromodal networks are more strongly associated with gender. These results suggest sex and gender are irreducible to one another not only in society but also in biology.

Mindfulness-based therapy improves brain functional network reconfiguration efficiency

Mindfulness-based interventions are showing increasing promise as a treatment for psychological disorders, with improvements in cognition and emotion regulation after intervention. Understanding the changes in functional brain activity and neural plasticity that underlie these benefits from mindfulness interventions is thus of interest in current neuroimaging research. Previous studies have found functional brain changes during resting and task states to be associated with mindfulness both cross-sectionally and longitudinally, particularly in the executive control, default mode and salience networks. However, limited research has combined information from rest and task to study mindfulness-related functional changes in the brain, particularly in the context of intervention studies with active controls. Recent work has found that the reconfiguration efficiency of brain activity patterns between rest and task states is behaviorally relevant in healthy young adults. Thus, we applied this measure to investigate how mindfulness intervention changed functional reconfiguration between rest and a breath-counting task in elderly participants with self-reported sleep difficulties. Improving on previous longitudinal designs, we compared the intervention effects of a mindfulness-based therapy to an active control (sleep hygiene) intervention. We found that mindfulness intervention improved self-reported mindfulness measures and brain functional reconfiguration efficiency in the executive control, default mode and salience networks, though the brain and behavioral changes were not associated with each other. Our findings suggest that neuroplasticity may be induced through regular mindfulness practice, thus bringing the intrinsic functional configuration in participants' brains closer to a state required for mindful awareness.

Individual differences in the language task-evoked and resting-state functional networks

The resting state functional network is highly variable across individuals. However, inter-individual differences in functional networks evoked by language tasks and their comparison with resting state are still unclear. To address these two questions, we used T1 anatomical data and functional brain imaging data of resting state and a story comprehension task from the Human Connectome Project (HCP) to characterize functional network variability and investigate the uniqueness of the functional network in both task and resting states. We first demonstrated that intrinsic and task-induced functional networks exhibited remarkable differences across individuals, and language tasks can constrain inter-individual variability in the functional brain network. Furthermore, we found that the inter-individual variability of functional networks in two states was broadly consistent and spatially heterogeneous, with high-level association areas manifesting more significant variability than primary visual processing areas. Our results suggested that the functional network underlying language comprehension is unique at the individual level, and the inter-individual variability architecture of the functional network is broadly consistent in language task and resting state.

A personalized cortical atlas for functional regions of interest

Advances in functional MRI (fMRI) allow mapping an individual's brain function in vivo. Task fMRI can localize domain-specific regions of cognitive processing or functional regions of interest (fROIs) within an individual. Moreover, data from resting state (no task) fMRI can be used to define an individual's connectome, which can characterize that individual's functional organization via connectivity-based parcellations. However, can connectivity-based parcellations alone predict an individual's fROIs? Here, we describe an approach to compute individualized rs-fROIs (i.e., regions that correspond to given fROI constructed using only resting state data) for motor control, working memory, high-level vision, and language comprehension. The rs-fROIs were computed and validated using a large sample of young adults (n = 1,018) with resting state and task fMRI from the Human Connectome Project. First, resting state parcellations were defined across a sequence of resolutions from broadscale to fine-grained networks in a training group of 500 individuals. Second, 21 rs-fROIs were defined from the training group by identifying the rs network that most closely matched task-defined fROIs across all individuals. Third, the selectivity of rs-fROIs was investigated in a training set of the remaining 518 individuals. All computed rs-fROIs were indeed selective for their preferred category. Critically, the rs-fROIs had higher selectivity than probabilistic atlas parcels for nearly all fROIs. In conclusion, we present a potential approach to define selective fROIs on an individual-level circumventing the need for multiple task-based localizers.NEW & NOTEWORTHY We compute individualized resting state parcels that identify an individual's own functional regions of interest (fROIs) for high-level vision, language comprehension, motor control, and working memory, using only their functional connectome. This approach demonstrates a rapid and powerful alternative for finding a large set of fROIs in an individual, using only their unique connectivity pattern, which does not require the costly acquisition of multiple fMRI localizer tasks.

Neuroanatomical and Functional Dissociations between Variably Present Anterior Lateral Prefrontal Sulci

The lateral prefrontal cortex (LPFC) is an evolutionarily expanded region in humans that is critical for numerous complex functions, many of which are largely hominoid specific. Although recent work shows that the presence or absence of specific sulci in anterior LPFC is associated with cognitive performance across age groups, it is unknown whether the presence of these structures relates to individual differences in the functional organization of LPFC. To fill this gap in knowledge, we leveraged multimodal neuroimaging data from two samples encompassing 82 young adult humans (aged 22-36 years) and show that the dorsal and ventral components of the paraintermediate frontal sulcus, or pimfs, present distinct morphological (surface area), architectural (thickness and myelination), and functional (resting-state connectivity networks) properties. We further contextualize the pimfs components within classic and modern cortical parcellations. Taken together, the dorsal and ventral pimfs components mark transitions in LPFC anatomy and function, across metrics and parcellations. These results emphasize that the pimfs is a critical structure to consider when examining individual differences in the anatomical and functional organization of LPFC and suggest that future individual-level parcellations could benefit from incorporating sulcal anatomy when delineating LPFC cortical regions.

Robust hierarchically organized whole-brain patterns of dysconnectivity in schizophrenia spectrum disorders observed after personalized intrinsic network topography

BACKGROUND

Spatial patterns of brain functional connectivity can vary substantially at the individual level. Applying cortical surface-based approaches with individualized rather than group templates may accelerate the discovery of biological markers related to psychiatric disorders. We investigated cortico-subcortical networks from multi-cohort data in people with schizophrenia spectrum disorders (SSDs) and healthy controls (HC) using individualized connectivity profiles.

METHODS

We utilized resting-state and anatomical MRI data from n = 406 participants (n = 203 SSD, n = 203 HC) from four cohorts. Functional timeseries were extracted from previously defined intrinsic network subregions of the striatum, thalamus, and cerebellum as well as 80 cortical regions of interest, representing six intrinsic networks using (1) volume-based approaches, (2) a surface-based group atlas approaches, and (3) Personalized Intrinsic Network Topography (PINT).

RESULTS

The correlations between all cortical networks and the expected subregions of the striatum, cerebellum, and thalamus were increased using a surface-based approach (Cohen's D volume vs. surface 0.27-1.00, all p < 10-6 ) and further increased after PINT (Cohen's D surface vs. PINT 0.18-0.96, all p < 10-4 ). In SSD versus HC comparisons, we observed robust patterns of dysconnectivity that were strengthened using a surface-based approach and PINT (Number of differing pairwise-correlations: volume: 404, surface: 570, PINT: 628, FDR corrected).

CONCLUSION

Surface-based and individualized approaches can more sensitively delineate cortical network dysconnectivity differences in people with SSDs. These robust patterns of dysconnectivity were visibly organized in accordance with the cortical hierarchy, as predicted by computational models.

Neural Representation of Collective Self-esteem in Resting-state Functional Connectivity and its Validation in Task-dependent Modality

INTRODUCTION

Collective self-esteem (CSE) is an important personality variable, defined as self-worth derived from membership in social groups. A study explored the neural basis of CSE using a task-based functional magnetic resonance imaging (fMRI) paradigm; however, task-independent neural basis of CSE remains to be explored, and whether the CSE neural basis of resting-state fMRI is consistent with that of task-based fMRI is unclear.

METHODS

We built support vector regression (SVR) models to predict CSE scores using topological metrics measured in the resting-state functional connectivity network (RSFC) as features. Then, to test the reliability of the SVR analysis, the activation pattern of the identified brain regions from SVR analysis was used as features to distinguish collective self-worth from other conditions by multivariate pattern classification in task-based fMRI dataset.

RESULTS

SVR analysis results showed that leverage centrality successfully decoded the individual differences in CSE. The ventromedial prefrontal cortex, anterior cingulate cortex, posterior cingulate gyrus, precuneus, orbitofrontal cortex, posterior insula, postcentral gyrus, inferior parietal lobule, temporoparietal junction, and inferior frontal gyrus, which are involved in self-referential processing, affective processing, and social cognition networks, participated in this prediction. Multivariate pattern classification analysis found that the activation pattern of the identified regions from the SVR analysis successfully distinguished collective self-worth from relational self-worth, personal self-worth and semantic control.

CONCLUSION

Our findings revealed CSE neural basis in the whole-brain RSFC network, and established the concordance between leverage centrality and the activation pattern (evoked during collective self-worth task) of the identified regions in terms of representing CSE.

Human Striatal Association Megaclusters

The striatum receives projections from multiple regions of the cerebral cortex consistent with its role in diverse motor, affective, and cognitive functions. Supporting cognitive functions, the caudate receives projections from cortical association regions. Building on recent insights about the details of how multiple cortical networks are specialized for distinct aspects of higher-order cognition, we revisited caudate organization using within-individual precision neuroimaging (n=2, each participant scanned 31 times). Detailed analysis revealed that the caudate has side-by-side zones that are coupled to at least Give distinct distributed association networks, paralleling the specialization observed in the cerebral cortex. Examining correlation maps from closely juxtaposed seed regions in the caudate recapitulated the Give distinct cerebral networks including their multiple spatially distributed regions. These results extend the general notion of parallel specialized basal ganglia circuits, with the additional discovery that even within the caudate, there is Gine-grained separation of multiple distinct higher-order networks.

Controversies and progress on standardization of large-scale brain network nomenclature

Progress in scientific disciplines is accompanied by standardization of terminology. Network neuroscience, at the level of macroscale organization of the brain, is beginning to confront the challenges associated with developing a taxonomy of its fundamental explanatory constructs. The Workgroup for HArmonized Taxonomy of NETworks (WHATNET) was formed in 2020 as an Organization for Human Brain Mapping (OHBM)-endorsed best practices committee to provide recommendations on points of consensus, identify open questions, and highlight areas of ongoing debate in the service of moving the field toward standardized reporting of network neuroscience results. The committee conducted a survey to catalog current practices in large-scale brain network nomenclature. A few well-known network names (e.g., default mode network) dominated responses to the survey, and a number of illuminating points of disagreement emerged. We summarize survey results and provide initial considerations and recommendations from the workgroup. This perspective piece includes a selective review of challenges to this enterprise, including (1) network scale, resolution, and hierarchies; (2) interindividual variability of networks; (3) dynamics and nonstationarity of networks; (4) consideration of network affiliations of subcortical structures; and (5) consideration of multimodal information. We close with minimal reporting guidelines for the cognitive and network neuroscience communities to adopt.

Connectome-based modelling of neurodegenerative diseases: towards precision medicine and mechanistic insight

Neurodegenerative diseases are the most common cause of dementia. Although their underlying molecular pathologies have been identified, there is substantial heterogeneity in the patterns of progressive brain alterations across and within these diseases. Recent advances in neuroimaging methods have revealed that pathological proteins accumulate along specific macroscale brain networks, implicating the network architecture of the brain in the system-level pathophysiology of neurodegenerative diseases. However, the extent to which 'network-based neurodegeneration' applies across the wide range of neurodegenerative disorders remains unclear. Here, we discuss the state-of-the-art of neuroimaging-based connectomics for the mapping and prediction of neurodegenerative processes. We review findings supporting brain networks as passive conduits through which pathological proteins spread. As an alternative view, we also discuss complementary work suggesting that network alterations actively modulate the spreading of pathological proteins between connected brain regions. We conclude this Perspective by proposing an integrative framework in which connectome-based models can be advanced along three dimensions of innovation: incorporating parameters that modulate propagation behaviour on the basis of measurable biological features; building patient-tailored models that use individual-level information and allowing model parameters to interact dynamically over time. We discuss promises and pitfalls of these strategies for improving disease insights and moving towards precision medicine.

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

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 network overlap is indicative of linear additive coupling. These results suggest that regions of network overlap concurrently process information from all contributing networks, potentially pointing to the role of overlapping network organization in the integration of information across multiple brain systems.

Estimating dynamic individual coactivation patterns based on densely sampled resting-state fMRI data and utilizing it for better subject identification

As a complex dynamic system, the brain exhibits spatially organized recurring patterns of activity over time. Coactivation patterns (CAPs), which analyzes data from each single frame, have been utilized to detect transient brain activity states recently. However, previous CAP analyses have been conducted at the group level, which might neglect meaningful individual differences. Here, we estimated individual CAP states at both subject- and scan-level based on a densely sampled dataset: Midnight Scan Club. We used differential identifiability, which measures the gap between intra- and inter-subject similarity, to evaluate individual differences. We found individual CAPs at the subject-level achieved the best fingerprinting ability by maintaining high intra-subject similarity and enlarging inter-subject differences, and brain regions of association networks mainly contributed to the identifiability. On the other hand, scan-level CAP states were unstable across scans for the same participant. Expectedly, we found subject-specific CAPs became more reliable and discriminative with more data (i.e., longer duration). As the acquisition time of each participant is limited in practice, our results recommend a data collection strategy that collects more scans with appropriate duration (e.g., 12 ~ 15 min/scan) to obtain more reliable subject-specific CAPs, when total acquisition time is fixed (e.g., 150 min). In summary, this work has constructed reliable subject-specific CAP states with meaningful individual differences, and recommended an appropriate data collection strategy, which can guide subsequent investigations into individualized brain dynamics.

Insights from personalized models of brain and behavior for identifying biomarkers in psychiatry

A main goal in translational neuroscience is to identify neural correlates of psychopathology ("biomarkers") that can be used to facilitate diagnosis, prognosis, and treatment. This goal has led to substantial research into how psychopathology symptoms relate to large-scale brain systems. However, these efforts have not yet resulted in practical biomarkers used in clinical practice. One reason for this underwhelming progress may be that many study designs focus on increasing sample size instead of collecting additional data within each individual. This focus limits the reliability and predictive validity of brain and behavioral measures in any one person. As biomarkers exist at the level of individuals, an increased focus on validating them within individuals is warranted. We argue that personalized models, estimated from extensive data collection within individuals, can address these concerns. We review evidence from two, thus far separate, lines of research on personalized models of (1) psychopathology symptoms and (2) fMRI measures of brain networks. We close by proposing approaches uniting personalized models across both domains to improve biomarker research.

Investigating the heterogeneity within the somatosensory-motor network and its relationship with the attention and default systems

The somatosensory-motor network (SMN) not only plays an important role in primary somatosensory and motor processing but is also central to many disorders. However, the SMN heterogeneity related to higher-order systems still remains unclear. Here, we investigated SMN heterogeneity from multiple perspectives. To characterize the SMN substructures in more detail, we used ultra-high-field functional MRI to delineate a finer-grained cortical parcellation containing 430 parcels that is more homogenous than the state-of-the-art parcellation. We personalized the new parcellation to account for individual differences and identified multiscale individual-specific brain structures. We found that the SMN subnetworks showed distinct resting-state functional connectivity (RSFC) patterns. The Hand subnetwork was central within the SMN and exhibited stronger RSFC with the attention systems than the other subnetworks, whereas the Tongue subnetwork exhibited stronger RSFC with the default systems. This two-fold differentiation was observed in the temporal ordering patterns within the SMN. Furthermore, we characterized how the distinct attention and default streams were carried forward into the functions of the SMN using dynamic causal modeling and identified two behavioral domains associated with this SMN fractionation using meta-analytic tools. Overall, our findings provided important insights into the heterogeneous SMN organization at the system level and suggested that the Hand subnetwork may be preferentially involved in exogenous processes, whereas the Tongue subnetwork may be more important in endogenous processes.

Effect of Extremely Preterm Birth on Adolescent Brain Network Organization

Introduction: Extremely preterm (EPT) birth, defined as birth at a gestational age (GA) <28 weeks, can have a lasting impact on cognition throughout the life span. Previous investigations reveal differences in brain structure and connectivity between infants born preterm and full-term (FT), but how does preterm birth impact the adolescent connectome? Methods: In this study, we investigate how EPT birth can alter broadscale network organization later in life by comparing resting-state functional magnetic resonance imaging connectome-based parcellations of the entire cortex in adolescents born EPT (N = 22) to age-matched adolescents born FT (GA ≥37 weeks, N = 28). We compare these parcellations to adult parcellations from previous studies and explore the relationship between an individual's network organization and behavior. Results: Primary (occipital and sensorimotor) and frontoparietal networks were observed in both groups. However, there existed notable differences in the limbic and insular networks. Surprisingly, the connectivity profile of the limbic network of EPT adolescents was more adultlike than the same network in FT adolescents. Finally, we found a relationship between adolescents' overall cognition score and their limbic network maturity. Discussion: Overall, preterm birth may contribute to the atypical development of broadscale network organization in adolescence and may partially explain the observed cognitive deficits.

Morphometric similarity network alterations in COVID-19 survivors correlate with behavioral features and transcriptional signatures

OBJECTIVES

To explore the differences in the cortical morphometric similarity network (MSN) between COVID-19 survivors and healthy controls, and the correlation between these differences and behavioralfeatures and transcriptional signatures.

MATERIALS & METHODS

39 COVID-19 survivors and 39 age-, sex- and education years-matched healthy controls (HCs) were included. All participants underwent MRI and behavioral assessments (PCL-17, GAD-7, PHQ-9). MSN analysis was used to compute COVID-19 survivors vs. HCs differences across brain regions. Correlation analysis was used to determine the associations between regional MSN differences and behavioral assessments, and determine the spatial similarities between regional MSN differences and risk genes transcriptional activity.

RESULTS

COVID-19 survivors exhibited decreased regional MSN in insula, precuneus, transverse temporal, entorhinal, para-hippocampal, rostral middle frontal and supramarginal cortices, and increased regional MSN in pars triangularis, lateral orbitofrontal, superior frontal, superior parietal, postcentral, and inferior temporal cortices. Regional MSN value of lateral orbitofrontal cortex was positively associated with GAD-7 and PHQ-9 scores, and rostral middle frontal was negatively related to PHQ-9 scores. The analysis of spatial similarities showed that seven risk genes (MFGE8, MOB2, NUP62, PMPCA, SDSL, TMEM178B, and ZBTB11) were related to regional MSN values.

CONCLUSION

The MSN differences were associated with behavioral and transcriptional signatures, early psychological counseling or intervention may be required to COVID-19 survivors. Our study provided a new insight into understanding the altered coordination of structure in COVID-19 and may offer a new endophenotype to further investigate the brain substrate.

Situating the parietal memory network in the context of multiple parallel distributed networks using high-resolution functional connectivity

A principle of brain organization is that networks serving higher cognitive functions are widely distributed across the brain. One exception has been the parietal memory network (PMN), which plays a role in recognition memory but is often defined as being restricted to posteromedial association cortex. We hypothesized that high-resolution estimates of the PMN would reveal small regions that had been missed by prior approaches. High-field 7T functional magnetic resonance imaging (fMRI) data from extensively sampled participants was used to define the PMN within individuals. The PMN consistently extended beyond the core posteromedial set to include regions in the inferior parietal lobule; rostral, dorsal, medial, and ventromedial prefrontal cortex; the anterior insula; and ramus marginalis of the cingulate sulcus. The results suggest that, when fine-scale anatomy is considered, the PMN matches the expected distributed architecture of other association networks, reinforcing that parallel distributed networks are an organizing principle of association cortex.

Brain hubs defined in the group do not overlap with regions of high inter-individual variability

Connector 'hubs' are brain regions with links to multiple networks. These regions are hypothesized to play a critical role in brain function. While hubs are often identified based on group-average functional magnetic resonance imaging (fMRI) data, there is considerable inter-subject variation in the functional connectivity profiles of the brain, especially in association regions where hubs tend to be located. Here we investigated how group hubs are related to locations of inter-individual variability. To answer this question, we examined inter-individual variation at group-level hubs in both the Midnight Scan Club and Human Connectome Project datasets. The top group hubs defined based on the participation coefficient did not overlap strongly with the most prominent regions of inter-individual variation (termed 'variants' in prior work). These hubs have relatively strong similarity across participants and consistent cross-network profiles, similar to what was seen for many other areas of cortex. Consistency across participants was further improved when these hubs were allowed to shift slightly in local position. Thus, our results demonstrate that the top group hubs defined with the participation coefficient are generally consistent across people, suggesting they may represent conserved cross-network bridges. More caution is warranted with alternative hub measures, such as community density (which are based on spatial proximity to network borders) and intermediate hub regions which show higher correspondence to locations of individual variability.

Expansion of a frontostriatal salience network in individuals with depression

Hundreds of neuroimaging studies spanning two decades have revealed differences in brain structure and functional connectivity in depression, but with modest effect sizes, complicating efforts to derive mechanistic pathophysiologic insights or develop biomarkers. 1 Furthermore, although depression is a fundamentally episodic condition, few neuroimaging studies have taken a longitudinal approach, which is critical for understanding cause and effect and delineating mechanisms that drive mood state transitions over time. The emerging field of precision functional mapping using densely-sampled longitudinal neuroimaging data has revealed unexpected, functionally meaningful individual differences in brain network topology in healthy individuals, 2-5 but these approaches have never been applied to individuals with depression. Here, using precision functional mapping techniques and 11 datasets comprising n=187 repeatedly sampled individuals and >21,000 minutes of fMRI data, we show that the frontostriatal salience network is expanded two-fold in most individuals with depression. This effect was replicable in multiple samples, including large-scale, group-average data (N=1,231 subjects), and caused primarily by network border shifts affecting specific functional systems, with three distinct modes of encroachment occurring in different individuals. Salience network expansion was unexpectedly stable over time, unaffected by changes in mood state, and detectable in children before the subsequent onset of depressive symptoms in adolescence. Longitudinal analyses of individuals scanned up to 62 times over 1.5 years identified connectivity changes in specific frontostriatal circuits that tracked fluctuations in specific symptom domains and predicted future anhedonia symptoms before they emerged. Together, these findings identify a stable trait-like brain network topology that may confer risk for depression and mood-state dependent connectivity changes in frontostriatal circuits that predict the emergence and remission of depressive symptoms over time.

Within-Individual Organization of the Human Cerebral Cortex: Networks, Global Topography, and Function

The human cerebral cortex is populated by specialized regions that are organized into networks. Here we estimated networks using a Multi-Session Hierarchical Bayesian Model (MS-HBM) applied to intensively sampled within-individual functional MRI (fMRI) data. The network estimation procedure was initially developed and tested in two participants (each scanned 31 times) and then prospectively applied to 15 new participants (each scanned 8 to 11 times). Detailed analysis of the networks revealed a global organization. Locally organized first-order sensory and motor networks were surrounded by spatially adjacent second-order networks that also linked to distant regions. Third-order networks each possessed regions distributed widely throughout association cortex. Moreover, regions of distinct third-order networks displayed side-by-side juxtapositions with a pattern that repeated similarly across multiple cortical zones. We refer to these as Supra-Areal Association Megaclusters (SAAMs). Within each SAAM, two candidate control regions were typically adjacent to three separate domain-specialized regions. Independent task data were analyzed to explore functional response properties. The somatomotor and visual first-order networks responded to body movements and visual stimulation, respectively. A subset of the second-order networks responded to transients in an oddball detection task, consistent with a role in orienting to salient or novel events. The third-order networks, including distinct regions within each SAAM, showed two levels of functional specialization. Regions linked to candidate control networks responded to working memory load across multiple stimulus domains. The remaining regions within each SAAM did not track working memory load but rather dissociated across language, social, and spatial / episodic processing domains. These results support a model of the cerebral cortex in which progressively higher-order networks nest outwards from primary sensory and motor cortices. Within the apex zones of association cortex there is specialization of large-scale networks that divides domain-flexible from domain-specialized regions repeatedly across parietal, temporal, and prefrontal cortices. We discuss implications of these findings including how repeating organizational motifs may emerge during development.

Enhancing precision in human neuroscience

Human neuroscience has always been pushing the boundary of what is measurable. During the last decade, concerns about statistical power and replicability - in science in general, but also specifically in human neuroscience - have fueled an extensive debate. One important insight from this discourse is the need for larger samples, which naturally increases statistical power. An alternative is to increase the precision of measurements, which is the focus of this review. This option is often overlooked, even though statistical power benefits from increasing precision as much as from increasing sample size. Nonetheless, precision has always been at the heart of good scientific practice in human neuroscience, with researchers relying on lab traditions or rules of thumb to ensure sufficient precision for their studies. In this review, we encourage a more systematic approach to precision. We start by introducing measurement precision and its importance for well-powered studies in human neuroscience. Then, determinants for precision in a range of neuroscientific methods (MRI, M/EEG, EDA, Eye-Tracking, and Endocrinology) are elaborated. We end by discussing how a more systematic evaluation of precision and the application of respective insights can lead to an increase in reproducibility in human neuroscience.

Predicting phenotypes of elderly from resting state fMRI

Machine learning techniques are increasingly embraced in neuroimaging studies of healthy and diseased human brains. They have been used successfully in predicting phenotypes, or even clinical outcomes, and in turning functional connectome metrics into phenotype biomarkers of both healthy individuals and patients. In this study, we used functional connectivity characteristics based on resting state functional magnetic resonance imaging data to accurately classify healthy elderly in terms of their phenotype status. Additionally, as the functional connections that contribute to the classification can be identified, we can draw inferences about the network that is predictive of the investigated phenotypes. Our proposed pipeline for phenotype classification can be expanded to other phenotypes (cognitive, psychological, clinical) and possibly be used to shed light on the modifiable risk and protective factors in normative and pathological brain aging.

Subject-specific whole-brain parcellations of nodes and boundaries are modulated differently under 10 Hz rTMS

Repetitive transcranial magnetic stimulation (rTMS) has gained considerable importance in the treatment of neuropsychiatric disorders, including major depression. However, it is not yet understood how rTMS alters brain's functional connectivity. Here we report changes in functional connectivity captured by resting state functional magnetic resonance imaging (rsfMRI) within the first hour after 10 Hz rTMS. We apply subject-specific parcellation schemes to detect changes (1) in network nodes, where the strongest functional connectivity of regions is observed, and (2) in network boundaries, where functional transitions between regions occur. We use support vector machine (SVM), a widely used machine learning algorithm that is robust and effective, for the classification and characterization of time intervals of changes in node and boundary maps. Our results reveal that changes in connectivity at the boundaries are slower and more complex than in those observed in the nodes, but of similar magnitude according to accuracy confidence intervals. These results were strongest in the posterior cingulate cortex and precuneus. As network boundaries are indeed under-investigated in comparison to nodes in connectomics research, our results highlight their contribution to functional adjustments to rTMS.

Cognition and Brain System Segregation in Pediatric Brain Tumor Patients Treated with Proton Therapy

Purpose

Pediatric brain tumor patients often experience significant cognitive sequelae. Resting-state functional MRI (rsfMRI) provides a measure of brain network organization, and we hypothesize that pediatric brain tumor patients treated with proton therapy will demonstrate abnormal brain network architecture related to cognitive outcome and radiation dosimetry.

Participants and Methods

Pediatric brain tumor patients treated with proton therapy were enrolled on a prospective study of cognitive assessment using the NIH Toolbox Cognitive Domain. rsfMRI was obtained in participants able to complete unsedated MRI. Brain system segregation (BSS), a measure of brain network architecture, was calculated for the whole brain, the high-level cognition association systems, and the sensory-motor systems.

Results

Twenty-six participants were enrolled in the study for cognitive assessment, and 18 completed rsfMRI. There were baseline cognitive deficits in attention and inhibition and processing speed prior to radiation with worsening performance over time in multiple domains. Average BSS across the whole brain was significantly decreased in participants compared with healthy controls (1.089 and 1.101, respectively; P = 0.001). Average segregation of association systems was significantly lower in participants than in controls (P < 0.001) while there was no difference in the sensory motor networks (P = 0.70). Right hippocampus dose was associated with worse attention and inhibition (P < 0.05) and decreased segregation in the dorsal attention network (P < 0.05).

Conclusion

Higher mean dose to the right hippocampus correlated with worse dorsal attention network segregation and worse attention and inhibition cognitive performance. Patients demonstrated alterations in brain network organization of association systems measured with rsfMRI; however, somatosensory system segregation was no different from healthy children. Further work with preradiation rsfMRI is needed to assess the effects of surgery and presence of a tumor on brain network architecture.

Model connectivity: leveraging the power of encoding models to overcome the limitations of functional connectivity

Functional connectivity (FC) is the most popular method for recovering functional networks of brain areas with fMRI. However, because FC is defined as temporal correlations in brain activity, FC networks are confounded by noise and lack a precise functional role. To overcome these limitations, we developed model connectivity (MC). MC is defined as similarities in encoding model weights, which quantify reliable functional activity in terms of interpretable stimulus- or task-related features. To compare FC and MC, both methods were applied to a naturalistic story listening dataset. FC recovered spatially broad networks that are confounded by noise, and that lack a clear role during natural language comprehension. By contrast, MC recovered spatially localized networks that are robust to noise, and that represent distinct categories of semantic concepts. Thus, MC is a powerful data-driven approach for recovering and interpreting the functional networks that support complex cognitive processes.

Intermediately synchronised brain states optimise trade-off between subject specificity and predictive capacity

Functional connectivity (FC) refers to the statistical dependencies between activity of distinct brain areas. To study temporal fluctuations in FC within the duration of a functional magnetic resonance imaging (fMRI) scanning session, researchers have proposed the computation of an edge time series (ETS) and their derivatives. Evidence suggests that FC is driven by a few time points of high-amplitude co-fluctuation (HACF) in the ETS, which may also contribute disproportionately to interindividual differences. However, it remains unclear to what degree different time points actually contribute to brain-behaviour associations. Here, we systematically evaluate this question by assessing the predictive utility of FC estimates at different levels of co-fluctuation using machine learning (ML) approaches. We demonstrate that time points of lower and intermediate co-fluctuation levels provide overall highest subject specificity as well as highest predictive capacity of individual-level phenotypes.