Large-scale Functional Networks Research Articles

Large-Scale Functional Networks, Cognition and Brain Structures Supporting Social Cognition and Theory of Mind Performance in Prodromal to Mild Alzheimer's Disease.

Impairment of social cognition (SC) skills such as recognition and attribution of intentions and affective states of others (Theory of Mind, ToM) has been evidenced in Alzheimer’s Disease (AD). This study investigated the neuropsychological, neuroanatomical and brain-functional underpinnings of SC processing to obtain an understanding of the social neurophenotype in early probable AD. Forty-six patients with mild cognitive impairment and mild probable AD underwent SC assessment including emotion recognition (Ekman-60-faces task) and cognitive and affective ToM (Reading-the-Mind-in-the-Eyes test and Story-based Empathy task). Linear models tested the association between SC scores and neuropsychological measures, grey matter maps and large-scale functional networks activity. The executive domain had the most predominant association with SC scores in the cognitive profile. Grey matter volume of the anterior cingulate, orbitofrontal, temporoparietal junction (TPJ), superior temporal, and cerebellar cortices were associated with ToM. Social cognition scores were associated with lower connectivity of the default-mode network with the prefrontal cortex. The right fronto-parietal network displayed higher inter-network connectivity in the right TPJ and insula while the salience network showed lower inter-network connectivity with the left TPJ and insula. Connectivity coupling alterations of executive-attentional networks may support default mode social-cognitive-associated decline through the recruitment of frontal executive mechanisms.

Long-term prognosis and educational determinants of brain network decline in older adult individuals

Older adults with lower education are at greater risk for dementia. It is unclear which brain changes lead to these outcomes. Longitudinal imaging-based measures of brain structure and function were examined in adult individuals (baseline age, 45–86 years; two to five visits per participant over 1–9 years). College degree completion differentiates individual-based and neighborhood-based measures of socioeconomic status and disadvantage. Older adults (~65 years and over) without a college degree exhibit a pattern of declining large-scale functional brain network organization (resting-state system segregation) that is less evident in their college-educated peers. Declining brain system segregation predicts impending changes in dementia severity, measured up to 10 years past the last scan date. The prognostic value of brain network change is independent of Alzheimer’s disease (AD)-related genetic risk (APOE status), the presence of AD-associated pathology (cerebrospinal fluid phosphorylated tau, cortical amyloid) and cortical thinning. These results demonstrate that the trajectory of an individual’s brain network organization varies in relation to their educational attainment and, more broadly, is a unique indicator of individual brain health during older age.

Large-scale functional brain networks of maladaptive childhood aggression identified by connectome-based predictive modeling.

Disruptions in frontoparietal networks supporting emotion regulation have been long implicated in maladaptive childhood aggression. However, the association of connectivity between large-scale functional networks with aggressive behavior has not been tested. The present study examined whether the functional organization of the connectome predicts severity of aggression in children. This cross-sectional study included a transdiagnostic sample of 100 children with aggressive behavior (27 females) and 29 healthy controls without aggression or psychiatric disorders (13 females). Severity of aggression was indexed by the total score on the parent-rated Reactive-Proactive Aggression Questionnaire. During fMRI, participants completed a face emotion perception task of fearful and calm faces. Connectome-based predictive modeling with internal cross-validation was conducted to identify brain networks that predicted aggression severity. The replication and generalizability of the aggression predictive model was then tested in an independent sample of children from the Adolescent Brain Cognitive Development (ABCD) study. Connectivity predictive of aggression was identified within and between networks implicated in cognitive control (medial frontal, frontoparietal), social functioning (default mode, salience), and emotion processing (subcortical, sensorimotor) (r=0.31, RMSE = 9.05, p=0.005). Out-of-sample replication (p<0.002) and generalization (p=0.007) of findings predicting aggression from the functional connectome was demonstrated in an independent sample of children from the ABCD study (n=1,791; n=1,701). Individual differences in large-scale functional networks contribute to variability in maladaptive aggression in children with psychiatric disorders. Linking these individual differences in the connectome to variation in behavioral phenotypes will advance identification of neural biomarkers of maladaptive childhood aggression to inform targeted treatments.

Disrupted Brain Connectivity Networks in Aphasia Revealed by Resting-State fMRI.

Aphasia is characterized by the disability of spontaneous conversation, listening, understanding, retelling, naming, reading, or writing. However, the neural mechanisms of language damage after stroke are still under discussion. This study aimed to investigate the global and nodal characterization of the functional networks in patients with aphasic stroke based on resting-state functional MRI (fMRI). Twenty-four right-handed patients with aphasia after stroke and 19 healthy controls (HC) underwent a 3-TfMRI scan. A whole-brain large-scale functional connectivity network was then constructed based on Power's atlas of 264 functional regions of interest, and the global and nodal topological properties of these networks were analyzed using graph theory approaches. The results showed that patients with aphasia had decreased in small-worldness (sigma), normalized clustering coefficient (gamma), and local efficiency (Eloc) values. Furthermore, Eloc was positively correlated with language ability, retelling, naming, and listening comprehension in patients with aphasia. Patients with aphasia also had decreased nodal degree and decreased nodal efficiency in the left postcentral gyrus, central opercular cortex, and insular cortex. Our results suggest that the global and local topology attributes were altered by injury in patients with aphasic stroke. We argue that the local efficiency of brain networks might be used as a potential indicator of basic speech function in patients with aphasia.

Metabolic and Neural Mechanisms Underlying the Associations Between Gut Bacteroides and Cognition: A Large-Scale Functional Network Connectivity Study

There is a proof-of-concept that microbial metabolites provide a molecular connection between the gut and the brain. Extensive research has established a link between gut Bacteroides and human cognition, yet the metabolic and neural mechanisms underlying this association remain largely unknown. Here, we collected fecal samples, resting-state functional MRI, and cognitive data from a large and homogeneous sample of 157 healthy young adults. 16S rRNA gene sequencing was conducted with abundances of Bacteroides and metabolic pathways quantified by species annotation and functional prediction analyses, respectively. Large-scale intra- and internetwork functional connectivity was measured using independent component analysis. Results showed that gut Bacteroides were related to multiple metabolic pathways, which in turn were associated with widespread functional network connectivity. Furthermore, functional network connectivity mediated the associations between some Bacteroides-related metabolic pathways and cognition. Remarkably, arginine and proline metabolism, phenylalanine metabolism, and biosynthesis of unsaturated fatty acids act as the key metabolic pathways that are most contributive, and the executive control and sensorimotor systems contribute most strongly at the neural level. Our findings suggest complex poly-pathway and poly-network processes linking Bacteroides to cognition, more generally yielding a novel conceptualization of targeting gut Bacteroides as an intervention strategy for individuals with cognitive impairment.

Mind the gaps: functional networks disrupted by white matter hyperintensities are associated with greater falls risk

White matter hyperintensities (WMH) are associated with greater falls risk and slow gait speed. Whether these deficits are caused by the disruption of large-scale functional networks remains inconclusive. Further, physical activity moderates the association between WMHs and falls, but whether this extends to the disruption of functional networks remains unknown. One hundred and sixty-four adults (>55 years old) were included in this study. Using lesion network mapping, we identified significant correlations between the percentage of WMH-related disruption of the dorsal attention network and Physiological Profile Assessment (PPA) score (r = 0.24, p < 0.01); and between disruption of both the sensorimotor (r = 0.23, p < 0.01) and ventral attention networks (r = 0.21, p = 0.01) with foam sway. There were no significant associations with floor sway or gait speed. Physical activity moderated the association between the dorsal attention network and PPA score (p = 0.045). Thus, future research should investigate whether physical activity should be recommended in the clinical management of older adults with cerebral small vessel disease.

Altered interplay among large-scale brain functional networks underpins multi-domain anosognosia in early-AD

Decline in self-awareness (anosognosia) is a prevalent early symptom of Alzheimer's disease (AD). The integrity of the functional hubs of the default mode network (DMN) has been heavily implicated in retained self-awareness abilities in AD patients. Moreover, an early breakdown of this network has been found to be a hallmark feature in the clinical profile of AD. However, the interplay among other large-scale brain networks in support of cognitive awareness in early-AD remains poorly understood.

Lesion covariance networks reveal proposed origins and pathways of diffuse gliomas.

Diffuse gliomas have been hypothesized to originate from neural stem cells in the subventricular zone and develop along previously healthy brain networks. Here, we evaluated these hypotheses by mapping independent sources of glioma localization and determining their relationships with neurogenic niches, genetic markers and large-scale connectivity networks. By applying independent component analysis to lesion data from 242 adult patients with high- and low-grade glioma, we identified three lesion covariance networks, which reflect clusters of frequent glioma localization. Replicability of the lesion covariance networks was assessed in an independent sample of 168 glioma patients. We related the lesion covariance networks to important clinical variables, including tumour grade and patient survival, as well as genomic information such as molecular genetic subtype and bulk transcriptomic profiles. Finally, we systematically cross-correlated the lesion covariance networks with structural and functional connectivity networks derived from neuroimaging data of over 4000 healthy UK BioBank participants to uncover intrinsic brain networks that may that underlie tumour development. The three lesion covariance networks overlapped with the anterior, posterior and inferior horns of the lateral ventricles respectively, extending into the frontal, parietal and temporal cortices. These locations were independently replicated. The first lesion covariance network, which overlapped with the anterior horn, was associated with low-grade, isocitrate dehydrogenase -mutated/1p19q-codeleted tumours, as well as a neural transcriptomic signature and improved overall survival. Each lesion covariance network significantly coincided with multiple structural and functional connectivity networks, with the first bearing an especially strong relationship with brain connectivity, consistent with its neural transcriptomic profile. Finally, we identified subcortical, periventricular structures with functional connectivity patterns to the cortex that significantly matched each lesion covariance network. In conclusion, we demonstrated replicable patterns of glioma localization with clinical relevance and spatial correspondence with large-scale functional and structural connectivity networks. These results are consistent with prior reports of glioma growth along white matter pathways, as well as evidence for the coordination of glioma stem cell proliferation by neuronal activity. Our findings describe how the locations of gliomas relate to their proposed subventricular origins, suggesting a model wherein periventricular brain connectivity guides tumour development.

Aberrant Development of Cross-Frequency Multiplex Functional Connectome in First-Episode, Drug-Naive Major Depressive Disorder and Schizophrenia.

Introduction: Both major depressive disorder (MDD) and schizophrenia (SCH) are characterized by neurodevelopmental abnormalities; however, transdiagnostic and diagnosis-specific patterns of such abnormalities have rarely been examined, particularly in large-scale functional brain networks via advanced multilayer network models. Methods: Here, we collected resting-state functional magnetic resonance imaging data from 45 MDD patients, 64 SCH patients, and 48 healthy controls (HCs; 13-45 years old), and we constructed functional networks in different frequency intervals. The frequency-dependent networks were then fused by multiplex network models, followed by graph-based topological analyses. Results: We found that functional networks of the patients showed common neurodevelopmental abnormalities in the right ventromedial parietooccipital sulcus (opposite correlations with age to HCs), whereas functional networks of the MDD patients exhibited specific alterations in the left superior parietal lobule and right precentral gyrus with respect to cross-frequency interactions. These findings were quite different from those from brain networks within each frequency interval, which revealed SCH-specific neurodevelopmental abnormalities in the right superior temporal gyrus (opposite correlations with age to the other two groups) in 0.027-0.073 Hz, and SCH-specific alterations in the left superior temporal gyrus and bilateral insula in 0.073-0.198 Hz. Finally, multivariate analysis of age prediction revealed that the subcortical network lost prediction ability in both patient groups, whereas the visual network exhibited additional prediction ability in the MDD patients. Discussion and Conclusion: Altogether, these findings demonstrate transdiagnostic and diagnosis-specific neurodevelopmental abnormalities and alterations in large-scale functional brain networks between MDD and SCH, which have important implications for understanding shared and unique neural mechanisms underlying the diseases.

Gait Variability Is Associated With the Strength of Functional Connectivity Between the Default and Dorsal Attention Brain Networks: Evidence From Multiple Cohorts.

In older adults, elevated gait variability when walking has been associated with both cognitive impairment and future falls. This study leveraged 3 existing data sets to determine relationships between gait variability and the strength of functional connectivity within and between large-scale brain networks in healthy older adults, those with mild-to-moderate functional impairment, and those with Parkinson's disease (PD). Gait and resting-state functional magnetic resonance imaging data were extracted from existing data sets on: (i) 12 older adults without overt disease yet with slow gait and mild executive dysfunction; (ii) 12 older adults with intact cognitive-motor function and age- and sex-matched to the first cohort; and (iii) 15 individuals with PD. Gait variability (%, coefficient of variation of stride time) during preferred walking speed was measured and correlated with the degree of functional connectivity within and between 7 established large-scale functional brain networks. Regression models adjusted for age and sex revealed that in each cohort, those with less gait variability exhibited greater negative correlation between fluctuations in resting-state brain activity between the default network and the dorsal attention network (functionally limited older: β = 4.38, p = .027; healthy older: β = 1.66, p = .032; PD: β = 1.65, p = .005). No other within- or between-network connectivity outcomes were consistently related to gait variability across all 3 cohorts. These results provide strong evidence that gait variability is uniquely related to functional connectivity between the default network and the dorsal attention network, and that this relationship may be independent of both functional status and underlying brain disease.

Intrinsic Functional Connectivity of the Anterior Cingulate Cortex Is Associated with Tolerance to Distress.

The ability to adapt under significant adversity, defined as psychological resilience, is instrumental in preventing stress-related disorders. An important aspect of resilience is the capacity to endure affective distress when in pursuit of goals, also known as distress tolerance. Evidence that links intrinsic baseline interactions within large-scale functional networks with performance under distress remains missing. We hypothesized that the anterior cingulate cortex (ACC) may engage in distress tolerance because of its involvement in attention and emotion regulation. Accordingly, we tested whether behavioral performance under distress is associated with baseline resting-state ACC functional connectivity (FC). Distress tolerance was measured in 97 participants using the behavioral indicator of resiliency to distress (BIRD) task. Analyses contrasted participants who quit the task before its designated termination (n = 51) with those who persisted throughout it (n = 46). Seed-based FC analysis indicated greater connectivity between the ACC and dorsolateral prefrontal cortex (DLPFC) in subjects who persisted throughout the task, along with greater FC between the ACC and the precentral gyrus in those who quit before its termination. The results shed light on the mechanisms underlying interindividual differences in the ability to handle distress.

Long-term stability of the cortical volumetric profile and the functional human connectome throughout childhood and adolescence.

There is compelling evidence showing that between-subject variability in several functional and structural brain features is sufficient for unique identification in adults. However, individuation of brain functional connectomes depends on the stabilization of neurodevelopmental processes during childhood and adolescence. Here, we aimed to (1) evaluate the intra-subject functional connectome stability over time for the whole brain and for large scale functional networks and (2) determine the long-term identification accuracy or 'fingerprinting' for the cortical volumetric profile and the functional connectome. For these purposes, we analysed a longitudinal cohort of 239 children and adolescents scanned in two sessions with an interval of approximately 3 years (age range 6-15 years at baseline and 9-18 years at follow-up). Corroborating previous results using short between-scan intervals in children and adolescents, we observed a moderate identification accuracy (38%) for the whole functional profile. In contrast, identification accuracy using cortical volumetric profile was 95%. Among the large-scale networks, the default-mode (26.8%), the frontoparietal (23.4%) and the dorsal-attention (27.6%) networks were the most discriminative. Our results provide further evidence for a protracted development of specific individual structural and functional connectivity profiles.

Between-sex variability of resting\xa0state functional brain networks in amyotrophic lateral sclerosis (ALS)

The organization of brain functional connectivity (FC) has been shown to differ between sexes. Amyotrophic lateral sclerosis (ALS) is characterized by sexual dimorphism, showing sex-specific trends in site of onset, phenotypes, and prognosis. Here, we explored resting state (RS) FC differences within major large-scale functional networks between women and men in a sample of ALS patients, in comparison to healthy controls (HCs). A group-level independent component analysis (ICA) was performed on RS-fMRI time-series enabling spatial and spectral analyses of large-scale RS FC networks in 45 patients with ALS (20 F; 25 M) and 31 HCs (15 F; 16 M) with a focus on sex-related differences. A whole-brain voxel-based morphometry (VBM) was also performed to highlight atrophy differences. Between-sex comparisons showed: decreased FC in the right middle frontal gyrus and in the precuneus within the default mode network (DMN), in affected men compared to affected women; decreased FC in the right post-central gyrus (sensorimotor network), in the right inferior parietal gyrus (right fronto-parietal network) and increased FC in the anterior cingulate cortex and right insula (salience network), in both affected and non-affected men compared to women. When comparing affected men to affected women, VBM analysis revealed atrophy in men in the right lateral occipital cortex. Our results suggest that in ALS sex-related trends of brain functional and structural changes are more heavily represented in DMN and in the occipital cortex, suggesting that sex is an additional dimension of functional and structural heterogeneity in ALS.

Large-scale functional network dynamics in human callosal agenesis: Increased subcortical involvement and preserved laterality

In the human brain, the corpus callosum is the major white-matter commissural tract enabling the transmission of sensory-motor, and higher level cognitive information between homotopic regions of the two cerebral hemispheres. Despite developmental absence (i.e., agenesis) of the corpus callosum (AgCC), functional connectivity is preserved, including interhemispheric connectivity. Subcortical structures have been hypothesised to provide alternative pathways to enable this preservation. To test this hypothesis, we used functional Magnetic Resonance Imaging (fMRI) recordings in children with AgCC and typically developing children, and a time-resolved approach to retrieve temporal characteristics of whole-brain functional networks. We observed an increased engagement of the cerebellum and amygdala/hippocampus networks in children with AgCC compared to typically developing children. There was little evidence that laterality of activation networks was affected in AgCC. Our findings support the hypothesis that subcortical structures play an essential role in the functional reconfiguration of the brain in the absence of a corpus callosum.

Impact of Transcutaneous Auricular Vagus Nerve Stimulation on Large-Scale Functional Brain Networks: From Local to Global.

Transcutaneous auricular vagus nerve stimulation (taVNS) is a novel non-invasive brain stimulation technique considered as a potential supplementary treatment option for a wide range of diseases. Although first promising findings were obtained so far, the exact mode of action of taVNS is not fully understood yet. We recently developed an examination schedule to probe for immediate taVNS-induced modifications of large-scale epileptic brain networks. With this schedule, we observed short-term taVNS to have a topology-modifying, robustness- and stability-enhancing immediate effect on large-scale functional brain networks from subjects with focal epilepsies. We here expand on this study and investigate the impact of short-term taVNS on various local and global characteristics of large-scale evolving functional brain networks from a group of 30 subjects with and without central nervous system diseases. Our findings point to differential, at first glance counterintuitive, taVNS-mediated alterations of local and global topological network characteristics that result in a reconfiguration of networks and a modification of their stability and robustness properties. We propose a model of a stimulation-related stretching and compression of evolving functional brain networks that may help to better understand the mode of action of taVNS.

Brain parcellation selection: An overlooked decision point with meaningful effects on individual differences in resting-state functional connectivity

Over the past decade extensive research has examined the segregation of the human brain into large-scale functional networks. The resulting network maps, i.e. parcellations, are now commonly used for the a priori identification of functional networks. However, the use of these parcellations, particularly in developmental and clinical samples, hinges on four fundamental assumptions: (1) the various parcellations are equally able to recover the networks of interest; (2) adult-derived parcellations well represent the networks in children's brains; (3) network properties, such as within-network connectivity, are reliably measured across parcellations; and (4) parcellation selection does not impact the results with regard to individual differences in given network properties. In the present study we examined these assumptions using eight common parcellation schemes in two independent developmental samples. We found that the parcellations are equally able to capture networks of interest in both children and adults. However, networks bearing the same name across parcellations (e.g., default network) do not produce reliable within-network measures of functional connectivity. Critically, parcellation selection significantly impacted the magnitude of associations of functional connectivity with age, poverty, and cognitive ability, producing meaningful differences in interpretation of individual differences in functional connectivity based on parcellation choice. Our findings suggest that work employing parcellations may benefit from the use of multiple schemes to confirm the robustness and generalizability of results. Furthermore, researchers looking to gain insight into functional networks may benefit from employing more nuanced network identification approaches such as using densely-sampled data to produce individual-derived network parcellations. A transition towards precision neuroscience will provide new avenues in the characterization of functional brain organization across development and within clinical populations.

Directed Brain Connectivity Identifies Widespread Functional Network Abnormalities in Parkinson's Disease.

Parkinson’s disease (PD) is a neurodegenerative disorder characterized by topological abnormalities in large-scale functional brain networks, which are commonly analyzed using undirected correlations in the activation signals between brain regions. This approach assumes simultaneous activation of brain regions, despite previous evidence showing that brain activation entails causality, with signals being typically generated in one region and then propagated to other ones. To address this limitation, here, we developed a new method to assess whole-brain directed functional connectivity in participants with PD and healthy controls using antisymmetric delayed correlations, which capture better this underlying causality. Our results show that whole-brain directed connectivity, computed on functional magnetic resonance imaging data, identifies widespread differences in the functional networks of PD participants compared with controls, in contrast to undirected methods. These differences are characterized by increased global efficiency, clustering, and transitivity combined with lower modularity. Moreover, directed connectivity patterns in the precuneus, thalamus, and cerebellum were associated with motor, executive, and memory deficits in PD participants. Altogether, these findings suggest that directional brain connectivity is more sensitive to functional network differences occurring in PD compared with standard methods, opening new opportunities for brain connectivity analysis and development of new markers to track PD progression.

Cortical Thinning and Abnormal Structural Covariance Network After Three Hours Sleep Restriction

Sleep loss leads to serious health problems, impaired attention, and emotional processing. It has been suggested that the abnormal neurobehavioral performance after sleep deprivation was involved in dysfunction of specific functional connectivity between brain areas. However, to the best of our knowledge, there was no study investigating the structural connectivity mechanisms underlying the dysfunction at network level. Surface morphological analysis and graph theoretical analysis were employed to investigate changes in cortical thickness following 3 h sleep restriction, and test whether the topological properties of structural covariance network was affected by sleep restriction. We found that sleep restriction significantly decreased cortical thickness in the right parieto-occipital cortex (Brodmann area 19). In addition, graph theoretical analysis revealed significantly enhanced global properties of structural covariance network including clustering coefficient and local efficiency, and increased nodal properties of the left insula cortex including nodal efficiency and betweenness, after 3 h sleep restriction. These results provided insights into understanding structural mechanisms of dysfunction of large-scale functional networks after sleep restriction.

Resting state functional networks in 1-to-3-year-old typically developing children

Brain functional networks undergo substantial development and refinement during the first years of life. Yet, the maturational pathways of functional network development remain poorly understood. Using resting-state fMRI data acquired during natural sleep from 24 typically developing toddlers, ages 1.5–3.5 years, we aimed to examine the large-scale resting-state functional networks and their relationship with age and developmental skills. Specifically, two network organization indices reflecting network connectivity and spatial variability were derived. Our results revealed that reduced spatial variability or increased network homogeneity in one of the default mode network components was associated with age, with older children displaying less spatially variable posterior DMN subcomponent, consistent with the notion of increased spatial and functional specialization. Further, greater network homogeneity in higher-order functional networks, including the posterior default mode, salience, and language networks, was associated with more advanced developmental skills measured with a standardized assessment of early learning, regardless of age. These results not only improve our understanding of brain functional network development during toddler years, but also inform the relationship between brain network organization and emerging cognitive and behavioral skills.

Large-scale functional brain networks for consciousness.

The generation and maintenance of consciousness are fundamental but difficult subjects in the fields of psychology, philosophy, neuroscience, and medicine. However, recent developments in neuro-imaging techniques coupled with network analysis have greatly advanced our understanding of consciousness. The present review focuses on large-scale functional brain networks based on neuro-imaging data to explain the awareness (contents) and wakefulness of consciousness. Despite limitations, neuroimaging data suggests brain maps for important psychological and cognitive processes such as attention, language, self-referential, emotion, motivation, social behavior, and wakefulness. We considered a review of these advancements would provide new insights into research on the neural correlates of consciousness.