NIMG-31. PROPOSED ORIGINS AND PATHWAYS OF DIFFUSE GLIOMAS REVEALED BY LESION COVARIANCE NETWORKS

2021 ◽  
Vol 23 (Supplement_6) ◽  
pp. vi135-vi135
Author(s):  
Ayan Mandal ◽  
Rafael Romero-Garcia ◽  
Jakob Seidlitz ◽  
Michael Hart ◽  
Aaron Alexander-Bloch ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Subventricular Zone ◽  
Large Scale ◽  
Brain Connectivity ◽  
Strong Relationship ◽  
Brain Regions ◽  
Low Grade ◽  
Transcriptomic Signature ◽  
Glioma Growth ◽  
Diffuse Gliomas

Abstract 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. Using lesion data from a total of 410 patients with high- and low-grade glioma, we identified – and replicated in an independent sample – three lesion covariance networks (LCNs), which reflect clusters of frequent glioma localization. These three LCNs overlapped with the anterior, posterior, and inferior horns of the lateral ventricles respectively, extending into the frontal, parietal, and temporal cortices. The first LCN, which overlapped with the anterior horn, was associated with low-grade, IDH-mutated/1p19q-codeleted tumors, as well as a neural transcriptomic signature and improved overall survival. Each LCN significantly coincided with multiple structural and functional connectivity networks, with LCN1 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 LCN. These results build upon prior reports of glioma growth along white matter pathways, as well as evidence for the coordination of glioma stem cell proliferation by neuronal activity. Cumulatively, our findings support a model wherein periventricular brain connectivity guides glioma development from the subventricular zone into distributed brain regions.

scholarly journals Lesion covariance networks reveal proposed origins and pathways of diffuse gliomas

2021 ◽  
Author(s):  
Ayan S Mandal ◽  
Rafael Romero-Garcia ◽  
Jakob Seidlitz ◽  
Michael G Hart ◽  
Aaron Alexander-Bloch ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Subventricular Zone ◽  
Large Scale ◽  
Brain Connectivity ◽  
Tumor Development ◽  
Strong Relationship ◽  
Low Grade ◽  
Connectivity Patterns ◽  
Transcriptomic Signature ◽  
Diffuse Gliomas

Diffuse gliomas have been hypothesized to originate from neural stem cells in the subventricular zone. Here, we evaluated this hypothesis by mapping independent sources of glioma localization and determining their relationships with neurogenic niches, genetic markers, and large-scale connectivity networks. Using lesion data from a total of 410 patients with glioma, we identified -- and replicated in an independent sample -- three lesion covariance networks (LCNs), which reflect clusters of frequent glioma co-localization. Each LCN overlapped with a distinct horn of the lateral ventricles. The first LCN, which overlapped with the anterior horn, was associated with low-grade, IDH-mutated/1p19q-codeleted tumors, as well as a neural transcriptomic signature and improved overall survival. Each LCN significantly corresponded with multiple brain networks, with LCN1 bearing an especially strong relationship with structural and functional connectivity, consistent with its neural transcriptomic profile. Finally, we identified subcortical, periventricular structures with functional connectivity patterns to the cortex that significantly matched each LCN. Cumulatively, our findings support a model wherein periventricular brain connectivity guides tumor development.

scholarly journals Topographic Shifts in Functional Connectivity and Reduced Lateralization in 16p11.2 Deletion Carriers: A Genetics-First Approach to Understanding Autism

2020 ◽  
Author(s):  
ABID Y QURESHI ◽  
Jared A. Nielsen ◽  
Jorge Sepulcre
Keyword(s):  
Functional Connectivity ◽  
Large Scale ◽  
Brain Connectivity ◽  
Brain Regions ◽  
Language Ability ◽  
Hemispheric Lateralization ◽  
Functional Coupling ◽  
Resting State Functional Connectivity ◽  
Hierarchical Processing ◽  
Large Scale Networks

Abstract Background: The study of autism has been confounded by genetic and etiologic heterogeneity. The current study utilized a genetics-first approach to investigate the underlying neurobiology of autism by studying individuals with copy number variation at 16p11.2. Our aim was to investigate the prevailing theories of brain connectivity in autism – specifically, in regard to (1) distributed brain networks, (2) local and distant connectivity, and (3) functional lateralization. Methods: We analyzed resting-state functional connectivity MRI acquired in 26 carriers of a 16p11.2 deletion and 42 age-matched control participants. We also compared the functional connectivity metrics with measures of language ability, IQ, and social behavior.Results: We do not find widespread disruption of canonical large-scale networks in the deletion carriers. Nor do we find quantitative differences in the degree of local and distant connections. Instead, we discover unique connections in 16p11.2 deletion carriers that are not present in the control participants. Specifically, functional coupling between auditory cortex and regions of the default network is present only in the deletion carriers. In addition to the topographic shifts in functional connectivity, we observe reduced right hemispheric lateralization in the deletion carriers and less left hemispheric lateralization in individuals with poorer language ability.Conclusions: Links or connections between primary sensory areas and higher-order association areas violate fundamental large-scale circuit properties by functionally connecting brain regions specialized in local (hierarchical) processing with those that specialize in distant (parallel) processing. Aberrant hemispheric lateralization and connections between auditory and default networks may underlie difficulties with language and social interactions.

scholarly journals Changes in functional connectivity associated with facial expression processing over the working adult lifespan

2021 ◽  
Author(s):  
Thomas Murray ◽  
Justin O'Brien ◽  
Veena Kumari
Keyword(s):  
Functional Connectivity ◽  
Facial Expressions ◽  
Brain Connectivity ◽  
Negative Emotions ◽  
Brain Regions ◽  
Emotional Expressions ◽  
Expression Recognition ◽  
Frontal Pole ◽  
Adult Lifespan ◽  
Age Related

The recognition of negative emotions from facial expressions is shown to decline across the adult lifespan, with some evidence that this decline begins around middle age. While some studies have suggested ageing may be associated with changes in neural response to emotional expressions, it is not known whether ageing is associated with changes in the network connectivity associated with processing emotional expressions. In this study, we examined the effect of participant age on whole-brain connectivity to various brain regions that have been associated with connectivity during emotion processing: the left and right amygdalae, medial prefrontal cortex (mPFC), and right posterior superior temporal sulcus (rpSTS). The study involved healthy participants aged 20-65 who viewed facial expressions displaying anger, fear, happiness, and neutral expressions during functional magnetic resonance imaging (fMRI). We found effects of age on connectivity between the left amygdala and voxels in the occipital pole and cerebellum, between the right amygdala and voxels in the frontal pole, and between the rpSTS and voxels in the orbitofrontal cortex, but no effect of age on connectivity with the mPFC. Furthermore, ageing was more greatly associated with a decline in connectivity to the left amygdala and rpSTS for negative expressions in comparison to happy and neutral expressions, consistent with the literature suggesting a specific age-related decline in the recognition of negative emotions. These results add to the literature surrounding ageing and expression recognition by suggesting that changes in underlying functional connectivity might contribute to changes in recognition of negative facial expressions across the adult lifespan.

scholarly journals Lynn_etal_MathSkills_CPM_Preprint_V1_05July2021

2021 ◽  
Author(s):  
Andrew Lynn ◽  
Eric D. Wilkey ◽  
Gavin Price
Keyword(s):  
Functional Connectivity ◽  
Predictive Modeling ◽  
Brain Connectivity ◽  
Network Connectivity ◽  
Brain Regions ◽  
Math Skills ◽  
Whole Brain ◽  
Building Models ◽  
State Dependent ◽  
Brain Behavior

The human brain comprises multiple canonical networks, several of which are distributed across frontal, parietal, and temporooccipital regions. Studies report both positive and negative correlations between children’s math skills and the strength of functional connectivity among these regions during math-related tasks and at rest. Yet, it is unclear how the relation between children’s math skills and functional connectivity map onto patterns of distributed whole-brain connectivity, canonical network connectivity, and whether these relations are consistent across different task-states. We used connectome-based predictive modeling to test whether functional connectivity during number comparison and at rest predicts children’s math skills (N=31, Mage=9.21years) using distributed whole-brain connections versus connections among canonical networks. We found that weaker connectivity distributed across the whole brain and weaker connectivity between key math-related brain regions in specific canonical networks predicts better math skills in childhood. The specific connections predicting math skills, and whether they were distributed or mapped onto canonical networks, varied between tasks, suggesting that state-dependent rather than trait-level functional network architectures support children’s math skills. Furthermore, the current predictive modeling approach moves beyond brain-behavior correlations and toward building models of brain connectivity that may eventually aid in predicting future math skills.

scholarly journals Large-scale network integration in the human brain tracks temporal fluctuations in memory encoding performance

eLife ◽  
2018 ◽  
Vol 7 ◽  
Author(s):  
Ruedeerat Keerativittayayut ◽  
Ryuta Aoki ◽  
Mitra Taghizadeh Sarabi ◽  
Koji Jimura ◽  
Kiyoshi Nakahara
Keyword(s):  
Functional Connectivity ◽  
Human Brain ◽  
Large Scale ◽  
Brain Regions ◽  
Brain Network ◽  
Time Varying ◽  
Memory Encoding ◽  
Graph Metrics ◽  
Connectivity Patterns ◽  
Network States

Although activation/deactivation of specific brain regions has been shown to be predictive of successful memory encoding, the relationship between time-varying large-scale brain networks and fluctuations of memory encoding performance remains unclear. Here, we investigated time-varying functional connectivity patterns across the human brain in periods of 30–40 s, which have recently been implicated in various cognitive functions. During functional magnetic resonance imaging, participants performed a memory encoding task, and their performance was assessed with a subsequent surprise memory test. A graph analysis of functional connectivity patterns revealed that increased integration of the subcortical, default-mode, salience, and visual subnetworks with other subnetworks is a hallmark of successful memory encoding. Moreover, multivariate analysis using the graph metrics of integration reliably classified the brain network states into the period of high (vs. low) memory encoding performance. Our findings suggest that a diverse set of brain systems dynamically interact to support successful memory encoding.

scholarly journals Reduced Brain Electric Activity and Functional Connectivity in Bipolar Euthymia: An sLORETA Source Localization Study

2019 ◽  
Vol 51 (3) ◽  
pp. 155-166
Author(s):  
Annamaria Painold ◽  
Pascal L. Faber ◽  
Eva Z. Reininghaus ◽  
Sabrina Mörkl ◽  
Anna K. Holl ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Large Scale ◽  
Time Course ◽  
Temporal Cortex ◽  
Electric Activity ◽  
Brain Regions ◽  
Frequency Bands ◽  
Head Surface ◽  
Density Values ◽  
Surface Eeg

Bipolar disorder (BD) is a chronic illness with a relapsing and remitting time course. Relapses are manic or depressive in nature and intermitted by euthymic states. During euthymic states, patients lack the criteria for a manic or depressive diagnosis, but still suffer from impaired cognitive functioning as indicated by difficulties in executive and language-related processing. The present study investigated whether these deficits are reflected by altered intracortical activity in or functional connectivity between brain regions involved in these processes such as the prefrontal and the temporal cortices. Vigilance-controlled resting state EEG of 13 euthymic BD patients and 13 healthy age- and sex-matched controls was analyzed. Head-surface EEG was recomputed into intracortical current density values in 8 frequency bands using standardized low-resolution electromagnetic tomography. Intracortical current densities were averaged in 19 evenly distributed regions of interest (ROIs). Lagged coherences were computed between each pair of ROIs. Source activity and coherence measures between patients and controls were compared (paired t tests). Reductions in temporal cortex activity and in large-scale functional connectivity in patients compared to controls were observed. Activity reductions affected all 8 EEG frequency bands. Functional connectivity reductions affected the delta, theta, alpha-2, beta-2, and gamma band and involved but were not limited to prefrontal and temporal ROIs. The findings show reduced activation of the temporal cortex and reduced coordination between many brain regions in BD euthymia. These activation and connectivity changes may disturb the continuous frontotemporal information flow required for executive and language-related processing, which is impaired in euthymic BD patients.

scholarly journals Functional Connectome of the Five-Factor Model of Personality

2018 ◽  
Vol 1 ◽  
Author(s):  
Nicola Toschi ◽  
Roberta Riccelli ◽  
Iole Indovina ◽  
Antonio Terracciano ◽  
Luca Passamonti
Keyword(s):  
Functional Connectivity ◽  
Large Scale ◽  
Factor Model ◽  
Brain Connectivity ◽  
Five Factor Model ◽  
Mechanistic Explanation ◽  
Data Sets ◽  
Personality Differences ◽  
Human Connectome Project ◽  
Maximal Information Coefficient

Abstract A key objective of the emerging field of personality neuroscience is to link the great variety of the enduring dispositions of human behaviour with reliable markers of brain function. This can be achieved by analysing big data-sets with methods that model whole-brain connectivity patterns. To meet these expectations, we exploited a large repository of personality and neuroimaging measures made publicly available via the Human Connectome Project. Using connectomic analyses based on graph theory, we computed global and local indices of functional connectivity (e.g., nodal strength, efficiency, clustering, betweenness centrality) and related these metrics to the five-factor model (FFM) personality traits (i.e., neuroticism, extraversion, openness, agreeableness, and conscientiousness). The maximal information coefficient was used to assess for linear and nonlinear statistical dependencies across the graph “nodes”, which were defined as distinct large-scale brain circuits identified via independent component analysis. Multivariate regression models and “train/test” approaches were used to examine the associations between FFM traits and connectomic indices as well as to assess the generalizability of the main findings, while accounting for age and sex variability. Conscientiousness was the sole FFM trait linked to measures of higher functional connectivity in the fronto-parietal and default mode networks. This offers a mechanistic explanation of the behavioural observation that conscientious people are reliable and efficient in goal-setting or planning. Our study provides new inputs to understanding the neurological basis of personality and contributes to the development of more realistic models of the brain dynamics that mediate personality differences.

Neurocognitive Aging and Functional Connectivity Using Functional Magnetic Resonance Imaging

2018 ◽  
Author(s):  
Hana Burianová
Keyword(s):  
Functional Connectivity ◽  
Cognitive Processes ◽  
Large Scale ◽  
Healthy Aging ◽  
Brain Activity ◽  
Brain Regions ◽  
Effective Strategies ◽  
Cognitive Improvement ◽  
Age Related ◽  
Neurocognitive Aging

Determining the mechanisms that underlie neurocognitive aging, such as compensation or dedifferentiation, and facilitating the development of effective strategies for cognitive improvement is essential due to the steadily rising aging population. One approach to study the characteristics of healthy aging comprises the assessment of functional connectivity, delineating markers of age-related neurocognitive plasticity. Functional connectivity paradigms characterize complex one-to-many (or many-to-many) structure–function relations, as higher-level cognitive processes are mediated by the interaction among a number of functionally related neural areas rather than localized to discrete brain regions. Task-related or resting-state interregional correlations of brain activity have been used as reliable indices of functional connectivity, delineating age-related alterations in a number of large-scale brain networks, which subserve attention, working memory, episodic retrieval, and task-switching. Together with behavioral and regional activation studies, connectivity studies and modeling approaches have contributed to our understanding of the mechanisms of age-related reorganization of distributed functional networks; specifically, reduced neural specificity (dedifferentiation) and associated impairment in inhibitory control and compensatory neural recruitment.

Discriminative Analysis of Symptom Severity and Ultra-High Risk of Schizophrenia Using Intrinsic Functional Connectivity

2020 ◽  
Vol 30 (09) ◽  
pp. 2050047
Author(s):  
Lubin Wang ◽  
Xianbin Li ◽  
Yuyang Zhu ◽  
Bei Lin ◽  
Qijing Bo ◽  
...  
Keyword(s):  
High Risk ◽  
Functional Connectivity ◽  
Symptom Severity ◽  
Large Scale ◽  
Brain Networks ◽  
Brain Regions ◽  
Support Vector ◽  
Healthy Controls ◽  
Intrinsic Functional Connectivity ◽  
Ultra High Risk

Past studies have consistently shown functional dysconnectivity of large-scale brain networks in schizophrenia. In this study, we aimed to further assess whether multivariate pattern analysis (MVPA) could yield a sensitive predictor of patient symptoms, as well as identify ultra-high risk (UHR) stage of schizophrenia from intrinsic functional connectivity of whole-brain networks. We first combined rank-based feature selection and support vector machine methods to distinguish between 43 schizophrenia patients and 52 healthy controls. The constructed classifier was then applied to examine functional connectivity profiles of 18 UHR individuals. The classifier indicated reliable relationship between MVPA measures and symptom severity, with higher classification accuracy in more severely affected schizophrenia patients. The UHR subjects had classification scores falling between those of healthy controls and patients, suggesting an intermediate level of functional brain abnormalities. Moreover, UHR individuals with schizophrenia-like connectivity profiles at baseline presented higher rate of conversion to full-blown illness in the follow-up visits. Spatial maps of discriminative brain regions implicated increases of functional connectivity in the default mode network, whereas decreases of functional connectivity in the cerebellum, thalamus and visual areas in schizophrenia. The findings may have potential utility in the early diagnosis and intervention of schizophrenia.

scholarly journals Linear Input as a Fundamental Motif of Brain Connectivity Organization

2019 ◽  
Author(s):  
Jonathan F. O’Rawe ◽  
Hoi-Chung Leung
Keyword(s):  
Functional Connectivity ◽  
Information Transfer ◽  
Brain Connectivity ◽  
Brain Regions ◽  
Fundamental Aspect ◽  
Biased Sampling ◽  
Whole Brain ◽  
3 Dimensional ◽  
Face Area ◽  
Learning Techniques

AbstractDescribing the pattern of region-to-region functional connectivity is an important step towards understanding information transfer and transformation between brain regions. Although fMRI data are limited in spatial resolution, recent advances in technology afford more precise mapping. Here, we extended previous methods, connective field mapping, to 3 dimensions to provide a more concise estimate of the organization and potential information transformation from one region to another. We first replicated previous work with the 3 dimensional model by showing that the topology of functional connectivity between early visual regions maintained along their eccentricity axis or the anterior-posterior dimension. We then examined higher order visual regions (e,g, fusiform face area) and showed that their pattern of connectivity, the convergence and biased sampling, seem to contribute to some of their core receptive field properties. We further demonstrated that linearity of input is a fundamental aspect of functional connectivity of the whole brain, with higher linearity between regions within a network than across networks; that is, high connective linearity was evident between early visual areas, and between prefrontal areas, but less evident between them. By decomposing the whole brain linearity matrix with manifold learning techniques, we found that the principle mode of the linearity maps onto decompositions in both functional connectivity and genetic expression reported in previous studies. The current work provides evidence supporting that linearity of input is likely a fundamental motif of functional connectivity between regions for information processing across the brain, with high linearity preserving the integrity of information from one region to another within a network.

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