Individual-specific features of brain systems identified with resting state functional correlations

Person-specific and precision neuroimaging: Current methods and future directions

Most neuroimaging studies of brain function analyze data in normalized space to identify regions of common activation across participants. These studies treat interindividual differences in brain organization as noise, but this approach can obscure important information about the brain's functional architecture. Recently, a number of studies have adopted a person-specific approach that aims to characterize these individual differences and explore their reliability and implications for behavior. A subset of these studies has taken a precision imaging approach that collects multiple hours of data from each participant to map brain function on a finer scale. In this review, we provide a broad overview of how person-specific and precision imaging techniques have used resting-state measures to examine individual differences in the brain's organization and their impact on behavior, followed by how task-based activity continues to add detail to these discoveries. We argue that person-specific and precision approaches demonstrate substantial promise in uncovering new details of the brain's functional organization and its relationship to behavior in many areas of cognitive neuroscience. We also discuss some current limitations in this new field and some new directions it may take.

Altered brain network topology in children with auditory processing disorder: A resting-state multi-echo fMRI study

Children with auditory processing disorder (APD) experience hearing difficulties, particularly in the presence of competing sounds, despite having normal audiograms. There is considerable debate on whether APD symptoms originate from bottom-up (e.g., auditory sensory processing) and/or top-down processing (e.g., cognitive, language, memory). A related issue is that little is known about whether functional brain network topology is altered in APD. Therefore, we used resting-state functional magnetic resonance imaging data to investigate the functional brain network organization of 57 children from 8 to 14 years old, diagnosed with APD (n = 28) and without hearing difficulties (healthy control, HC; n = 29). We applied complex network analysis using graph theory to assess the whole-brain integration and segregation of functional networks and brain hub architecture. Our results showed children with APD and HC have similar global network properties -i.e., an average of all brain regions- and modular organization. Still, the APD group showed different hub architecture in default mode-ventral attention, somatomotor and frontoparietal-dorsal attention modules. At the nodal level -i.e., single-brain regions-, we observed decreased participation coefficient (PC - a measure quantifying the diversity of between-network connectivity) in auditory cortical regions in APD, including bilateral superior temporal gyrus and left middle temporal gyrus. Beyond auditory regions, PC was also decreased in APD in bilateral posterior temporo-occipital cortices, left intraparietal sulcus, and right posterior insular cortex. Correlation analysis suggested a positive association between PC in the left parahippocampal gyrus and the listening-in-spatialized-noise -sentences task where APD children were engaged in auditory perception. In conclusion, our findings provide evidence of altered brain network organization in children with APD, specific to auditory networks, and shed new light on the neural systems underlying children's listening difficulties.

Evaluation of functional MRI-based human brain parcellation: a review

Brain parcellations play a crucial role in the analysis of brain imaging data sets, as they can significantly affect the outcome of the analysis. In recent years, several novel approaches for constructing MRI-based brain parcellations have been developed with promising results. In the absence of ground truth, several evaluation approaches have been used to evaluate currently available brain parcellations. In this article, we review and critique methods used for evaluating functional brain parcellations constructed using fMRI data sets. We also describe how some of these evaluation methods have been used to estimate the optimal parcellation granularity. We provide a critical discussion of the current approach to the problem of identifying the optimal brain parcellation that is suited for a given neuroimaging study. We argue that the criteria for an optimal brain parcellation must depend on the application the parcellation is intended for. We describe a teleological approach to the evaluation of brain parcellations, where brain parcellations are evaluated in different contexts and optimal brain parcellations for each context are identified separately. We conclude by discussing several directions for further research that would result in improved evaluation strategies.

Individualized Functional Subnetworks Connect Human Striatum and Frontal Cortex

The striatum and cerebral cortex are interconnected via multiple recurrent loops that play a major role in many neuropsychiatric conditions. Primate corticostriatal connections can be precisely mapped using invasive tract-tracing. However, noninvasive human research has not mapped these connections with anatomical precision, limited in part by the practice of averaging neuroimaging data across individuals. Here we utilized highly sampled resting-state functional connectivity MRI for individual-specific precision functional mapping (PFM) of corticostriatal connections. We identified ten individual-specific subnetworks linking cortex-predominately frontal cortex-to striatum, most of which converged with nonhuman primate tract-tracing work. These included separable connections between nucleus accumbens core/shell and orbitofrontal/medial frontal gyrus; between anterior striatum and dorsomedial prefrontal cortex; between dorsal caudate and lateral prefrontal cortex; and between middle/posterior putamen and supplementary motor/primary motor cortex. Two subnetworks that did not converge with nonhuman primates were connected to cortical regions associated with human language function. Thus, precision subnetworks identify detailed, individual-specific, neurobiologically plausible corticostriatal connectivity that includes human-specific language networks.

Causal mapping of human brain function

Mapping human brain function is a long-standing goal of neuroscience that promises to inform the development of new treatments for brain disorders. Early maps of human brain function were based on locations of brain damage or brain stimulation that caused a functional change. Over time, this approach was largely replaced by technologies such as functional neuroimaging, which identify brain regions in which activity is correlated with behaviours or symptoms. Despite their advantages, these technologies reveal correlations, not causation. This creates challenges for interpreting the data generated from these tools and using them to develop treatments for brain disorders. A return to causal mapping of human brain function based on brain lesions and brain stimulation is underway. New approaches can combine these causal sources of information with modern neuroimaging and electrophysiology techniques to gain new insights into the functions of specific brain areas. In this Review, we provide a definition of causality for translational research, propose a continuum along which to assess the relative strength of causal information from human brain mapping studies and discuss recent advances in causal brain mapping and their relevance for developing treatments.

Segregation and integration of the functional connectome in neurodevelopmentally 'at risk' children

Functional connectivity within and between Intrinsic Connectivity Networks (ICNs) transforms over development and is thought to support high order cognitive functions. But how variable is this process, and does it diverge with altered cognitive development? We investigated age-related changes in integration and segregation within and between ICNs in neurodevelopmentally 'at-risk' children, identified by practitioners as experiencing cognitive difficulties in attention, learning, language, or memory. In our analysis we used performance on a battery of 10 cognitive tasks alongside resting-state functional magnetic resonance imaging in 175 at-risk children and 62 comparison children aged 5-16. We observed significant age-by-group interactions in functional connectivity between two network pairs. Integration between the ventral attention and visual networks and segregation of the limbic and fronto-parietal networks increased with age in our comparison sample, relative to at-risk children. Furthermore, functional connectivity between the ventral attention and visual networks in comparison children significantly mediated age-related improvements in executive function, compared to at-risk children. We conclude that integration between ICNs show divergent neurodevelopmental trends in the broad population of children experiencing cognitive difficulties, and that these differences in functional brain organisation may partly explain the pervasive cognitive difficulties within this group over childhood and adolescence.

Browsing Multiple Subjects When the Atlas Adaptation Cannot Be Achieved via a Warping Strategy

Brain mapping studies often need to identify brain structures or functional circuits into a set of individual brains. To this end, multiple atlases have been published to represent such structures based on different modalities, subject sets, and techniques. The mainstream approach to exploit these atlases consists in spatially deforming each individual data onto a given atlas using dense deformation fields, which supposes the existence of a continuous mapping between atlases and individuals. However, this continuity is not always verified, and this "iconic" approach has limits. We present in this study an alternative, complementary, "structural" approach, which consists in extracting structures from the individual data, and comparing them without deformation. A "structural atlas" is thus a collection of annotated individual data with a common structure nomenclature. It may be used to characterize structure shape variability across individuals or species, or to train machine learning systems. This study exhibits Anatomist, a powerful structural 3D visualization software dedicated to building, exploring, and editing structural atlases involving a large number of subjects. It has been developed primarily to decipher the cortical folding variability; cortical sulci vary enormously in both size and shape, and some may be missing or have various topologies, which makes iconic approaches inefficient to study them. We, therefore, had to build structural atlases for cortical sulci, and use them to train sulci identification algorithms. Anatomist can display multiple subject data in multiple views, supports all kinds of neuroimaging data, including compound structural object graphs, handles arbitrary coordinate transformation chains between data, and has multiple display features. It is designed as a programming library in both C++ and Python languages, and may be extended or used to build dedicated custom applications. Its generic design makes all the display and structural aspects used to explore the variability of the cortical folding pattern work in other applications, for instance, to browse axonal fiber bundles, deep nuclei, functional activations, or other kinds of cortical parcellations. Multimodal, multi-individual, or inter-species display is supported, and adaptations to large scale screen walls have been developed. These very original features make it a unique viewer for structural atlas browsing.

Toward personalized medicine in connectomic deep brain stimulation

At the group-level, deep brain stimulation leads to significant therapeutic benefit in a multitude of neurological and neuropsychiatric disorders. At the single-patient level, however, symptoms may sometimes persist despite "optimal" electrode placement at established treatment coordinates. This may be partly explained by limitations of disease-centric strategies that are unable to account for heterogeneous phenotypes and comorbidities observed in clinical practice. Instead, tailoring electrode placement and programming to individual patients' symptom profiles may increase the fraction of top-responding patients. Here, we propose a three-step, circuit-based framework with the aim of developing patient-specific treatment targets that address the unique symptom constellation prevalent in each patient. First, we describe how a symptom network target library could be established by mapping beneficial or undesirable DBS effects to distinct circuits based on (retrospective) group-level data. Second, we suggest ways of matching the resulting symptom networks to circuits defined in the individual patient (template matching). Third, we introduce network blending as a strategy to calculate optimal stimulation targets and parameters by selecting and weighting a set of symptom-specific networks based on the symptom profile and subjective priorities of the individual patient. We integrate the approach with published literature and conclude by discussing limitations and future challenges.

Decoding task specific and task general functional architectures of the brain

Functional magnetic resonance imaging (fMRI) is used to capture complex and dynamic interactions between brain regions while performing tasks. Task related alterations in the brain have been classified as task specific and task general, depending on whether they are particular to a task or common across multiple tasks. Using recent attempts in interpreting deep learning models, we propose an approach to determine both task specific and task general architectures of the functional brain. We demonstrate our methods with a reference‐based decoder on deep learning classifiers trained on 12,500 rest and task fMRI samples from the Human Connectome Project (HCP). The decoded task general and task specific motor and language architectures were validated with findings from previous studies. We found that unlike intersubject variability that is characteristic of functional pathology of neurological diseases, a small set of connections are sufficient to delineate the rest and task states. The nodes and connections in the task general architecture could serve as potential disease biomarkers as alterations in task general brain modulations are known to be implicated in several neuropsychiatric disorders.

Evidence for Schizophrenia-Specific Pathophysiology of Nicotine Dependence

Tobacco use is the top preventable cause of early mortality in schizophrenia. Over 60% of people with schizophrenia smoke, three times the general prevalence. The biological basis of this increased risk is not understood, and existing interventions do not target schizophrenia-specific pathology. We therefore used a connectome-wide analysis to identify schizophrenia-specific circuits of nicotine addiction. We reanalyzed data from two studies: In Cohort 1, 35 smokers (18 schizophrenia, 17 control) underwent resting-state fMRI and clinical characterization. A multivariate pattern analysis of whole-connectome data was used to identify the strongest links between cigarette use and functional connectivity. In Cohort 2, 12 schizophrenia participants and 12 controls were enrolled in a randomized, controlled crossover study of nicotine patch with resting-state fMRI. We correlated change in network functional connectivity with nicotine dose. In Cohort 1, the strongest (p < 0.001) correlate between connectivity and cigarette use was driven by individual variation in default mode network (DMN) topography. In individuals with greater daily cigarette consumption, we observed a pathological expansion of the DMN territory into the identified parieto-occipital region, while in individuals with lower daily cigarette consumption, this region was external to the DMN. This effect was entirely driven by schizophrenia participants. Given the relationship between DMN topography and nicotine use we observed in Cohort 1, we sought to directly test the impact of nicotine on this network using an independent second cohort. In Cohort 2, nicotine reduced DMN connectivity in a dose-dependent manner (R = -0.50; 95% CI -0.75 to -0.12, p < 0.05). In the placebo condition, schizophrenia subjects had hyperconnectivity compared to controls (p < 0.05). Nicotine administration normalized DMN hyperconnectivity in schizophrenia. We here provide direct evidence that the biological basis of nicotine dependence is different in schizophrenia and in non-schizophrenia populations. Our results suggest the high prevalence of nicotine use in schizophrenia may be an attempt to correct a network deficit known to interfere with cognition.

The role of neural load effects in predicting individual differences in working memory function

Studies of working memory (WM) function have tended to adopt either a within-subject approach, focusing on effects of load manipulations, or a between-subjects approach, focusing on individual differences. This dichotomy extends to WM neuroimaging studies, with different neural correlates being identified for within- and between-subjects variation in WM. Here, we examined this issue in a systematic fashion, leveraging the large-sample Human Connectome Project dataset, to conduct a well-powered, whole-brain analysis of the N-back WM task. We first demonstrate the advantages of parcellation schemes for dimension reduction, in terms of load-related effect sizes. This parcel-based approach is then utilized to directly compare the relationship between load-related (within-subject) and behavioral individual differences (between-subject) effects through both correlational and predictive analyses. The results suggest a strong linkage of within-subject and between-subject variation, with larger load-effects linked to stronger brain-behavior correlations. In frontoparietal cortex no hemispheric biases were found towards one type of variation, but the Dorsal Attention Network did exhibit greater sensitivity to between over within-subjects variation, whereas in the Somatomotor network, the reverse pattern was observed. Cross-validated predictive modeling capitalizing on this tight relationship between the two effects indicated greater predictive power for load-activated than load-deactivated parcels, while also demonstrating that load-related effect size can serve as an effective guide to feature (i.e., parcel) selection, in maximizing predictive power while maintaining interpretability. Together, the findings demonstrate an important consistency across within- and between-subjects approaches to identifying the neural substrates of WM, which can be effectively harnessed to develop more powerful predictive models.

Stimulating Memory: Reviewing Interventions Using Repetitive Transcranial Magnetic Stimulation to Enhance or Restore Memory Abilities

Human memory systems are imperfect recording devices that are affected by age and disease, but recent findings suggest that the functionality of these systems may be modifiable through interventions using non-invasive brain stimulation such as repetitive transcranial magnetic stimulation (rTMS). The translational potential of these rTMS interventions is clear: memory problems are the most common cognitive complaint associated with healthy aging, while pathological conditions such as Alzheimer's disease are often associated with severe deficits in memory. Therapies to improve memory or treat memory loss could enhance independence while reducing costs for public health systems. Despite this promise, several important factors limit the generalizability and translational potential of rTMS interventions for memory. Heterogeneity of protocol design, rTMS parameters, and outcome measures present significant challenges to interpretation and reproducibility. However, recent advances in cognitive neuroscience, including rTMS approaches and recent insights regarding functional brain networks, may offer methodological tools necessary to design new interventional studies with enhanced experimental rigor, improved reproducibility, and greater likelihood of successful translation to clinical settings. In this review, we first discuss the current state of the literature on memory modulation with rTMS, then offer a commentary on developments in cognitive neuroscience that are relevant to rTMS interventions, and finally close by offering several recommendations for the design of future investigations using rTMS to modulate human memory performance.

Whole-Brain Resting-State Functional Connectivity Patterns Associated With Pediatric Anxiety and Involuntary Attention Capture

BACKGROUND

Pediatric anxiety disorders are linked to dysfunction in multiple functional brain networks, as well as to alterations in the allocation of spatial attention. We used network-level analyses to characterize resting-state functional connectivity (rs-fc) alterations associated with 1) symptoms of anxiety and 2) alterations in stimulus-driven attention associated with pediatric anxiety disorders. We hypothesized that anxiety was related to altered connectivity of the frontoparietal, default mode, cingulo-opercular, and ventral attention networks and that anxiety-related connectivity alterations that include the ventral attention network would simultaneously be related to deviations in stimulus-driven attention.

METHODS

A sample of children (n = 61; mean = 10.6 years of age), approximately half of whom met criteria for a current anxiety disorder, completed a clinical assay, an attention task, and rs-fc magnetic resonance imaging scans. Network-level analyses examined whole-brain rs-fc patterns associated with clinician-rated anxiety and with involuntary capture of attention. Post hoc analyses controlled for comorbid symptoms.

RESULTS

Elevated clinician-rated anxiety was associated with altered connectivity within the cingulo-opercular network, as well as between the cingulo-opercular network and the ventral attention, default mode, and visual networks. Connectivity between the ventral attention and cingulo-opercular networks was associated with variation in both anxiety and stimulus-driven attention.

CONCLUSIONS

Pediatric anxiety is related to aberrant connectivity patterns among several networks, most of which include the cingulo-opercular network. These results help clarify the within- and between-network interactions associated with pediatric anxiety and its association with altered attention, suggesting that specific network connections could be targeted to improve specific altered processes associated with anxiety.

Parallel hippocampal-parietal circuits for self- and goal-oriented processing

The hippocampus is critically important for a diverse range of cognitive processes, such as episodic memory, prospective memory, affective processing, and spatial navigation. Using individual-specific precision functional mapping of resting-state functional MRI data, we found the anterior hippocampus (head and body) to be preferentially functionally connected to the default mode network (DMN), as expected. The hippocampal tail, however, was strongly preferentially functionally connected to the parietal memory network (PMN), which supports goal-oriented cognition and stimulus recognition. This anterior-posterior dichotomy of resting-state functional connectivity was well-matched by differences in task deactivations and anatomical segmentations of the hippocampus. Task deactivations were localized to the hippocampal head and body (DMN), relatively sparing the tail (PMN). The functional dichotomization of the hippocampus into anterior DMN-connected and posterior PMN-connected parcels suggests parallel but distinct circuits between the hippocampus and medial parietal cortex for self- versus goal-oriented processing.

Nodal Memberships to Communities of Functional Brain Networks Reveal Functional Flexibility and Individualized Connectome

Human brain network is organized as interconnected communities for supporting cognition and behavior. Despite studies on the nonoverlapping communities of brain network, overlapping community structure and its relationship to brain function remain largely unknown. With this consideration, we employed the Bayesian nonnegative matrix factorization to decompose the functional brain networks constructed from resting-state fMRI data into overlapping communities with interdigitated mapping to functional subnetworks. By examining the heterogeneous nodal membership to communities, we classified nodes into three classes: Most nodes in somatomotor and limbic subnetworks were affiliated with one dominant community and classified as unimodule nodes; most nodes in attention and frontoparietal subnetworks were affiliated with more than two communities and classified as multimodule nodes; and the remaining nodes affiliated with two communities were classified as bimodule nodes. This three-class paradigm was highly reproducible across sessions and subjects. Furthermore, the more likely a node was classified as multimodule node, the more flexible it will be engaged in multiple tasks. Finally, the FC feature vector associated with multimodule nodes could serve as connectome "fingerprinting" to gain high subject discriminability. Together, our findings offer new insights on the flexible spatial overlapping communities that related to task-based functional flexibility and individual connectome "fingerprinting."

Development of Individual Variability in Brain Functional Connectivity and Capability across the Adult Lifespan

Individual variability exists in both brain function and behavioral performance. However, changes in individual variability in brain functional connectivity and capability across adult development and aging have not yet been clearly examined. Based on resting-state functional magnetic resonance imaging data from a large cohort of participants (543 adults, aged 18-88 years), brain functional connectivity was analyzed to characterize the spatial distribution and differences in individual variability across the adult lifespan. Results showed high individual variability in the association cortex over the adult lifespan, whereas individual variability in the primary cortex was comparably lower in the initial stage but increased with age. Individual variability was also negatively correlated with the strength/number of short-, medium-, and long-range functional connections in the brain, with long-range connections playing a more critical role in increasing global individual variability in the aging brain. More importantly, in regard to specific brain regions, individual variability in the motor cortex was significantly correlated with differences in motor capability. Overall, we identified specific patterns of individual variability in brain functional structure during the adult lifespan and demonstrated that functional variability in the brain can reflect behavioral performance. These findings advance our understanding of the underlying principles of the aging brain across the adult lifespan and suggest how to characterize degenerating behavioral capability using imaging biomarkers.

Tracking positive and negative symptom improvement in first-episode schizophrenia treated with risperidone using individual-level functional connectivity

BACKGROUND

To improve the treatment outcomes of patients with schizophrenia, research efforts have focused on identifying brain-based markers of treatment response. Personal characteristics regarding disease-related behaviors likely stem from inter-individual variability in the organization of brain functional systems. This study aimed to track dimension-specific changes in psychotic symptoms following risperidone treatment using individual-level functional connectivity (FC).

METHODS

A reliable cortical parcellation approach that accounts for individual heterogeneity in cortical functional anatomy was used to localize functional regions in a longitudinal cohort, consisting of 42 drug-naive first-episodes schizophrenia (FES) patients at baseline and after 8 weeks of risperidone treatment. FC was calculated in individually specified brain regions and used to predict the baseline severity and improvement of positive and negative symptoms in FES.

RESULTS

Distinct sets of individual-specific FC were separately associated with the positive and negative symptom burden at baseline, which could be used to track the corresponding symptom resolution in FES patients following risperidone treatment. Between-network connections of the fronto-parietal network (FPN) contributed the most to predicting the positive symptom domain. A combination of between-network connections of the default mode network, FPN, and within-network connections of the FPN contributed markedly to the prediction model of negative symptom.

CONCLUSION

This novel study, which accounts for individual brain variation, take a step toward establishing individual-specific theranostic biomarkers in schizophrenia.

Decomposition of individual-specific and individual-shared components from resting-state functional connectivity using a multi-task machine learning method

Resting-state functional connectivity (RSFC) can be used for mapping large-scale human brain networks during rest. There is considerable interest in distinguishing the individual-shared and individual-specific components in RSFC for the better identification of individuals and prediction of behavior. Therefore, we propose a multi-task learning based sparse convex alternating structure optimization (MTL-sCASO) method to decompose RSFC into individual-specific connectivity and individual-shared connectivity. We used synthetic data to validate the efficacy of the MTL-sCASO method. In addition, we verified that individual-specific connectivity achieves higher identification rates than the Pearson correlation (PC) method, and the individual-specific components observed in 886 individuals from the Human Connectome Project (HCP) examined in two sessions over two consecutive days might serve as individual fingerprints. Individual-specific connectivity has low inter-subject similarity (-0.005±0.023), while individual-shared connectivity has high inter-subject similarity (0.822±0.061). We also determined the anatomical locations (region or subsystem) related to individual attributes and common features. We find that individual-specific connectivity exhibits low degree centrality in the sensorimotor processing system but high degree centrality in the control system. Importantly, the individual-specific connectivity estimated by the MTL-sCASO method accurately predicts behavioral scores (improved by 9.4% compared to the PC method) in the cognitive dimension. The decomposition of individual-specific and individual-shared components from RSFC provides a new approach for tracing individual traits and group analysis using functional brain networks.

Intrinsic connectivity reveals functionally distinct cortico-hippocampal networks in the human brain

Episodic memory depends on interactions between the hippocampus and interconnected neocortical regions. Here, using data-driven analyses of resting-state functional magnetic resonance imaging (fMRI) data, we identified the networks that interact with the hippocampus-the default mode network (DMN) and a "medial temporal network" (MTN) that included regions in the medial temporal lobe (MTL) and precuneus. We observed that the MTN plays a critical role in connecting the visual network to the DMN and hippocampus. The DMN could be further divided into 3 subnetworks: a "posterior medial" (PM) subnetwork comprised of posterior cingulate and lateral parietal cortices; an "anterior temporal" (AT) subnetwork comprised of regions in the temporopolar and dorsomedial prefrontal cortex; and a "medial prefrontal" (MP) subnetwork comprised of regions primarily in the medial prefrontal cortex (mPFC). These networks vary in their functional connectivity (FC) along the hippocampal long axis and represent different kinds of information during memory-guided decision-making. Finally, a Neurosynth meta-analysis of fMRI studies suggests new hypotheses regarding the functions of the MTN and DMN subnetworks, providing a framework to guide future research on the neural architecture of episodic memory.

Probabilistic mapping of human functional brain networks identifies regions of high group consensus

Many recent developments surrounding the functional network organization of the human brain have focused on data that have been averaged across groups of individuals. While such group-level approaches have shed considerable light on the brain's large-scale distributed systems, they conceal individual differences in network organization, which recent work has demonstrated to be common and widespread. This individual variability produces noise in group analyses, which may average together regions that are part of different functional systems across participants, limiting interpretability. However, cost and feasibility constraints may limit the possibility for individual-level mapping within studies. Here our goal was to leverage information about individual-level brain organization to probabilistically map common functional systems and identify locations of high inter-subject consensus for use in group analyses. We probabilistically mapped 14 functional networks in multiple datasets with relatively high amounts of data. All networks show "core" (high-probability) regions, but differ from one another in the extent of their higher-variability components. These patterns replicate well across four datasets with different participants and scanning parameters. We produced a set of high-probability regions of interest (ROIs) from these probabilistic maps; these and the probabilistic maps are made publicly available, together with a tool for querying the network membership probabilities associated with any given cortical location. These quantitative estimates and public tools may allow researchers to apply information about inter-subject consensus to their own fMRI studies, improving inferences about systems and their functional specializations.

Heritability of individualized cortical network topography

Human cortex is patterned by a complex and interdigitated web of large-scale functional networks. Recent methodological breakthroughs reveal variation in the size, shape, and spatial topography of cortical networks across individuals. While spatial network organization emerges across development, is stable over time, and is predictive of behavior, it is not yet clear to what extent genetic factors underlie interindividual differences in network topography. Here, leveraging a nonlinear multidimensional estimation of heritability, we provide evidence that individual variability in the size and topographic organization of cortical networks are under genetic control. Using twin and family data from the Human Connectome Project (n = 1,023), we find increased variability and reduced heritability in the size of heteromodal association networks (h2 : M = 0.34, SD = 0.070), relative to unimodal sensory/motor cortex (h2 : M = 0.40, SD = 0.097). We then demonstrate that the spatial layout of cortical networks is influenced by genetics, using our multidimensional estimation of heritability (h2-multi; M = 0.14, SD = 0.015). However, topographic heritability did not differ between heteromodal and unimodal networks. Genetic factors had a regionally variable influence on brain organization, such that the heritability of network topography was greatest in prefrontal, precuneus, and posterior parietal cortex. Taken together, these data are consistent with relaxed genetic control of association cortices relative to primary sensory/motor regions and have implications for understanding population-level variability in brain functioning, guiding both individualized prediction and the interpretation of analyses that integrate genetics and neuroimaging.

Functional connectivity hemispheric contrast (FC-HC): A new metric for language mapping

Development of a task-free method for presurgical mapping of language function is important for use in young or cognitively impaired patients. Resting state connectivity fMRI (RS-fMRI) is a task-free method that may be used to identify cognitive networks. We developed a voxelwise RS-fMRI metric, Functional Connectivity Hemispheric Contrast (FC-HC), to map the language network and determine language laterality through comparison of within-hemispheric language network connections (Integration) to cross-hemispheric connections (Segregation). For the first time, we demonstrated robustness and efficacy of a RS-fMRI metric to map language networks across five groups (total N = 243) that differed in MRI scanning parameters, fMRI scanning protocols, age, and development (typical vs pediatric epilepsy). The resting state FC-HC maps for the healthy pediatric and adult groups showed higher values in the left hemisphere, and had high agreement with standard task language fMRI; in contrast, the epilepsy patient group map was bilateral. FC-HC has strong but not perfect agreement with task fMRI and thus, may reflect related and complementary information about language plasticity and compensation.

Personalized connectivity-guided DLPFC-TMS for depression: Advancing computational feasibility, precision and reproducibility

Repetitive transcranial magnetic stimulation (rTMS) of the dorsolateral prefrontal cortex (DLPFC) is an established treatment for refractory depression, however, therapeutic outcomes vary. Mounting evidence suggests that clinical response relates to functional connectivity with the subgenual cingulate cortex (SGC) at the precise DLPFC stimulation site. Critically, SGC-related network architecture shows considerable interindividual variation across the spatial extent of the DLPFC, indicating that connectivity-based target personalization could potentially be necessary to improve treatment outcomes. However, to date accurate personalization has not appeared feasible, with recent work indicating that the intraindividual reproducibility of optimal targets is limited to 3.5 cm. Here we developed reliable and accurate methodologies to compute individualized connectivity-guided stimulation targets. In resting-state functional MRI scans acquired across 1,000 healthy adults, we demonstrate that, using this approach, personalized targets can be reliably and robustly pinpointed, with a median accuracy of ~2 mm between scans repeated across separate days. These targets remained highly stable, even after 1 year, with a median intraindividual distance between coordinates of only 2.7 mm. Interindividual spatial variation in personalized targets exceeded intraindividual variation by a factor of up to 6.85, suggesting that personalized targets did not trivially converge to a group-average site. Moreover, personalized targets were heritable, suggesting that connectivity-guided rTMS personalization is stable over time and under genetic control. This computational framework provides capacity for personalized connectivity-guided TMS targets to be robustly computed with high precision and has the flexibly to advance research in other basic research and clinical applications.

Moving beyond the mean: Subgroups and dimensions of brain activity and cognitive performance across domains

Human neuroimaging during cognitive tasks has provided unique and important insights into the neurobiology of cognition. However, the vast majority of research relies on group aggregate or average statistical maps of activity, which do not fully capture the rich intersubject variability in brain function. In order to fully understand the neurobiology of cognitive processes, it is necessary to explore the range of variability in activation patterns across individuals. To better characterize individual variability, hierarchical clustering was performed separately on six fMRI tasks in 822 participants from the Human Connectome Project. Across all tasks, clusters ranged from a predominantly 'deactivating' pattern towards a more 'activating' pattern of brain activity, with significant differences in out-of-scanner cognitive test scores between clusters. Cluster stability was assessed via a resampling approach; a cluster probability matrix was generated, as the probability of any pair of participants clustering together when both were present in a random subsample. Rather than forming distinct clusters, participants fell along a spectrum or into pseudo-clusters without clear boundaries. A principal components analysis of the cluster probability matrix revealed three components explaining over 90% of the variance in clustering. Plotting participants in this lower-dimensional 'similarity space' revealed manifolds of variations along an S 'snake' shaped spectrum or a folded circle or 'tortilla' shape. The 'snake' shape was present in tasks where individual variability related to activity along covarying networks, while the 'tortilla' shape represented multiple networks which varied independently.

Large-Scale Functional Brain Network Architecture Changes Associated With Trauma-Related Dissociation

OBJECTIVE

Dissociative experiences commonly occur in response to trauma, and while their presence strongly affects treatment approaches in posttraumatic spectrum disorders, their etiology remains poorly understood and their phenomenology incompletely characterized. Methods to reliably assess the severity of dissociation symptoms, without relying solely on self-report, would have tremendous clinical utility. Brain-based measures have the potential to augment symptom reports, although it remains unclear whether brain-based measures of dissociation are sufficiently sensitive and robust to enable individual-level estimation of dissociation severity based on brain function. The authors sought to test the robustness and sensitivity of a brain-based measure of dissociation severity.

METHODS

An intrinsic network connectivity analysis was applied to functional MRI scans obtained from 65 women with histories of childhood abuse and current posttraumatic stress disorder (PTSD). The authors tested for continuous measures of trauma-related dissociation using the Multidimensional Inventory of Dissociation. Connectivity estimates were derived with a novel machine learning technique using individually defined homologous functional regions for each participant.

RESULTS

The models achieved moderate ability to estimate dissociation, after controlling for childhood trauma and PTSD severity. Connections that contributed the most to the estimation mainly involved the default mode and frontoparietal control networks. By contrast, all models performed at chance levels when using a conventional group-based network parcellation.

CONCLUSIONS

Trauma-related dissociative symptoms, distinct from PTSD and childhood trauma, can be estimated on the basis of network connectivity. Furthermore, between-network brain connectivity may provide an unbiased estimate of symptom severity, paving the way for more objective, clinically useful biomarkers of dissociation and advancing our understanding of its neural mechanisms.

Neurophysiological Effects of Whole Coffee Cherry Extract in Older Adults with Subjective Cognitive Impairment: A Randomized, Double-Blind, Placebo-Controlled, Cross-Over Pilot Study

Bioactive plant-based compounds have shown promise as protective agents across multiple domains including improvements in neurological and psychological measures. Methodological challenges have limited our understanding of the neurophysiological changes associated with polyphenol-rich supplements such as whole coffee cherry extract (WCCE). In the current study, we (1) compared 100 mg of WCCE to a placebo using an acute, randomized, double-blind, within-subject, cross-over design, and we (2) conducted a phytochemical analysis of WCCE. The primary objective of the study was to determine the neurophysiological and behavioral changes that resulted from the acute administration of WCCE. We hypothesized that WCCE would increase brain-derived neurotrophic factor (BDNF) and glutamate levels while also increasing neurofunctional measures in cognitive brain regions. Furthermore, we expected there to be increased behavioral performance associated with WCCE, as measured by reaction time and accuracy. Participants underwent four neuroimaging scans (pre- and post-WCCE and placebo) to assess neurofunctional/metabolic outcomes using functional magnetic resonance imaging and magnetic resonance spectroscopy. The results suggest that polyphenol-rich WCCE is associated with decreased reaction time and may protect against cognitive errors on tasks of working memory and response inhibition. Behavioral findings were concomitant with neurofunctional changes in structures involved in decision-making and attention. Specifically, we found increased functional connectivity between the anterior cingulate and regions involved in sensory and decision-making networks. Additionally, we observed increased BDNF and an increased glutamate/gamma-aminobutyric acid (GABA) ratio following WCCE administration. These results suggest that WCCE is associated with acute neurophysiological changes supportive of faster reaction times and increased, sustained attention.

Network variants are similar between task and rest states

Recent work has demonstrated that individual-specific variations in functional networks (termed “network variants”) can be identified in individuals using resting state functional magnetic resonance imaging (fMRI). These network variants exhibit reliability over time, suggesting that they may be trait-like markers of individual differences in brain organization. However, while networks variants are reliable at rest, is is still untested whether they are stable between task and rest states. Here, we use precision data from the Midnight Scan Club (MSC) to demonstrate that (1) task data can be used to identify network variants reliably, (2) these network variants show substantial spatial overlap with those observed in rest, although state-specific effects are present, (3) network variants assign to similar canonical functional networks in task and rest states, and (4) single tasks or a combination of multiple tasks produce similar network variants to rest. Together, these findings further reinforce the trait-like nature of network variants and demonstrate the utility of using task data to define network variants.

Rapid Precision Functional Mapping of Individuals Using Multi-Echo fMRI

Resting-state functional magnetic resonance imaging (fMRI) is widely used in cognitive and clinical neuroscience, but long-duration scans are currently needed to reliably characterize individual differences in functional connectivity (FC) and brain network topology. In this report, we demonstrate that multi-echo fMRI can improve the reliability of FC-based measurements. In four densely sampled individual humans, just 10 min of multi-echo data yielded better test-retest reliability than 30 min of single-echo data in independent datasets. This effect is pronounced in clinically important brain regions, including the subgenual cingulate, basal ganglia, and cerebellum, and is linked to three biophysical signal mechanisms (thermal noise, regional variability in the rate of T2∗ decay, and S0-dependent artifacts) with spatially distinct influences. Together, these findings establish the potential utility of multi-echo fMRI for rapid precision mapping using experimentally and clinically tractable scan times and will facilitate longitudinal neuroimaging of clinical populations.

Connectivity-based localization of human hypothalamic nuclei in functional images of standard voxel size

Despite their critical roles in autonomic functions, individual hypothalamic nuclei have not been extensively investigated in humans using functional magnetic resonance imaging, partly due to the difficulty in resolving individual nuclei contained in the small structure of the hypothalamus. Areal parcellation analyses enable discrimination of individual hypothalamic nuclei but require a higher spatial resolution, which necessitates long scanning time or large amounts of data to compensate for the low signal-to-noise ratio in 3T or 1.5T scanners. In this study, we present analytic procedures to estimate likely locations of individual nuclei in the standard 2-mm resolution based on our higher resolution dataset. The spatial profiles of functional connectivity with the cerebral cortex for each nucleus in the medial hypothalamus were calculated using our higher resolution dataset. Voxels in the hypothalamus in standard resolution images from the Human Connectome Project (HCP) database that predominantly shared connectivity profiles with the same nucleus were subsequently identified. Voxels representing individual nuclei, as identified with the analytic procedures, were reproducible across 20 HCP datasets of 20 subjects each. Furthermore, the identified voxels were spatially separate. These results suggest that these analytic procedures are capable of refining voxels that represent individual hypothalamic nuclei in standard resolution. Our results highlight the potential utility of these procedures in various settings such as patient studies, where lengthy scans are infeasible.

Precision Inhibitory Stimulation of Individual-Specific Cortical Hubs Disrupts Information Processing in Humans

Noninvasive brain stimulation (NIBS) is a promising treatment for psychiatric and neurologic conditions, but outcomes are variable across treated individuals. In principle, precise targeting of individual-specific features of functional brain networks could improve the efficacy of NIBS interventions. Network theory predicts that the role of a node in a network can be inferred from its connections; as such, we hypothesized that targeting individual-specific "hub" brain areas with NIBS should impact cognition more than nonhub brain areas. Here, we first demonstrate that the spatial positioning of hubs is variable across individuals but reproducible within individuals upon repeated imaging. We then tested our hypothesis in healthy individuals using a prospective, within-subject, double-blind design. Inhibition of a hub with continuous theta burst stimulation disrupted information processing during working-memory more than inhibition of a nonhub area, despite targets being separated by only a few centimeters on the right middle frontal gyrus of each subject. Based upon these findings, we conclude that individual-specific brain network features are functionally relevant and could leveraged as stimulation sites in future NIBS interventions.

Predictive modeling of neurobehavioral state and trait variation across development

A key goal of human neurodevelopmental research is to map neural and behavioral trajectories across both health and disease. A growing number of developmental consortia have begun to address this gap by providing open access to cross-sectional and longitudinal 'big data' repositories. However, it remains challenging to develop models that enable prediction of both within-subject and between-subject neurodevelopmental variation. Here, we present a conceptual and analytical perspective of two essential ingredients for mapping neurodevelopmental trajectories: state and trait components of variance. We focus on mapping variation across a range of neural and behavioral measurements and consider concurrent alterations of state and trait variation across development. We present a quantitative framework for combining both state- and trait-specific sources of neurobehavioral variation across development. Specifically, we argue that non-linear mixed growth models that leverage state and trait components of variance and consider environmental factors are necessary to comprehensively map brain-behavior relationships. We discuss this framework in the context of mapping language neurodevelopmental changes in early childhood, with an emphasis on measures of functional connectivity and their reliability for establishing robust neurobehavioral relationships. The ultimate goal is to statistically unravel developmental trajectories of neurobehavioral relationships that involve a combination of individual differences and age-related changes.

Real-Time Resting-State Functional Magnetic Resonance Imaging Using Averaged Sliding Windows with Partial Correlations and Regression of Confounding Signals

Background/Introduction: There is considerable interest in using real-time functional magnetic resonance imaging (fMRI) for monitoring functional connectivity dynamics. To date, the majority of real-time resting-state fMRI studies have examined a limited number of brain regions. This is, in part, due to the computational demands of traditional seed- and independent component analysis-based methods, in particular when using increasingly available high-speed fMRI methods. Methods: This study describes a computationally efficient, real-time, seed-based, resting-state fMRI analysis pipeline using moving averaged sliding-windows (ASW) with partial correlations and regression of motion parameters and signals from white matter and cerebrospinal fluid. Results: Analytical and numerical analyses of ASW correlation and sliding-window regression as a function of window width show selectable bandpass filter characteristics and effective suppression of artifactual correlations resulting from signal drifts and transients. The analysis pipeline is compatible with multislab echo-volumar imaging and simultaneous multislice echo-planar imaging with repetition times as short as 136 msec. High-speed, resting-state fMRI data in healthy controls demonstrate the effectiveness of this approach for minimizing artifactual correlations in white and gray matter, which was comparable to conventional regression across the entire scan. Integrating sliding-window averaging (width: W₁) within a second-level sliding-window (width: W₂) enabled monitoring of intra- and internetwork correlation dynamics of up to 12 resting-state networks with bandpass filter characteristics determined by the first-level sliding-window and temporal resolution W₁ + W₂. Conclusions: The computational performance and confound tolerance make this seed-based, resting-state fMRI approach suitable for real-time monitoring of data quality and resting-state connectivity dynamics in neuroscience and clinical research studies. Impact statement Using averaged sliding-windows for seed-based correlation and regression of confounding signals provides a powerful model-free approach to increase tolerance to artifactual signal transients in resting-state analysis. The algorithmic efficiency of this sliding-window approach enables real-time, seed-based, resting-state functional magnetic resonance imaging (fMRI) of multiple networks with computation of connectivity matrices and online monitoring of data quality. Integration of a second-level sliding-window enables mapping of resting-state connectivity dynamics. Sensitivity and tolerance to confounding signals compare favorably with conventional correlation and confound regression across the entire scan. This methodological advance has the potential to enhance the clinical utility of resting-state fMRI and facilitate neuroscience applications.

Modelling subject variability in the spatial and temporal characteristics of functional modes

Recent work has highlighted the scale and ubiquity of subject variability in observations from functional MRI data (fMRI). Furthermore, it is highly likely that errors in the estimation of either the spatial presentation of, or the coupling between, functional regions can confound cross-subject analyses, making accurate and unbiased representations of functional data essential for interpreting any downstream analyses. Here, we extend the framework of probabilistic functional modes (PFMs) (Harrison et al., 2015) to capture cross-subject variability not only in the mode spatial maps, but also in the functional coupling between modes and in mode amplitudes. A new implementation of the inference now also allows for the analysis of modern, large-scale data sets, and the combined inference and analysis package, PROFUMO, is available from git.fmrib.ox.ac.uk/samh/profumo. A new implementation of the inference now also allows for the analysis of modern, large-scale data sets. Using simulated data, resting-state data from 1000 subjects collected as part of the Human Connectome Project (Van Essen et al., 2013), and an analysis of 14 subjects in a variety of continuous task-states (Kieliba et al., 2019), we demonstrate how PFMs are able to capture, within a single model, a rich description of how the spatio-temporal structure of resting-state fMRI activity varies across subjects. We also compare the new PFM model to the well established independent component analysis with dual regression (ICA-DR) pipeline. This reveals that, under PFM assumptions, much more of the (behaviorally relevant) cross-subject variability in fMRI activity should be attributed to the variability in spatial maps, and that, after accounting for this, functional coupling between modes primarily reflects current cognitive state. This has fundamental implications for the interpretation of cross-sectional studies of functional connectivity that do not capture cross-subject variability to the same extent as PFMs.

Personalizing neuromodulation

In the era of "big data", we are gaining rich person-specific information about neuroanatomy, neural function, and cognitive functions. However, the optimal ways to create precise approaches to optimize individuals' mental functions in health and disease are unclear. Multimodal analysis and modeling approaches can guide neuromodulation by combining anatomical networks, functional signal analysis, and cognitive neuroscience paradigms in single subjects. Our progress could be improved by progressing from statistical fits to mechanistic models. Using transcranial magnetic stimulation as an example, we discuss how integrating methods with a focus on mechanisms could improve our predictions TMS effects within individuals, refine our models of health and disease, and improve our treatments.

Functional Connectivity Signatures of Parkinson's Disease

 Resting-state functional magnetic resonance imaging (RS-fMRI) studies have been extensively applied to analyze the pathophysiology of neurodegenerative disorders such as Parkinson’s disease (PD). In the present narrative review, we attempt to summarize the most recent RS-fMRI findings highlighting the role of brain networks re-organization and adaptation in the course of PD. We also discuss limitations and potential definition of early functional connectivity signatures to track and predict future PD progression. Understanding the neural correlates and potential predisposing factors of clinical progression and complication will be crucial to guide novel clinical trials and to foster preventive strategies.

Default-mode network streams for coupling to language and control systems

The human brain is organized into large-scale networks identifiable using resting-state functional connectivity (RSFC). These functional networks correspond with broad cognitive domains; for example, the Default-mode network (DMN) is engaged during internally oriented cognition. However, functional networks may contain hierarchical substructures corresponding with more specific cognitive functions. Here, we used individual-specific precision RSFC to test whether network substructures could be identified in 10 healthy human brains. Across all subjects and networks, individualized network subdivisions were more valid-more internally homogeneous and better matching spatial patterns of task activation-than canonical networks. These measures of validity were maximized at a hierarchical scale that contained ∼83 subnetworks across the brain. At this scale, nine DMN subnetworks exhibited topographical similarity across subjects, suggesting that this approach identifies homologous neurobiological circuits across individuals. Some DMN subnetworks matched known features of brain organization corresponding with cognitive functions. Other subnetworks represented separate streams by which DMN couples with other canonical large-scale networks, including language and control networks. Together, this work provides a detailed organizational framework for studying the DMN in individual humans.

Individual Variation in Functional Brain Network Topography is Linked to Schizophrenia Symptomatology

Resting-state fMRI (rsfMRI) demonstrates that the brain is organized into distributed networks. Numerous studies have examined links between psychiatric symptomatology and network functional connectivity. Traditional rsfMRI analyses assume that the spatial organization of networks is invariant between individuals. This dogma has recently been overturned by the demonstration that networks show significant variation between individuals. We tested the hypothesis that previously observed relationships between schizophrenia-negative symptom severity and network connectivity are actually due to individual differences in network spatial organization. Forty-four participants diagnosed with schizophrenia underwent rsfMRI scans and clinical assessments. A multivariate pattern analysis determined how whole-brain functional connectivity correlates with negative symptom severity at the individual voxel level. Brain connectivity to a region of the right dorsolateral prefrontal cortex correlates with negative symptom severity. This finding results from individual differences in the topographic distribution of 2 networks: the default mode network (DMN) and the task-positive network (TPN). Both networks demonstrate strong (r = ~0.49) and significant (P < .001) relationships between topography and symptom severity. For individuals with low symptom severity, this critical region is part of the DMN. In highly symptomatic individuals, this region is part of the TPN. Previously overlooked individual variation in brain organization is tightly linked to differences in schizophrenia symptom severity. Recognizing critical links between network topography and pathological symptomology may identify key circuits that underlie cognitive and behavioral phenotypes. Individual variation in network topography likely guides different responses to clinical interventions that rely on anatomical targeting (eg, transcranial magnetic stimulation [TMS]).

Defining Individual-Specific Functional Neuroanatomy for Precision Psychiatry

Studies comparing diverse groups have shown that many psychiatric diseases involve disruptions across distributed large-scale networks of the brain. There is hope that functional magnetic resonance imaging (fMRI) functional connectivity techniques will shed light on these disruptions, providing prognostic and diagnostic biomarkers as well as targets for therapeutic interventions. However, to date, progress on clinical translation of fMRI methods has been limited. Here, we argue that this limited translation is driven by a combination of intersubject heterogeneity and the relatively low reliability of standard fMRI techniques at the individual level. We review a potential solution to these limitations: the use of new "precision" fMRI approaches that shift the focus of analysis from groups to single individuals through the use of extended data acquisition strategies. We begin by discussing the potential advantages of fMRI functional connectivity methods for improving our understanding of functional neuroanatomy and disruptions in psychiatric disorders. We then discuss the budding field of precision fMRI and findings garnered from this work. We demonstrate that precision fMRI can improve the reliability of functional connectivity measures, while showing high stability and sensitivity to individual differences. We close by discussing the application of these approaches to clinical settings.

Causes and Consequences of Diagnostic Heterogeneity in Depression: Paths to Discovering Novel Biological Depression Subtypes

Depression is a highly heterogeneous syndrome that bears only modest correlations with its biological substrates, motivating a renewed interest in rethinking our approach to diagnosing depression for research purposes and new efforts to discover subtypes of depression anchored in biology. Here, we review the major causes of diagnostic heterogeneity in depression, with consideration of both clinical symptoms and behaviors (symptomatology and trajectory of depressive episodes) and biology (genetics and sexually dimorphic factors). Next, we discuss the promise of using data-driven strategies to discover novel subtypes of depression based on functional neuroimaging measures, including dimensional, categorical, and hybrid approaches to parsing diagnostic heterogeneity and understanding its biological basis. The merits of using resting-state functional magnetic resonance imaging functional connectivity techniques for subtyping are considered along with a set of technical challenges and potential solutions. We conclude by identifying promising future directions for defining neurobiologically informed depression subtypes and leveraging them in the future for predicting treatment outcomes and informing clinical decision making.

Iterative consensus spectral clustering improves detection of subject and group level brain functional modules

Specialized processing in the brain is performed by multiple groups of brain regions organized as functional modules. Although, in vivo studies of brain functional modules involve multiple functional Magnetic Resonance Imaging (fMRI) scans, the methods used to derive functional modules from functional networks of the brain ignore individual differences in the functional architecture and use incomplete functional connectivity information. To correct this, we propose an Iterative Consensus Spectral Clustering (ICSC) algorithm that detects the most representative modules from individual dense weighted connectivity matrices derived from multiple scans. The ICSC algorithm derives group-level modules from modules of multiple individuals by iteratively minimizing the consensus-cost between the two. We demonstrate that the ICSC algorithm can be used to derive biologically plausible group-level (for multiple subjects) and subject-level (for multiple subject scans) brain modules, using resting-state fMRI scans of 589 subjects from the Human Connectome Project. We employed a multipronged strategy to show the validity of the modularizations obtained from the ICSC algorithm. We show a heterogeneous variability in the modular structure across subjects where modules involved in visual and motor processing were highly stable across subjects. Conversely, we found a lower variability across scans of the same subject. The performance of our algorithm was compared with existing functional brain modularization methods and we show that our method detects group-level modules that are more representative of the modules of multiple individuals. Finally, the experiments on synthetic images quantitatively demonstrate that the ICSC algorithm detects group-level and subject-level modules accurately under varied conditions. Therefore, besides identifying functional modules for a population of subjects, the proposed method can be used for applications in personalized neuroscience. The ICSC implementation is available at https://github.com/SCSE-Biomedical-Computing-Group/ICSC.

Individual Variation in Functional Topography of Association Networks in Youth

The spatial distribution of large-scale functional networks on the cerebral cortex differs between individuals and is particularly variable in association networks that are responsible for higher-order cognition. However, it remains unknown how this functional topography evolves in development and supports cognition. Capitalizing on advances in machine learning and a large sample imaged with 27 min of high-quality functional MRI (fMRI) data (n = 693, ages 8-23 years), we delineate how functional topography evolves during youth. We found that the functional topography of association networks is refined with age, allowing accurate prediction of unseen individuals' brain maturity. The cortical representation of association networks predicts individual differences in executive function. Finally, variability of functional topography is associated with fundamental properties of brain organization, including evolutionary expansion, cortical myelination, and cerebral blood flow. Our results emphasize the importance of considering the plasticity and diversity of functional neuroanatomy during development and suggest advances in personalized therapeutics.

Individualized Connectome-Targeted Transcranial Magnetic Stimulation for Neuropsychiatric Sequelae of Repetitive Traumatic Brain Injury in a Retired NFL Player

OBJECTIVE

The recent advent of individualized resting-state network mapping (RSNM) has revealed substantial interindividual variability in anatomical localization of brain networks identified by using resting-state functional MRI (rsfMRI). RSNM enables personalized targeting of focal neuromodulation techniques such as repetitive transcranial magnetic stimulation (rTMS). rTMS is believed to exert antidepressant efficacy by modulating connectivity between the stimulation site, the default mode network (DMN), and the subgenual anterior cingulate cortex (sgACC). Personalized rTMS may be particularly useful after repetitive traumatic brain injury (TBI), which is associated with neurodegenerative tauopathy in medial temporal limbic structures. These degenerative changes are believed to be related to treatment-resistant neurobehavioral disturbances observed in many retired athletes.

METHODS

The authors describe a case in which RSNM was successfully used to target rTMS to treat these neuropsychiatric disturbances in a retired NFL defensive lineman whose symptoms were not responsive to conventional treatments. RSNM was used to identify left-right dorsolateral prefrontal rTMS targets with maximal difference between dorsal attention network and DMN correlations. These targets were spatially distinct from those identified by prior methods. Twenty sessions of left-sided excitatory and right-sided inhibitory rTMS were administered at these targets.

RESULTS

Treatment led to improvement in Montgomery-Åsberg Depression Rating Scale (72%), cognitive testing, and headache scales scores. Compared with healthy individuals and subjects with TBI-associated depression, baseline rsfMRI revealed substantially elevated DMN connectivity with the medial temporal lobe (MTL). Serial rsfMRI scans revealed gradual improvement in MTL-DMN connectivity and stimulation site connectivity with sgACC.

CONCLUSIONS

These results highlight the possibility of individualized neuromodulation and biomarker-based monitoring for neuropsychiatric sequelae of repetitive TBI.

Organization of Propagated Intrinsic Brain Activity in Individual Humans

Spontaneous infra-slow (<0.1 Hz) fluctuations in functional magnetic resonance imaging (fMRI) signals are temporally correlated within large-scale functional brain networks, motivating their use for mapping systems-level brain organization. However, recent electrophysiological and hemodynamic evidence suggest state-dependent propagation of infra-slow fluctuations, implying a functional role for ongoing infra-slow activity. Crucially, the study of infra-slow temporal lag structure has thus far been limited to large groups, as analyzing propagation delays requires extensive data averaging to overcome sampling variability. Here, we use resting-state fMRI data from 11 extensively-sampled individuals to characterize lag structure at the individual level. In addition to stable individual-specific features, we find spatiotemporal topographies in each subject similar to the group average. Notably, we find a set of early regions that are common to all individuals, are preferentially positioned proximal to multiple functional networks, and overlap with brain regions known to respond to diverse behavioral tasks-altogether consistent with a hypothesized ability to broadly influence cortical excitability. Our findings suggest that, like correlation structure, temporal lag structure is a fundamental organizational property of resting-state infra-slow activity.

Integrative and Network-Specific Connectivity of the Basal Ganglia and Thalamus Defined in Individuals

The basal ganglia, thalamus, and cerebral cortex form an interconnected network implicated in many neurological and psychiatric illnesses. A better understanding of cortico-subcortical circuits in individuals will aid in development of personalized treatments. Using precision functional mapping-individual-specific analysis of highly sampled human participants-we investigated individual-specific functional connectivity between subcortical structures and cortical functional networks. This approach revealed distinct subcortical zones of network specificity and multi-network integration. Integration zones were systematic, with convergence of cingulo-opercular control and somatomotor networks in the ventral intermediate thalamus (motor integration zones), dorsal attention and visual networks in the pulvinar, and default mode and multiple control networks in the caudate nucleus. The motor integration zones were present in every individual and correspond to consistently successful sites of deep brain stimulation (DBS; essential tremor). Individually variable subcortical zones correspond to DBS sites with less consistent treatment effects, highlighting the importance of PFM for neurosurgery, neurology, and psychiatry.

A set of functionally-defined brain regions with improved representation of the subcortex and cerebellum

An important aspect of network-based analysis is robust node definition. This issue is critical for functional brain network analyses, as poor node choice can lead to spurious findings and misleading inferences about functional brain organization. Two sets of functional brain nodes from our group are well represented in the literature: (1) 264 volumetric regions of interest (ROIs) reported in Power et al., 2011, and (2) 333 cortical surface parcels reported in Gordon et al., 2016. However, subcortical and cerebellar structures are either incompletely captured or missing from these ROI sets. Therefore, properties of functional network organization involving the subcortex and cerebellum may be underappreciated thus far. Here, we apply a winner-take-all partitioning method to resting-state fMRI data to generate novel functionally-constrained ROIs in the thalamus, basal ganglia, amygdala, hippocampus, and cerebellum. We validate these ROIs in three datasets using several criteria, including agreement with existing literature and anatomical atlases. Further, we demonstrate that combining these ROIs with established cortical ROIs recapitulates and extends previously described functional network organization. This new set of ROIs is made publicly available for general use, including a full list of MNI coordinates and functional network labels.

Neuroimage signature from salient keypoints is highly specific to individuals and shared by close relatives

Neuroimaging studies typically adopt a common feature space for all data, which may obscure aspects of neuroanatomy only observable in subsets of a population, e.g. cortical folding patterns unique to individuals or shared by close relatives. Here, we propose to model individual variability using a distinctive keypoint signature: a set of unique, localized patterns, detected automatically in each image by a generic saliency operator. The similarity of an image pair is then quantified by the proportion of keypoints they share using a novel Jaccard-like measure of set overlap. Experiments demonstrate the keypoint method to be highly efficient and accurate, using a set of 7536 T1-weighted MRIs pooled from four public neuroimaging repositories, including twins, non-twin siblings, and 3334 unique subjects. All same-subject image pairs are identified by a similarity threshold despite confounds including aging and neurodegenerative disease progression. Outliers reveal previously unknown data labeling inconsistencies, demonstrating the usefulness of the keypoint signature as a computational tool for curating large neuroimage datasets.