Estimates of segregation and overlap of functional connectivity networks in the human cerebral cortex

Involvement of the cerebellum in structural connectivity enhancement in episodic migraine

BACKGROUND

The pathophysiology of migraine remains poorly understood, yet a growing number of studies have shown structural connectivity disruptions across large-scale brain networks. Although both structural and functional changes have been found in the cerebellum of migraine patients, the cerebellum has barely been assessed in previous structural connectivity studies of migraine. Our objective is to investigate the structural connectivity of the entire brain, including the cerebellum, in individuals diagnosed with episodic migraine without aura during the interictal phase, compared with healthy controls.

METHODS

To that end, 14 migraine patients and 15 healthy controls were recruited (all female), and diffusion-weighted and T1-weighted MRI data were acquired. The structural connectome was estimated for each participant based on two different whole-brain parcellations, including cortical and subcortical regions as well as the cerebellum. The structural connectivity patterns, as well as global and local graph theory metrics, were compared between patients and controls, for each of the two parcellations, using network-based statistics and a generalized linear model (GLM), respectively. We also compared the number of connectome streamlines within specific white matter tracts using a GLM.

RESULTS

We found increased structural connectivity in migraine patients relative to healthy controls with a distinct involvement of cerebellar regions, using both parcellations. Specifically, the node degree of the posterior lobe of the cerebellum was greater in patients than in controls and patients presented a higher number of streamlines within the anterior limb of the internal capsule. Moreover, the connectomes of patients exhibited greater global efficiency and shorter characteristic path length, which correlated with the age onset of migraine.

CONCLUSIONS

A distinctive pattern of heightened structural connectivity and enhanced global efficiency in migraine patients compared to controls was identified, which distinctively involves the cerebellum. These findings provide evidence for increased integration within structural brain networks in migraine and underscore the significance of the cerebellum in migraine pathophysiology.

Real-Time fMRI Neurofeedback Training of Selective Attention in Older Adults

BACKGROUND

Selective attention declines with age, due to age-related functional changes in dorsal anterior cingulate cortex (dACC). Real-time functional magnetic resonance imaging (rtfMRI) neurofeedback has been used in young adults to train volitional control of brain activity, including in dACC.

METHODS

For the first time, this study used rtfMRI neurofeedback to train 19 young and 27 older adults in volitional up- or down-regulation of bilateral dACC during a selective attention task.

RESULTS

Older participants in the up-regulation condition (experimental group) showed greater reward points and dACC BOLD signal across training sessions, reflective of neurofeedback training success; and faster reaction time and better response accuracy, suggesting behavioral benefits on selective attention. These effects were not observed for older participants in the down-regulation condition (inverse condition control group), supporting specificity of volitional dACC up-regulation training in older adults. These effects were, unexpectedly, also not observed for young participants in the up-regulation condition (age control group), perhaps due to a lack of motivation to continue the training.

CONCLUSIONS

These findings provide promising first evidence of functional plasticity in dACC in late life via rtfMRI neurofeedback up-regulation training, enhancing selective attention, and demonstrate proof of concept of rtfMRI neurofeedback training in cognitive aging.

Large-scale brain connectivity changes following the administration of lysergic acid diethylamide, d-amphetamine, and 3,4-methylenedioxyamphetamine

Psychedelics have recently attracted significant attention for their potential to mitigate symptoms associated with various psychiatric disorders. However, the precise neurobiological mechanisms responsible for these effects remain incompletely understood. A valuable approach to gaining insights into the specific mechanisms of action involves comparing psychedelics with substances that have partially overlapping neurophysiological effects, i.e., modulating the same neurotransmitter systems. Imaging data were obtained from the clinical trial NCT03019822, which explored the acute effects of lysergic acid diethylamide (LSD), d-amphetamine, and 3,4-methylenedioxymethamphetamine (MDMA) in 28 healthy volunteers. The clinical trial employed a double-blind, placebo-controlled, crossover design. Herein, various resting-state connectivity measures were examined, including within-network connectivity (integrity), between-network connectivity (segregation), seed-based connectivity of resting-state networks, and global connectivity. Differences between placebo and the active conditions were assessed using repeated-measures ANOVA, followed by post-hoc pairwise t-tests. Changes in voxel-wise seed-based connectivity were correlated with serotonin 2 A receptor density maps. Compared to placebo, all substances reduced integrity in several networks, indicating both common and unique effects. While LSD uniquely reduced integrity in the default-mode network (DMN), the amphetamines, in contrast to our expectations, reduced integrity in more networks than LSD. However, LSD exhibited more pronounced segregation effects, characterized solely by decreases, in contrast to the amphetamines, which also induced increases. Across all substances, seed-based connectivity mostly increased between networks, with LSD demonstrating more pronounced effects than both amphetamines. Finally, while all substances decreased global connectivity in visual areas, compared to placebo, LSD specifically increased global connectivity in the basal ganglia and thalamus. These findings advance our understanding of the distinctive neurobiological effects of psychedelics, prompting further exploration of their therapeutic potential.

Antagonistic behavior of brain networks mediated by low-frequency oscillations: electrophysiological dynamics during internal-external attention switching

Antagonistic activity of brain networks likely plays a fundamental role in how the brain optimizes its performance by efficient allocation of computational resources. A prominent example involves externally/internally oriented attention tasks, implicating two anticorrelated, intrinsic brain networks: the default mode network (DMN) and the dorsal attention network (DAN). To elucidate electrophysiological underpinnings and causal interplay during attention switching, we recorded intracranial EEG (iEEG) from 25 epilepsy patients with electrode contacts localized in the DMN and DAN. We show antagonistic network dynamics of activation-related changes in high-frequency (> 50 Hz) and low-frequency (< 30 Hz) power. The temporal profile of information flow between the networks estimated by functional connectivity suggests that the activated network inhibits the other one, gating its activity by increasing the amplitude of the low-frequency oscillations. Insights about inter-network communication may have profound implications for various brain disorders in which these dynamics are compromised.

Cortical and subcortical substrates of working memory in the right hemisphere: A connectome-based lesion-symptom mapping study

Working Memory (WM) is a cognitive system whose crucial role is to temporarily hold and manipulate information. Early studies suggest that verbal WM is typically associated with left hemisphere (LH) brain regions, while the processing of visuospatial information in WM more specifically depends on the right hemisphere (RH). However, recent evidence suggests a more complex network involving both hemispheres' prefrontal and posterior parietal cortices in these processes. Unfortunately, previous lesion studies often examined only one modality (either verbal, or visuospatial) or one hemisphere, which limits the possible conclusions regarding non-lateralized hemispheric involvement. Using connectome-based lesion-symptom mapping on a large sample of patients with left (LBD) and right (RBD) focal brain damage, we examined whether gray matter damage and white matter disconnections predict deficits of WM updating in an N-back task. Patients were examined with two WM tasks that differed regarding modality (verbal, spatial) and cognitive load (1-back, 2-back). Behavioral outcomes indicated that RBD patients showed significant deficits in WM updating, regardless of task modality or load. This observation was supported by whole-brain voxel-based analysis, revealing associations between WM deficits and gray matter clusters in the RH. Specifically, damage to the right lateral frontal cortex including the brain region homologous to Broca's area was associated with verbal WM deficits, while damage to the right inferior parietal lobe and posterior temporal cortex predicted spatial WM deficits. Additionally, white matter analyses identified severely impacted tracts in the RH, predicting deficits in both verbal and spatial WM. Our findings suggest that the mental manipulation of both verbal and visuospatial information in WM updating relies on the integrity of the RH, irrespective of the specific type of information held in mind.

Functional network modules overlap and are linked to interindividual connectome differences during human brain development

The modular structure of functional connectomes in the human brain undergoes substantial reorganization during development. However, previous studies have implicitly assumed that each region participates in one single module, ignoring the potential spatial overlap between modules. How the overlapping functional modules develop and whether this development is related to gray and white matter features remain unknown. Using longitudinal multimodal structural, functional, and diffusion MRI data from 305 children (aged 6 to 14 years), we investigated the maturation of overlapping modules of functional networks and further revealed their structural associations. An edge-centric network model was used to identify the overlapping modules, and the nodal overlap in module affiliations was quantified using the entropy measure. We showed a regionally heterogeneous spatial topography of the overlapping extent of brain nodes in module affiliations in children, with higher entropy (i.e., more module involvement) in the ventral attention, somatomotor, and subcortical regions and lower entropy (i.e., less module involvement) in the visual and default-mode regions. The overlapping modules developed in a linear, spatially dissociable manner, with decreased entropy (i.e., decreased module involvement) in the dorsomedial prefrontal cortex, ventral prefrontal cortex, and putamen and increased entropy (i.e., increased module involvement) in the parietal lobules and lateral prefrontal cortex. The overlapping modular patterns captured individual brain maturity as characterized by chronological age and were predicted by integrating gray matter morphology and white matter microstructural properties. Our findings highlight the maturation of overlapping functional modules and their structural substrates, thereby advancing our understanding of the principles of connectome development.

Treatment outcomes in tinnitus patients are associated with brain functional network: Evidence from connectome gradient and gene expression analysis

The neuroimaging mechanisms underlying differences in the outcomes of sound therapy for tinnitus patients remain unclear. We hypothesize that abnormal hierarchical architecture is the neuro-biomarker for treatment outcome explanation. We conducted functional connectome gradient analyses on resting-state functional MRI images that acquired before intervention to investigate differences among the patients with effective treatment (ET, n = 27), ineffective treatment (IT, n = 41), and healthy controls (HC, n = 59). General linear models were used to analyze the associations between intergroup differential regions and clinical characteristics. Partial least squares regression was employed to reveal correlations with gene expression. Compared to HC, both ET and IT groups displayed significant differences in the default mode network. Moreover, the ET group exhibited wider gradient range and greater gradient variance. Also, the gradient scores of the differential regions between the ET and HC groups were significantly correlated with Self-rating Anxiety Scale and Self-rating Depression Scale scores, and exhibited positive correlations with the transcriptional profiles of genes related to depression and anxiety. Our results indicated that the abnormalities of ET group, may be more relevant to psychiatric disorders, bringing a higher possible therapeutic potential due to the plasticity of the nervous system. Connectome gradient dysfunction with genetic evidence may serve as an indicator for identifying diverse treatment outcomes of the sound therapy for tinnitus patients before treatment.

Abnormal Dynamic Reconstruction of Overlapping Communities in Schizophrenia Patients

OBJECTIVE

This study aims to explore the changes in dynamic overlapping communities in the brains of schizophrenia (SZ) patients and further investigate the dynamic restructuring patterns of overlapping communities in SZ patients.

MATERIALS AND METHODS

A total of 43 SZ patients and 49 normal controls (NC) were selected for resting-state functional MRI (rs-fMRI) scans. Dynamic functional connectivity analysis was conducted separately on SZ patients and NC using rs-fMRI and Jackknife Correlation techniques to construct dynamic brain network models. Based on these models, a dynamic overlapping community detection method was utilized to explore the abnormal overlapping community structure in SZ patients using evaluation metrics such as the structural stability of overlapping communities, nodes' functional diversity, and activity level of overlapping communities.

RESULTS

The stability of communities in SZ patients showed a decreasing trend. The changes in the overlapping community structure of SZ patients may be related to a decrease in the diversity of overlapping node functions. Additionally, compared to the NC group, the activity level of overlapping communities of SZ patients was significantly reduced.

CONCLUSION

The structure or organization of the brain functional network in SZ patients is abnormal or disrupted, and the activity of the brain network in information processing and transmission is weakened in SZ patients.

Edge-Community Entropy Is a Novel Neural Correlate of Aging and Moderator of Fluid Cognition

Decreased neuronal specificity of the brain in response to cognitive demands (i.e., neural dedifferentiation) has been implicated in age-related cognitive decline. Investigations into functional connectivity analogs of these processes have focused primarily on measuring segregation of nonoverlapping networks at rest. Here, we used an edge-centric network approach to derive entropy, a measure of specialization, from spatially overlapping communities during cognitive task fMRI. Using Human Connectome Project Lifespan data (713 participants, 36-100 years old, 55.7% female), we characterized a pattern of nodal despecialization differentially affecting the medial temporal lobe and limbic, visual, and subcortical systems. At the whole-brain level, global entropy moderated declines in fluid cognition across the lifespan and uniquely covaried with age when controlling for the network segregation metric modularity. Importantly, relationships between both metrics (entropy and modularity) and fluid cognition were age dependent, although entropy's relationship with cognition was specific to older adults. These results suggest entropy is a potentially important metric for examining how neurological processes in aging affect functional specialization at the nodal, network, and whole-brain level.

A new model for dynamic mapping of effective connectivity in task fMRI

Whole-brain dynamic functional connectivity is a growing area in neuroimaging research, encompassing data-driven methods for investigating how large-scale brain networks dynamically reorganize during resting states. However, this approach has been rarely applied to functional magnetic resonance imaging (fMRI) data acquired during task performance. In this study, we first combined the psychophysiological interactions (PPI) and sliding-window methods to analyze dynamic effective connectivity of fMRI data obtained from subjects performing the N-back task within the Human Connectome Project dataset. We then proposed a hypothetical model called Condition Activated Specific Trajectory (CAST) to represent a series of spatiotemporal synchronous changes in significantly activated connections across time windows, which we refer to as a trajectory. Our finding demonstrate that the CAST model outperforms other models in terms of intra-group consistency of individual spatial pattern of PPI connectivity, overall representational ability of temporal variability and hierarchy for individual task performance and cognitive traits. This dynamic view afforded by the CAST model reflects the intrinsic nature of coherent brain activities.

Updating functional brain units: Insights far beyond Luria

This paper reviews Luria's model of the three functional units of the brain. To meet this objective, several issues were reviewed: the theory of functional systems and the contributions of phylogenesis and embryogenesis to the brain's functional organization. This review revealed several facts. In the first place, the relationship/integration of basic homeostatic needs with complex forms of behavior. Secondly, the multi-scale hierarchical and distributed organization of the brain and interactions between cells and systems. Thirdly, the phylogenetic role of exaptation, especially in basal ganglia and cerebellum expansion. Finally, the tripartite embryogenetic organization of the brain: rhinic, limbic/paralimbic, and supralimbic zones. Obviously, these principles of brain organization are in contradiction with attempts to establish separate functional brain units. The proposed new model is made up of two large integrated complexes: a primordial-limbic complex (Luria's Unit I) and a telencephalic-cortical complex (Luria's Units II and III). As a result, five functional units were delineated: Unit I. Primordial or preferential (brainstem), for life-support, behavioral modulation, and waking regulation; Unit II. Limbic and paralimbic systems, for emotions and hedonic evaluation (danger and relevance detection and contribution to reward/motivational processing) and the creation of cognitive maps (contextual memory, navigation, and generativity [imagination]); Unit III. Telencephalic-cortical, for sensorimotor and cognitive processing (gnosis, praxis, language, calculation, etc.), semantic and episodic (contextual) memory processing, and multimodal conscious agency; Unit IV. Basal ganglia systems, for behavior selection and reinforcement (reward-oriented behavior); Unit V. Cerebellar systems, for the prediction/anticipation (orthometric supervision) of the outcome of an action. The proposed brain units are nothing more than abstractions within the brain's simultaneous and distributed physiological processes. As function transcends anatomy, the model necessarily involves transition and overlap between structures. Beyond the classic approaches, this review includes information on recent systemic perspectives on functional brain organization. The limitations of this review are discussed.

How do tasks impact the reliability of fMRI functional connectivity?

While there is growing interest in the use of functional magnetic resonance imaging-functional connectivity (fMRI-FC) for biomarker research, low measurement reliability of conventional acquisitions may limit applications. Factors known to impact FC reliability include scan length, head motion, signal properties, such as temporal signal-to-noise ratio (tSNR), and the acquisition state or task. As tasks impact signal in a region-wise fashion, they likely impact FC reliability differently across the brain, making task an important decision in study design. Here, we use the densely sampled Midnight Scan Club (MSC) dataset, comprising 5 h of rest and 6 h of task fMRI data in 10 healthy adults, to investigate regional effects of tasks on FC reliability. We further considered how BOLD signal properties contributing to tSNR, that is, temporal mean signal (tMean) and temporal standard deviation (tSD), vary across the brain, associate with FC reliability, and are modulated by tasks. We found that, relative to rest, tasks enhanced FC reliability and increased tSD for specific task-engaged regions. However, FC signal variability and reliability is broadly dampened during tasks outside task-engaged regions. From our analyses, we observed signal variability was the strongest driver of FC reliability. Overall, our findings suggest that the choice of task can have an important impact on reliability and should be considered in relation to maximizing reliability in networks of interest as part of study design.

Multimodal measures of spontaneous brain activity reveal both common and divergent patterns of cortical functional organization

Large-scale functional networks have been characterized in both rodent and human brains, typically by analyzing fMRI-BOLD signals. However, the relationship between fMRI-BOLD and underlying neural activity is complex and incompletely understood, which poses challenges to interpreting network organization obtained using this technique. Additionally, most work has assumed a disjoint functional network organization (i.e., brain regions belong to one and only one network). Here, we employ wide-field Ca2+ imaging simultaneously with fMRI-BOLD in mice expressing GCaMP6f in excitatory neurons. We determine cortical networks discovered by each modality using a mixed-membership algorithm to test the hypothesis that functional networks exhibit overlapping organization. We find that there is considerable network overlap (both modalities) in addition to disjoint organization. Our results show that multiple BOLD networks are detected via Ca2+ signals, and networks determined by low-frequency Ca2+ signals are only modestly more similar to BOLD networks. In addition, the principal gradient of functional connectivity is nearly identical for BOLD and Ca2+ signals. Despite similarities, important differences are also detected across modalities, such as in measures of functional connectivity strength and diversity. In conclusion, Ca2+ imaging uncovers overlapping functional cortical organization in the mouse that reflects several, but not all, properties observed with fMRI-BOLD signals.

Abnormal Spatial and Temporal Overlap of Time-Varying Brain Functional Networks in Patients with Schizophrenia

Schizophrenia (SZ) is a complex psychiatric disorder with unclear etiology and pathological features. Neuroscientists are increasingly proposing that schizophrenia is an abnormality in the dynamic organization of brain networks. Previous studies have found that the dynamic brain networks of people with SZ are abnormal in both space and time. However, little is known about the interactions and overlaps between hubs of the brain underlying spatiotemporal dynamics. In this study, we aimed to investigate different patterns of spatial and temporal overlap of hubs between SZ patients and healthy individuals. Specifically, we obtained resting-state functional magnetic resonance imaging data from the public dataset for 43 SZ patients and 49 healthy individuals. We derived a representation of time-varying functional connectivity using the Jackknife Correlation (JC) method. We employed the Betweenness Centrality (BC) method to identify the hubs of the brain's functional connectivity network. We then applied measures of temporal overlap, spatial overlap, and hierarchical clustering to investigate differences in the organization of brain hubs between SZ patients and healthy controls. Our findings suggest significant differences between SZ patients and healthy controls at the whole-brain and subnetwork levels. Furthermore, spatial overlap and hierarchical clustering analysis showed that quasi-periodic patterns were disrupted in SZ patients. Analyses of temporal overlap revealed abnormal pairwise engagement preferences in the hubs of SZ patients. These results provide new insights into the dynamic characteristics of the network organization of the SZ brain.

Cocaine Use Prediction With Tensor-Based Machine Learning on Multimodal MRI Connectome Data

This letter considers the use of machine learning algorithms for predicting cocaine use based on magnetic resonance imaging (MRI) connectomic data. The study used functional MRI (fMRI) and diffusion MRI (dMRI) data collected from 275 individuals, which was then parcellated into 246 regions of interest (ROIs) using the Brainnetome atlas. After data preprocessing, the data sets were transformed into tensor form. We developed a tensor-based unsupervised machine learning algorithm to reduce the size of the data tensor from 275 (individuals) × 2 (fMRI and dMRI) × 246 (ROIs) × 246 (ROIs) to 275 (individuals) × 2 (fMRI and dMRI) × 6 (clusters) × 6 (clusters). This was achieved by applying the high-order Lloyd algorithm to group the ROI data into six clusters. Features were extracted from the reduced tensor and combined with demographic features (age, gender, race, and HIV status). The resulting data set was used to train a Catboost model using subsampling and nested cross-validation techniques, which achieved a prediction accuracy of 0.857 for identifying cocaine users. The model was also compared with other models, and the feature importance of the model was presented. Overall, this study highlights the potential for using tensor-based machine learning algorithms to predict cocaine use based on MRI connectomic data and presents a promising approach for identifying individuals at risk of substance abuse.

Alteration of the large-scale white-matter functional networks in autism spectrum disorder

Autism spectrum disorder is a neurodevelopmental disorder whose core deficit is social dysfunction. Previous studies have indicated that structural changes in white matter are associated with autism spectrum disorder. However, few studies have explored the alteration of the large-scale white-matter functional networks in autism spectrum disorder. Here, we identified ten white-matter functional networks on resting-state functional magnetic resonance imaging data using the K-means clustering algorithm. Compared with the white matter and white-matter functional network connectivity of the healthy controls group, we found significantly decreased white matter and white-matter functional network connectivity mainly located within the Occipital network, Middle temporo-frontal network, and Deep network in autism spectrum disorder. Compared with healthy controls, findings from white-matter gray-matter functional network connectivity showed the decreased white-matter gray-matter functional network connectivity mainly distributing in the Occipital network and Deep network. Moreover, we compared the spontaneous activity of white-matter functional networks between the two groups. We found that the spontaneous activity of Middle temporo-frontal and Deep network was significantly decreased in autism spectrum disorder. Finally, the correlation analysis showed that the white matter and white-matter functional network connectivity between the Middle temporo-frontal network and others networks and the spontaneous activity of the Deep network were significantly correlated with the Social Responsiveness Scale scores of autism spectrum disorder. Together, our findings indicate that changes in the white-matter functional networks are associated behavioral deficits in autism spectrum disorder.

Limbic network co-localization predicts pharmacoresistance in dysplasia-related epilepsy

To evaluate the role of focal cortical dysplasia co-localization to cortical functional networks in the development of pharmacoresistance. One hundred thirty-six focal cortical dysplasia patients with 3.0 T or 1.5 T MRI were identified from clinical databases at Children's National Hospital. Clinico-radio-pathologic factors and network co-localization were determined. Using binomial logistic regression, limbic network co-localization (odds ratio 4.164 95% confidence interval 1.02-17.08, p = 0.048), and focal to bilateral tonic-clonic seizures (4.82, 1.30-18.03, p = 0.019) predicted pharmacoresistance. These findings provide clinicians with markers to identify patients with focal cortical dysplasia-related epilepsy at high risk of developing pharmacoresistance and should facilitate earlier epilepsy surgical evaluation.

Living on the edge: network neuroscience beyond nodes

Network neuroscience has emphasized the connectional properties of neural elements - cells, populations, and regions. This has come at the expense of the anatomical and functional connections that link these elements to one another. A new perspective - namely one that emphasizes 'edges' - may prove fruitful in addressing outstanding questions in network neuroscience. We highlight one recently proposed 'edge-centric' method and review its current applications, merits, and limitations. We also seek to establish conceptual and mathematical links between this method and previously proposed approaches in the network science and neuroimaging literature. We conclude by presenting several avenues for future work to extend and refine existing edge-centric analysis.

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.

Resting-State Functional Connectivity Explained Psychotic-like Experiences in the General Population and Partially Generalized to Patients and Relatives

Background

Psychotic-like experiences (PLEs) are considered the subclinical portion of the psychosis continuum. Research suggests that there are resting-state functional connectivity (rsFC) substrates of PLEs, yet it is unclear if the same substrates underlie more severe psychosis. Here, to our knowledge, we report the first study to build a cross-validated rsFC model of PLEs in a large community sample and directly test its ability to explain psychosis in an independent sample of patients with psychosis and their relatives.

Methods

Resting-state FC of 855 healthy young adults from the WU-Minn Human Connectome Project (HCP) was used to predict PLEs with elastic net. An rsFC composite score based on the resulting model was correlated with psychotic traits and symptoms in 118 patients with psychosis, 71 nonpsychotic first-degree relatives, and 45 healthy control subjects from the psychosis HCP.

Results

In the HCP, the cross-validated model explained 3.3% of variance in PLEs. Predictive connections spread primarily across the default, frontoparietal, cingulo-opercular, and dorsal attention networks. The model partially generalized to a younger, but not older, subsample in the psychosis HCP, explaining two measures of positive/disorganized psychotic traits (the Structured Interview for Schizotypy: β = 0.25, pone-tailed = .027; the Schizotypy Personality Questionnaire positive factor: β = 0.14, pone-tailed = .041). However, it did not differentiate patients from relatives and control subjects or explain psychotic symptoms in patients.

Conclusions

Some rsFC substrates of PLEs are shared across the psychosis continuum. However, explanatory power was modest, and generalization was partial. It is equally important to understand shared versus distinct rsFC variances across the psychosis continuum.

Functional connectomic profile correlates with effective anterior thalamic stimulation for refractory epilepsy

BACKGROUND

Deep brain stimulation of the anterior nucleus of the thalamus (ANT-DBS) is an effective treatment for refractory epilepsy; however, seizure outcome varies among individuals. Identifying a reliable noninvasive biomarker to predict good responders would be helpful.

OBJECTIVES

To test whether the functional connectivity between the ANT-DBS sites and the seizure foci correlates with effective seizure control in refractory epilepsy.

METHODS

We performed a proof-of-concept pilot study of patients with focal refractory epilepsy receiving ANT-DBS. Using normative human connectome data derived from 1000 healthy participants, we investigated whether intrinsic functional connectivity between the seizure foci and the DBS site was associated with seizure outcome. We repeated this analysis controlling for the extent of seizure foci, distance between the seizure foci and DBS site, and using functional connectivity of the ANT instead of the DBS site to test the contribution of variance in DBS sites.

RESULTS

Eighteen patients with two or more seizure foci were included. Greater functional connectivity between the seizure foci and the DBS site correlated with more favorable outcome. The degree of functional connectivity accounted for significant variance in clinical outcomes (DBS site: |r| = 0.773, p < 0.001 vs ANT-atlas: |r| = 0.715, p = 0.001), which remained significant when controlling for the extent of the seizure foci (|r| = 0.773, p < 0.001) and the distance between the seizure foci and DBS site (|r| = 0.777, p < 0.001). Significant correlations were independent of variance in the DBS sites (|r| = 0.148, p = 0.57).

CONCLUSION

These findings suggest that functional connectomic profile is a potential reliable non-invasive biomarker to predict ANT-DBS outcomes. Accordingly, the identification of ANT responders could decrease the surgical risk for patients who may not benefit and optimize the cost-effective allocation of health care resources.

Different macaque brain network remodeling after spinal cord injury and NT3 treatment

Graph theory-based analysis describes the brain as a complex network. Only a few studies have examined modular composition and functional connectivity (FC) between modules in patients with spinal cord injury (SCI). Little is known about the longitudinal changes in hubs and topological properties at the modular level after SCI and treatment. We analyzed differences in FC and nodal metrics reflecting modular interaction to investigate brain reorganization after SCI-induced compensation and neurotrophin-3 (NT3)-chitosan-induced regeneration. Mean inter-modular FC and participation coefficient of areas related to motor coordination were significantly higher in the treatment animals than in the SCI-only ones at the late stage. The magnocellular part of the red nucleus may reflect the best difference in brain reorganization after SCI and therapy. Treatment can enhance information flows between regions and promote the integration of motor functions to return to normal. These findings may reveal the information processing of disrupted network modules.

Scalable Orthonormal Projective NMF via Diversified Stochastic Optimization

The increasing availability of large-scale neuroimaging initiatives opens exciting opportunities for discovery science of human brain structure and function. Data-driven techniques, such as Orthonormal Projective Non-negative Matrix Factorization (opNMF), are well positioned to explore multivariate relationships in big data towards uncovering brain organization. opNMF enjoys advantageous interpretability and reproducibility compared to commonly used matrix factorization methods like Principal Component Analysis (PCA) and Independent Component Analysis (ICA), which led to its wide adoption in clinical computational neuroscience. However, applying opNMF in large-scale cohort studies is hindered by its limited scalability caused by its accompanying computational complexity. In this work, we address the computational challenges of opNMF using a stochastic optimization approach that learns over mini-batches of the data. Additionally, we diversify the stochastic batches via repulsive point processes, which reduce redundancy in the mini-batches and in turn lead to lower variance in the updates. We validated our framework on gray matter tissue density maps estimated from 1000 subjects part of the Open Access Series of Imaging (OASIS) dataset. We demonstrated that operations over mini-batches of data yield significant reduction in computational cost. Importantly, we showed that our novel optimization does not compromise the accuracy or interpretability of factors when compared to standard opNMF. The proposed model enables new investigations of brain structure using big neuroimaging data that could improve our understanding of brain structure in health and disease.

Multimodal measures of spontaneous brain activity reveal both common and divergent patterns of cortical functional organization

Large-scale functional networks have been characterized in both rodent and human brains, typically by analyzing fMRI-BOLD signals. However, the relationship between fMRI-BOLD and underlying neural activity is complex and incompletely understood, which poses challenges to interpreting network organization obtained using this technique. Additionally, most work has assumed a disjoint functional network organization (i.e., brain regions belong to one and only one network). Here, we employed wide-field Ca2+ imaging simultaneously with fMRI-BOLD in mice expressing GCaMP6f in excitatory neurons. We determined cortical networks discovered by each modality using a mixed-membership algorithm to test the hypothesis that functional networks are overlapping rather than disjoint. Our results show that multiple BOLD networks are detected via Ca2+ signals; there is considerable network overlap (both modalities); networks determined by low-frequency Ca2+ signals are only modestly more similar to BOLD networks; and, despite similarities, important differences are detected across modalities (e.g., brain region "network diversity"). In conclusion, Ca2+ imaging uncovered overlapping functional cortical organization in the mouse that reflected several, but not all, properties observed with fMRI-BOLD signals.

A task-general connectivity model reveals variation in convergence of cortical inputs to functional regions of the cerebellum

While resting-state fMRI studies have provided a broad picture of the connectivity between human neocortex and cerebellum, the degree of convergence of cortical inputs onto cerebellar circuits remains unknown. Does each cerebellar region receive input from a single cortical area or convergent inputs from multiple cortical areas? Here, we use task-based fMRI data to build a range of cortico-cerebellar connectivity models, each allowing for a different degree of convergence. We compared these models by their ability to predict cerebellar activity patterns for novel Task Sets. Models that allow some degree of convergence provided the best predictions, arguing for convergence of multiple cortical inputs onto single cerebellar voxels. Importantly, the degree of convergence varied across the cerebellum with the highest convergence observed in areas linked to language, working memory, and social cognition. These findings suggest important differences in the way that functional subdivisions of the cerebellum support motor and cognitive function.

Identification of overlapping and interacting networks reveals intrinsic spatiotemporal organization of the human brain

The human brain is a complex network that exhibits dynamic fluctuations in activity across space and time. Depending on the analysis method, canonical brain networks identified from resting-state fMRI (rs-fMRI) are typically constrained to be either orthogonal or statistically independent in their spatial and/or temporal domains. We avoid imposing these potentially unnatural constraints through the combination of a temporal synchronization process ("BrainSync") and a three-way tensor decomposition method ("NASCAR") to jointly analyze rs-fMRI data from multiple subjects. The resulting set of interacting networks comprises minimally constrained spatiotemporal distributions, each representing one component of functionally coherent activity across the brain. We show that these networks can be clustered into six distinct functional categories and naturally form a representative functional network atlas for a healthy population. This functional network atlas could help explore group and individual differences in neurocognitive function, as we demonstrate in the context of ADHD and IQ prediction.

Computing personalized brain functional networks from fMRI using self-supervised deep learning

A novel self-supervised deep learning (DL) method is developed to compute personalized brain functional networks (FNs) for characterizing brain functional neuroanatomy based on functional MRI (fMRI). Specifically, a DL model of convolutional neural networks with an encoder-decoder architecture is developed to compute personalized FNs directly from fMRI data. The DL model is trained to optimize functional homogeneity of personalized FNs without utilizing any external supervision in an end-to-end fashion. We demonstrate that a DL model trained on fMRI scans from the Human Connectome Project can identify personalized FNs and generalizes well across four different datasets. We further demonstrate that the identified personalized FNs are informative for predicting individual differences in behavior, brain development, and schizophrenia status. Taken together, the self-supervised DL allows for rapid, generalizable computation of personalized FNs.

Intra and inter-individual variability in functional connectomes of patients with First Episode of Psychosis

Patients with Schizophrenia may show different clinical presentations, not only regarding inter-individual comparisons but also in one specific subject over time. In fMRI studies, functional connectomes have been shown to carry valuable individual level information, which can be associated with cognitive and behavioral variables. Moreover, functional connectomes have been used to identify subjects within a group, as if they were fingerprints. For the particular case of Schizophrenia, it has been shown that there is reduced connectome stability as well as higher inter-individual variability. Here, we studied inter and intra-individual heterogeneity by exploring functional connectomes' variability and related it with clinical variables (PANSS Total scores and antipsychotic's doses). Our sample consisted of 30 patients with First Episode of Psychosis and 32 Healthy Controls, with a test-retest approach of two resting-state fMRI scanning sessions. In our patients' group, we found increased deviation from healthy functional connectomes and increased intragroup inter-subject variability, which was positively correlated to symptoms' levels in six subnetworks (visual, somatomotor, dorsal attention, ventral attention, frontoparietal and DMN). Moreover, changes in symptom severity were positively related to changes in deviation from healthy functional connectomes. Regarding intra-subject variability, we were unable to replicate previous findings of reduced connectome stability (i.e., increased intra-subject variability), but we found a trend suggesting that result. Our findings highlight the relevance of variability characterization in Schizophrenia, and they can be related to evidence of Schizophrenia patients having a noisy functional connectome.

The Entangled Brain

The Entangled Brain (Pessoa, L., 2002. MIT Press) promotes the idea that we need to understand the brain as a complex, entangled system. Why does the complex systems perspective, one that entails emergent properties, matter for brain science? In fact, many neuroscientists consider these ideas a distraction. We discuss three principles of brain organization that inform the question of the interactional complexity of the brain: (1) massive combinatorial anatomical connectivity; (2) highly distributed functional coordination; and (3) networks/circuits as functional units. To motivate the challenges of mapping structure and function, we discuss neural circuits illustrating the high anatomical and functional interactional complexity typical in the brain. We discuss potential avenues for testing for network-level properties, including those relying on distributed computations across multiple regions. We discuss implications for brain science, including the need to characterize decentralized and heterarchical anatomical-functional organization. The view advocated has important implications for causation, too, because traditional accounts of causality provide poor candidates for explanation in interactionally complex systems like the brain given the distributed, mutual, and reciprocal nature of the interactions. Ultimately, to make progress understanding how the brain supports complex mental functions, we need to dissolve boundaries within the brain-those suggested to be associated with perception, cognition, action, emotion, motivation-as well as outside the brain, as we bring down the walls between biology, psychology, mathematics, computer science, philosophy, and so on.

Characteristic cortico-cortical connection profile of human precuneus revealed by probabilistic tractography

It is generally hypothesized that functional connectivity (FC) reflects the underlying structural connectivity (SC). The precuneus is associated with highly integrated cognitive functions. However, our understanding of the structural connections that could underlie them is limited. This study aimed to characterize the cortico-cortical connections by probabilistic tractography. The precuneus corresponds to the five cortical areas (7Am, PCV, 7Pm, 7m, POS2) on the HCP MMP atlas. We first conducted the atlas-based probabilistic tractography. The anterior part (7Am) was strongly connected to the sensorimotor region. The dorsal part (7Am, 7Pm) was highly connected with the adjacent parietal and temporal cortex, while the ventral part (PCV, 7m) showed strong connections with the adjacent posterior cingulate and medial prefrontal cortex. The most posterior part (POS2) was explicitly connected to the visual cortex. In addition, there was a correlation between SC and resting-state fMRI connectivity (Spearman's rank correlation coefficient = 0.322 ± 0.019, p < 0.05 corrected at subject level). Collectively, the current study revealed the characteristic connectional profile of precuneus, which could shed light on the structural heterogeneity for the future functional analyses.

TTLL11 gene is associated with sustained attention performance and brain networks: A genome-wide association study of a healthy Chinese sample

Genetic studies on attention have mainly focused on children with attention-deficit/hyperactivity disorder (ADHD), so little systematic research has been conducted on genetic correlates of attention performance and their potential brain mechanisms among healthy individuals. The current study included a genome-wide association study (GWAS, N = 1145 healthy young adults) aimed to identify genes associated with sustained attention and an imaging genetics study (an independent sample of 483 healthy young adults) to examine any identified genes' influences on brain function. The GWAS found that TTLL11 showed genome-wide significant associations with sustained attention, with rs13298112 as the most significant SNP and the GG homozygotes showing more impulsive but also more focused responses than the A allele carriers. A retrospective examination of previously published ADHD GWAS results confirmed an un-reported, small but statistically significant effect of TTLL11 on ADHD. The imaging genetics study replicated this association and showed that the TTLL11 gene was associated with resting state activity and connectivity of the somatomoter network, and can be predicted by dorsal attention network connectivity. Specifically, the GG homozygotes showed lower brain activity, weaker brain network connectivity, and non-significant brain-attention association compared to the A allele carriers. Expression database showed that expression of this gene is enriched in the brain and that the G allele is associated with lower expression level than the A allele. These results suggest that TTLL11 may play a major role in healthy individuals' attention performance and may also contribute to the etiology of ADHD.

Dual-axes of functional organisation across lateral parietal cortex: the angular gyrus forms part of a multi-modal buffering system

Decades of neuropsychological and neuroimaging evidence have implicated the lateral parietal cortex (LPC) in a myriad of cognitive domains, generating numerous influential theoretical models. However, these theories fail to explain why distinct cognitive activities appear to implicate common neural regions. Here we discuss a unifying model in which the angular gyrus forms part of a wider LPC system with a core underlying neurocomputational function; the multi-sensory buffering of spatio-temporally extended representations. We review the principles derived from computational modelling with neuroimaging task data and functional and structural connectivity measures that underpin the unified neurocomputational framework. We propose that although a variety of cognitive activities might draw on shared underlying machinery, variations in task preference across angular gyrus, and wider LPC, arise from graded changes in the underlying structural connectivity of the region to different input/output information sources. More specifically, we propose two primary axes of organisation: a dorsal-ventral axis and an anterior-posterior axis, with variations in task preference arising from underlying connectivity to different core cognitive networks (e.g. the executive, language, visual, or episodic memory networks).

Test-retest reliability of modular-relevant analysis in brain functional network

Introduction

The human brain could be modeled as a complex network via functional magnetic resonance imaging (fMRI), and the architecture of these brain functional networks can be studied from multiple spatial scales with different graph theory tools. Detecting modules is an important mesoscale network measuring approach that has provided crucial insights for uncovering how brain organizes itself among different functional subsystems. Despite its successful application in a wide range of brain network studies, the lack of comprehensive reliability assessment prevents its potential extension to clinical trials.

Methods

To fill this gap, this paper, using resting-state test-retest fMRI data, systematically explored the reliabilities of five popular network metrics derived from modular structure. Considering the repeatability of network partition depends heavily on network size and module detection algorithm, we constructed three types of brain functional networks for each subject by using a set of coarse-to-fine brain atlases and adopted four methods for single-subject module detection and twelve methods for group-level module detection.

Results

The results reported moderate-to-good reliability in modularity, intra- and inter-modular functional connectivities, within-modular degree and participation coefficient at both individual and group levels, indicating modular-relevant network metrics can provide robust evaluation results. Further analysis identified the significant influence of module detection algorithm and node definition approach on reliabilities of network partitions and its derived network analysis results.

Discussion

This paper provides important guidance for choosing reliable modular-relevant network metrics and analysis strategies in future studies.

Effect of group-based vs individualized stimulation site selection on reliability of network-targeted TMS

BACKGROUND

Transcranial magnetic stimulation (TMS) is a widely used technique for the noninvasive assessment and manipulation of brain activity and behavior. Although extensively used for research and clinical purposes, recent studies have questioned the reliability of TMS findings because of the high inter-individual variability that has been observed.

OBJECTIVE

In this study, we compared the efficacy and reliability of different targeting scenarios on the TMS-evoked response.

METHODS

24 subjects underwent a single pulse stimulation protocol over two parietal nodes belonging to the Dorsal Attention (DAN) and Default Mode (DMN) Networks respectively. Across visits, the stimulated target for both networks was chosen either based on group-derived networks' maps or personalized network topography based on individual anatomy and functional profile. All stimulation visits were conducted twice, one month apart, during concomitant electroencephalography recording.

RESULTS

At the network level, we did not observe significant differences in the TMS-evoked response between targeting conditions. However, reliable patterns of activity were observed- for both networks tested- following the individualized targeting approach. When the same analyses were carried out at the electrode space level, evidence of reliable patterns was observed following the individualized stimulation of the DAN, but not of the DMN.

CONCLUSIONS

Our findings suggest that individualization of stimulation sites might ensure reliability of the evoked TMS-response across visits. Furthermore, individualized stimulation sites appear to be of foremost importance in highly variable, high order task-positive networks, such as the DAN.

Frequency-dependent white-matter functional network changes associated with cognitive deficits in subcortical vascular cognitive impairment

Vascular cognitive impairment (VCI) refers to all forms of cognitive decline associated with cerebrovascular diseases, in which white matter (WM) is highly vulnerable. Although previous studies have shown that blood oxygen level-dependent (BOLD) signals inside WM can effectively reflect neural activities, whether WM BOLD signal alterations are present and their roles underlying cognitive impairment in VCI remain largely unknown. In this study, 36 subcortical VCI (SVCI) patients and 36 healthy controls were enrolled to evaluate WM dysfunction. Specifically, fourteen distinct WM networks were identified from resting-state functional MRI using K-means clustering analysis. Subsequently, between-network functional connectivity (FC) and within-network BOLD signal amplitude of WM networks were calculated in three frequency bands (band A: 0.01-0.15 Hz, band B: 0.08-0.15 Hz, and band C: 0.01-0.08 Hz). Patients with SVCI manifested decreased FC mainly in bilateral parietal WM regions, forceps major, superior and inferior longitudinal fasciculi. These connections extensively linked with distinct WM networks and with gray-matter networks such as frontoparietal control, dorsal and ventral attention networks, which exhibited frequency-specific alterations in SVCI. Additionally, extensive amplitude reductions were found in SVCI, showing frequency-dependent properties in parietal, anterior corona radiate, pre/post central, superior and inferior longitudinal fasciculus networks. Furthermore, these decreased FC and amplitudes showed significant positive correlations with cognitive performances in SVCI, and high diagnostic performances for SVCI especially combining all bands. Our study indicated that VCI-related cognitive deficits were characterized by frequency-dependent WM functional abnormalities, which offered novel applicable neuromarkers for VCI.

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.

Tasks activating the default mode network map multiple functional systems

Recent developments in network neuroscience suggest reconsidering what we thought we knew about the default mode network (DMN). Although this network has always been seen as unitary and associated with the resting state, a new deconstructive line of research is pointing out that the DMN could be divided into multiple subsystems supporting different functions. By now, it is well known that the DMN is not only deactivated by tasks, but also involved in affective, mnestic, and social paradigms, among others. Nonetheless, it is starting to become clear that the array of activities in which it is involved, might also be extended to more extrinsic functions. The present meta-analytic study is meant to push this boundary a bit further. The BrainMap database was searched for all experimental paradigms activating the DMN, and their activation likelihood estimation maps were then computed. An additional map of task-induced deactivations was also created. A multidimensional scaling indicated that such maps could be arranged along an anatomo-psychological gradient, which goes from midline core activations, associated with the most internal functions, to that of lateral cortices, involved in more external tasks. Further multivariate investigations suggested that such extrinsic mode is especially related to reward, semantic, and emotional functions. However, an important finding was that the various activation maps were often different from the canonical representation of the resting-state DMN, sometimes overlapping with it only in some peripheral nodes, and including external regions such as the insula. Altogether, our findings suggest that the intrinsic-extrinsic opposition may be better understood in the form of a continuous scale, rather than a dichotomy.

Dissociable multi-scale patterns of development in personalized brain networks

The brain is organized into networks at multiple resolutions, or scales, yet studies of functional network development typically focus on a single scale. Here, we derive personalized functional networks across 29 scales in a large sample of youths (n = 693, ages 8-23 years) to identify multi-scale patterns of network re-organization related to neurocognitive development. We found that developmental shifts in inter-network coupling reflect and strengthen a functional hierarchy of cortical organization. Furthermore, we observed that scale-dependent effects were present in lower-order, unimodal networks, but not higher-order, transmodal networks. Finally, we found that network maturation had clear behavioral relevance: the development of coupling in unimodal and transmodal networks are dissociably related to the emergence of executive function. These results suggest that the development of functional brain networks align with and refine a hierarchy linked to cognition.

Abnormal dynamic functional connectivity after sleep deprivation from temporal variability perspective

Sleep deprivation (SD) is very common in modern society and regarded as a potential causal mechanism of several clinical disorders. Previous neuroimaging studies have explored the neural mechanisms of SD using magnetic resonance imaging (MRI) from static (comparing two MRI sessions [one after SD and one after resting wakefulness]) and dynamic (using repeated MRI during one night of SD) perspectives. Recent SD researches have focused on the dynamic functional brain organization during the resting-state scan. Our present study adopted a novel metric (temporal variability), which has been successfully applied to many clinical diseases, to examine the dynamic functional connectivity after SD in 55 normal young subjects. We found that sleep-deprived subjects showed increased regional-level temporal variability in large-scale brain regions, and decreased regional-level temporal variability in several thalamus subregions. After SD, participants exhibited enhanced intra-network temporal variability in the default mode network (DMN) and increased inter-network temporal variability in numerous subnetwork pairs. Furthermore, we found that the inter-network temporal variability between visual network and DMN was negative related with the slowest 10% respond speed (β = -.42, p = 5.57 × 10^-4 ) of the psychomotor vigilance test after SD following the stepwise regression analysis. In conclusion, our findings suggested that sleep-deprived subjects showed abnormal dynamic brain functional configuration, which provides new insights into the neural underpinnings of SD and contributes to our understanding of the pathophysiology of clinical disorders.

Distinct Patterns of Automatic and Controlled Incongruent Information Processing in the Human Brain

It is a fundamental ability to discriminate incongruent information in daily activity. However, the underlying neural dynamics are still unclear. Using stereoelectroencephalography (SEEG), in this study, we investigated the fine-grained and different states of incongruent information processing in patients with refractory epilepsy who underwent intracranial electrode implantation. All patients performed a delayed match-to-sample paradigm in the sequential pairs of visual stimuli (S1 followed by S2). Participants were asked to discriminate whether the relevant feature of S2 was identical to S1 while ignoring the irrelevant feature. The spatiotemporal cortical responses evoked by different conditions were calculated and compared, respectively, in the context of brain intrinsic functional networks. In total, we obtained SEEG recordings from 241 contacts in gray matter. In the processing of irrelevant incongruent information, the activated brain areas included the superior parietal lobule, supramarginal gyrus, angular gyrus, inferior temporal gyrus, and fusiform gyrus. By comparing the relevant incongruent condition with the congruent condition, the activated brain areas included the middle frontal gyrus, superior temporal gyrus, middle temporal gyrus, posterior superior temporal sulcus, and posterior cingulate cortex. We demonstrated the dynamics of incongruent information processing with high spatiotemporal resolution and suggested that the process of automatic detection of irrelevant incongruent information requires the involvement of local regions and relatively few networks. Meanwhile, controlled discrimination of relevant incongruent information requires the participation of extensive regions and a wide range of nodes in the network. Furthermore, both the frontoparietal control network and default mode network were engaged in the incongruent information processing.

Task-specific network interactions across key cognitive domains

Human cognition is organized in distributed networks in the brain. Although distinct specialized networks have been identified for different cognitive functions, previous work also emphasizes the overlap of key cognitive domains in higher level association areas. The majority of previous studies focused on network overlap and dissociation during resting states whereas task-related network interactions across cognitive domains remain largely unexplored. A better understanding of network overlap and dissociation during different cognitive tasks may elucidate flexible (re-)distribution of resources during human cognition. The present study addresses this issue by providing a broad characterization of large-scale network dynamics in three key cognitive domains. Combining prototypical tasks of the larger domains of attention, language, and social cognition with whole-brain multivariate activity and connectivity approaches, we provide a spatiotemporal characterization of multiple large-scale, overlapping networks that differentially interact across cognitive domains. We show that network activity and interactions increase with increased cognitive complexity across domains. Interaction patterns reveal a common core structure across domains as well as dissociable domain-specific network activity. The observed patterns of activation and deactivation of overlapping and strongly coupled networks provide insight beyond region-specific activity within a particular cognitive domain toward a network perspective approach across diverse key cognitive functions.

Comparison of Resting-State Functional MRI Methods for Characterizing Brain Dynamics

Resting-state functional MRI (fMRI) exhibits time-varying patterns of functional connectivity. Several different analysis approaches have been developed for examining these resting-state dynamics including sliding window connectivity (SWC), phase synchrony (PS), co-activation pattern (CAP), and quasi-periodic patterns (QPP). Each of these approaches can be used to generate patterns of activity or inter-areal coordination which vary across time. The individual frames can then be clustered to produce temporal groupings commonly referred to as “brain states.” Several recent publications have investigated brain state alterations in clinical populations, typically using a single method for quantifying frame-wise functional connectivity. This study directly compares the results of k-means clustering in conjunction with three of these resting-state dynamics methods (SWC, CAP, and PS) and quantifies the brain state dynamics across several metrics using high resolution data from the human connectome project. Additionally, these three dynamics methods are compared by examining how the brain state characterizations vary during the repeated sequences of brain states identified by a fourth dynamic analysis method, QPP. The results indicate that the SWC, PS, and CAP methods differ in the clusters and trajectories they produce. A clear illustration of these differences is given by how each one results in a very different clustering profile for the 24s sequences explicitly identified by the QPP algorithm. PS clustering is sensitive to QPPs with the mid-point of most QPP sequences grouped into the same single cluster. CAPs are also highly sensitive to QPPs, separating each phase of the QPP sequences into different sets of clusters. SWC (60s window) is less sensitive to QPPs. While the QPPs are slightly more likely to occur during specific SWC clusters, the SWC clustering does not vary during the 24s QPP sequences, the goal of this work is to improve both the practical and theoretical understanding of different resting-state dynamics methods, thereby enabling investigators to better conceptualize and implement these tools for characterizing functional brain networks.

The overlapping modular organization of human brain functional networks across the adult lifespan

Previous studies have demonstrated that the brain functional modular organization, which is a fundamental feature of the human brain, would change along the adult lifespan. However, these studies assumed that each brain region belonged to a single functional module, although there has been convergent evidence supporting the existence of overlap among functional modules in the human brain. To reveal how age affects the overlapping functional modular organization, this study applied an overlapping module detection algorithm that requires no prior knowledge to the resting-state fMRI data of a healthy cohort (N = 570) aged from 18 to 88 years old. A series of measures were derived to delineate the characteristics of the overlapping modular structure and the set of overlapping nodes (brain regions participating in two or more modules) identified from each participant. Age-related regression analyses on these measures found linearly decreasing trends in the overlapping modularity and the modular similarity. The number of overlapping nodes was found increasing with age, but the increment was not even over the brain. In addition, across the adult lifespan and within each age group, the nodal overlapping probability consistently had positive correlations with both functional gradient and flexibility. Further, by correlation and mediation analyses, we showed that the influence of age on memory-related cognitive performance might be explained by the change in the overlapping functional modular organization. Together, our results revealed age-related decreased segregation from the brain functional overlapping modular organization perspective, which could provide new insight into the adult lifespan changes in brain function and the influence of such changes on cognitive performance.

Deep Brain Stimulation Modulates Multiple Abnormal Resting-State Network Connectivity in Patients With Parkinson’s Disease

Background

Deep brain stimulation (DBS) improves motor and non-motor symptoms in patients with Parkinson’s disease (PD). Researchers mainly investigated the motor networks to reveal DBS mechanisms, with few studies extending to other networks. This study aimed to investigate multi-network modulation patterns using DBS in patients with PD.

Methods

Twenty-four patients with PD underwent 1.5 T functional MRI (fMRI) scans in both DBS-on and DBS-off states, with twenty-seven age-matched healthy controls (HCs). Default mode, sensorimotor, salience, and left and right frontoparietal networks were identified by using the independent component analysis. Power spectra and functional connectivity of these networks were calculated. In addition, multiregional connectivity was established from 15 selected regions extracted from the abovementioned networks. Comparisons were made among groups. Finally, correlation analyses were performed between the connectivity changes and symptom improvements.

Results

Compared with HCs, PD-off showed abnormal power spectra and functional connectivity both within and among these networks. Some of the abovementioned abnormalities could be corrected by DBS, including increasing the power spectra in the sensorimotor network and modulating the parts of the ipsilateral functional connectivity in different regions centered in the frontoparietal network. Moreover, the DBS-induced functional connectivity changes were correlated with motor and depression improvements in patients with PD.

Conclusion

DBS modulated the abnormalities in multi-networks. The functional connectivity alterations were associated with motor and psychiatric improvements in PD. This study lays the foundation for large-scale brain network research on multi-network DBS modulation.

Individual variability in functional organization of the neonatal brain

The adult brain is organized into distinct functional networks, forming the basis of information processing and determining individual differences in behavior. Is this network organization genetically determined and present at birth? And what is the individual variability in this organization in neonates? Here, we use unsupervised learning to uncover intrinsic functional brain organization using resting-state connectivity from a large cohort of neonates (Developing Human Connectome Project). We identified a set of symmetric, hierarchical, and replicable networks: sensorimotor, visual, default mode, ventral attention, and high-level vision. We quantified individual variability across neonates, and found the most individual variability in the ventral attention networks. Crucially, the variability of these networks was not driven by SNR differences or differences from adult networks (Yeo et al., 2011). Finally, differential gene expression provided a potential explanation for the emergence of these distinct networks and identified potential genes of interest for future developmental and individual variability research. Overall, we found neonatal connectomes (even at the voxel-level) can reveal broad individual-specific information processing units. The presence of individual differences in neonates and the framework for personalized parcellations demonstrated here has the potential to improve prediction of behavior and future outcomes from neonatal and infant brain data.

Performance scaling for structural MRI surface parcellations: a machine learning analysis in the ABCD Study

The use of predefined parcellations on surface-based representations of the brain as a method for data reduction is common across neuroimaging studies. In particular, prediction-based studies typically employ parcellation-driven summaries of brain measures as input to predictive algorithms, but the choice of parcellation and its influence on performance is often ignored. Here we employed preprocessed structural magnetic resonance imaging (sMRI) data from the Adolescent Brain Cognitive Development Study® to examine the relationship between 220 parcellations and out-of-sample predictive performance across 45 phenotypic measures in a large sample of 9- to 10-year-old children (N = 9,432). Choice of machine learning (ML) pipeline and use of alternative multiple parcellation-based strategies were also assessed. Relative parcellation performance was dependent on the spatial resolution of the parcellation, with larger number of parcels (up to ~4,000) outperforming coarser parcellations, according to a power-law scaling of between 1/4 and 1/3. Performance was further influenced by the type of parcellation, ML pipeline, and general strategy, with existing literature-based parcellations, a support vector-based pipeline, and ensembling across multiple parcellations, respectively, as the highest performing. These findings highlight the choice of parcellation as an important influence on downstream predictive performance, showing in some cases that switching to a higher resolution parcellation can yield a relatively large boost to performance.

The role of disrupted functional connectivity in aphasia

Language is one of the most complex and specialized higher cognitive processes. Brain damage to the distributed, primarily left-lateralized language network can result in aphasia, a neurologic disorder characterized by receptive and/or expressive deficits in spoken and/or written language. Most often, aphasia is the consequence of stroke-termed poststroke aphasia (PSA)-yet, aphasia can also manifest due to neurodegenerative disease, specifically, a disorder called primary progressive aphasia (PPA). In recent years, functional connectivity neuroimaging studies have provided emerging evidence supporting theories regarding the relationships between language impairments, structural brain damage, and functional network properties in these two disorders. This chapter reviews the current evidence for the "network phenotype of stroke injury" hypothesis (Siegel et al., 2016) as it pertains to PSA and the "network degeneration hypothesis" (Seeley et al., 2009) as it pertains to PPA. Methodologic considerations for functional connectivity studies, limitations of the current functional connectivity literature in aphasia, and future directions are also discussed.

Scan Once, Analyse Many: Using Large Open-Access Neuroimaging Datasets to Understand the Brain

We are now in a time of readily available brain imaging data. Not only are researchers now sharing data more than ever before, but additionally large-scale data collecting initiatives are underway with the vision that many future researchers will use the data for secondary analyses. Here I provide an overview of available datasets and some example use cases. Example use cases include examining individual differences, more robust findings, reproducibility-both in public input data and availability as a replication sample, and methods development. I further discuss a variety of considerations associated with using existing data and the opportunities associated with large datasets. Suggestions for further readings on general neuroimaging and topic-specific discussions are also provided.