scholarly journals How the Statistical Validation of Functional Connectivity Patterns Can Prevent Erroneous Definition of Small-World Properties of a Brain Connectivity Network

2012 ◽  
Vol 2012 ◽  
pp. 1-13 ◽  
Author(s):  
J. Toppi ◽  
F. De Vico Fallani ◽  
G. Vecchiato ◽  
A. G. Maglione ◽  
F. Cincotti ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Adjacency Matrix ◽  
Brain Connectivity ◽  
Small World ◽  
Connectivity Pattern ◽  
Edge Density ◽  
Statistical Validation ◽  
Common Procedure ◽  
Connectivity Patterns ◽  
Definition Of

The application of Graph Theory to the brain connectivity patterns obtained from the analysis of neuroelectrical signals has provided an important step to the interpretation and statistical analysis of such functional networks. The properties of a network are derived from the adjacency matrix describing a connectivity pattern obtained by one of the available functional connectivity methods. However, no common procedure is currently applied for extracting the adjacency matrix from a connectivity pattern. To understand how the topographical properties of a network inferred by means of graph indices can be affected by this procedure, we compared one of the methods extensively used in Neuroscience applications (i.e. fixing the edge density) with an approach based on the statistical validation of achieved connectivity patterns. The comparison was performed on the basis of simulated data and of signals acquired on a polystyrene head used as a phantom. The results showed (i) the importance of the assessing process in discarding the occurrence of spurious links and in the definition of the real topographical properties of the network, and (ii) a dependence of the small world properties obtained for the phantom networks from the spatial correlation of the neighboring electrodes.

scholarly journals Convolutional Sparse Coded Dynamic Brain Functional Connectivity

2018 ◽  
Author(s):  
Jin Yan ◽  
Yingying Zhu
Keyword(s):  
Functional Connectivity ◽  
Brain Connectivity ◽  
Sliding Window ◽  
Brain Network ◽  
Clinical Applications ◽  
Connectivity Pattern ◽  
Brain Disorder ◽  
Connectivity Patterns ◽  
Brain Functional Connectivity ◽  
The Brain

AbstractFunctional brain network has been widely studied in many previous work for brain disorder diagnosis and brain network analysis. However, most previous work focus on static dynamic brain network research. Lots of recent work reveals that the brain shows dynamic activity even in resting state. Such dynamic brain functional connectivity reveals discriminative patterns for identifying many brain disorders. Current sliding window based dynamic brain connectivity framework are not easy to be applied to real clinical applications due to many issues: First, how to set up the optimal sliding window size and how to determine the threshold for the brain connectivity patterns. Secondly, how to represent the high dimensional dynamic brain connectivity pattern in a low dimensional representations for diagnosis purpose. Last, how to deal with the different length dynamic brain network patterns especially when the raw data are of different length. In order to address all those above issues, we proposed a new framework, which employs multiple scale sliding windows and automatically learns a sparse and low ran dynamic brain functional connectivity patterns from raw fMRI data. Furthermore, we are able to measure different length dynamic brain functional connectivity patterns in an equal space by learning a sparse coded convolutional filters. We have evaluated our method with state of the art dynamic brain network methods and the results demonstrated the strong potential of our methods for brain disorder diagnosis in real clinical applications.

scholarly journals Resting-state brain network topological properties and the correlation with neuropsychological assessment in adolescent narcolepsy

SLEEP ◽  
2020 ◽  
Vol 43 (8) ◽  
Author(s):  
Xiao Fulong ◽  
Spruyt Karen ◽  
Lu Chao ◽  
Zhao Dianjiang ◽  
Zhang Jun ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Neuropsychological Assessment ◽  
Resting State ◽  
Brain Connectivity ◽  
Small World ◽  
Theoretical Method ◽  
Brain Network ◽  
Topological Properties ◽  
Depressive Feelings ◽  
Network Properties

Abstract Study Objectives To evaluate functional connectivity and topological properties of brain networks, and to investigate the association between brain topological properties and neuropsychiatric behaviors in adolescent narcolepsy. Methods Resting-state functional magnetic resonance imaging (fMRI) and neuropsychological assessment were applied in 26 adolescent narcolepsy patients and 30 healthy controls. fMRI data were analyzed in three ways: group independent component analysis and a graph theoretical method were applied to evaluate topological properties within the whole brain. Lastly, network-based statistics was utilized for group comparisons in region-to-region connectivity. The relationship between topological properties and neuropsychiatric behaviors was analyzed with correlation analyses. Results In addition to sleepiness, depressive symptoms and impulsivity were detected in adolescent narcolepsy. In adolescent narcolepsy, functional connectivity was decreased between regions of the limbic system and the default mode network (DMN), and increased in the visual network. Adolescent narcolepsy patients exhibited disrupted small-world network properties. Regional alterations in the caudate nucleus (CAU) and posterior cingulate gyrus were associated with subjective sleepiness and regional alterations in the CAU and inferior occipital gyrus were associated with impulsiveness. Remodeling within the salience network and the DMN was associated with sleepiness, depressive feelings, and impulsive behaviors in narcolepsy. Conclusions Alterations in brain connectivity and regional topological properties in narcoleptic adolescents were associated with their sleepiness, depressive feelings, and impulsive behaviors.

scholarly journals Age of Insomnia Onset Correlates with a Reversal of Default Mode Network and Supplementary Motor Cortex Connectivity

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Emiliano Santarnecchi ◽  
Chiara Del Bianco ◽  
Isabella Sicilia ◽  
Davide Momi ◽  
Giorgio Di Lorenzo ◽  
...  
Keyword(s):  
Visual Cortex ◽  
Functional Connectivity ◽  
Sleep Deprivation ◽  
Default Mode Network ◽  
Age Of Onset ◽  
Brain Connectivity ◽  
Disruptive Effect ◽  
Default Mode ◽  
Connectivity Patterns ◽  
The Impact

Insomnia might occur as result of increased cognitive and physiological arousal caused by acute or long acting stressors and associated cognitive rumination. This might lead to alterations in brain connectivity patterns as those captured by functional connectivity fMRI analysis, leading to potential insight about primary insomnia (PI) pathophysiology as well as the impact of long-term exposure to sleep deprivation. We investigated changes of voxel-wise connectivity patterns in a sample of 17 drug-naïve PI patients and 17 age-gender matched healthy controls, as well as the relationship between brain connectivity and age of onset, illness duration, and severity. Results showed a significant increase in resting-state functional connectivity of the bilateral visual cortex in PI patients, associated with decreased connectivity between the visual cortex and bilateral temporal pole. Regression with clinical scores originally unveiled a pattern of increased local connectivity as measured by intrinsic connectivity contrast (ICC), specifically resembling the default mode network (DMN). Additionally, age of onset was found to be correlated with the connectivity of supplementary motor area (SMA), and the strength of DMN←→SMA connectivity was significantly correlated with both age of onset (R2 = 41%) and disease duration (R2 = 21%). Chronic sleep deprivation, but most importantly early insomnia onset, seems to have a significant disruptive effect over the physiological negative correlation between DMN and SMA, a well-known fMRI marker of attention performance in humans. This suggests the need for more in-depth investigations on the prevention and treatment of connectivity changes and associated cognitive and psychological deficits in PI patients.

scholarly journals Multivariate Pattern Analysis of fMRI in Breast Cancer Survivors and Healthy Women

2013 ◽  
Vol 20 (4) ◽  
pp. 391-401 ◽  
Author(s):  
S.M. Hadi Hosseini ◽  
Shelli R. Kesler
Keyword(s):  
Breast Cancer ◽  
Functional Connectivity ◽  
Breast Cancer Survivors ◽  
Pattern Analysis ◽  
Brain Connectivity ◽  
Survival Rates ◽  
Connectivity Pattern ◽  
Multivariate Pattern Analysis ◽  
The Right ◽  
Multivariate Pattern

AbstractAdvances in breast cancer (BC) treatments have resulted in significantly improved survival rates. However, BC chemotherapy is often associated with several side effects including cognitive dysfunction. We applied multivariate pattern analysis (MVPA) to functional magnetic resonance imaging (fMRI) to find a brain connectivity pattern that accurately and automatically distinguishes chemotherapy-treated (C+) from non-chemotherapy treated (C−) BC females and healthy female controls (HC). Twenty-seven C+, 29 C−, and 30 HC underwent fMRI during an executive-prefrontal task (Go/Nogo). The pattern of functional connectivity associated with this task discriminated with significant accuracy between C+ and HC groups (72%, p = .006) and between C+ and C− groups (71%, p = .012). However, the accuracy of discrimination between C− and HC was not significant (51%, p = .46). Compared with HC, behavioral performance of the C+ and C− groups during the task was intact. However, the C+ group demonstrated altered functional connectivity in the right frontoparietal and left supplementary motor area networks compared to HC, and in the right middle frontal and left superior frontal gyri networks, compared to C−. Our results provide further evidence that executive function performance may be preserved in some chemotherapy-treated BC survivors through recruitment of additional neural connections. (JINS, 2013, 19, 1–11)

scholarly journals Sex-Related Neurocircuitry Supporting Camouflaging in Adults with Autism: Female Protection Insights

2021 ◽  
Author(s):  
Melissa Walsh ◽  
Broc Pagni ◽  
Leanna Monahan ◽  
Shanna Delaney ◽  
Christopher J Smith ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Cognitive Control ◽  
Emotion Recognition ◽  
Brain Connectivity ◽  
Data Driven ◽  
Sex Bias ◽  
Adults With Autism ◽  
Connectivity Patterns ◽  
Females With Autism

Background: The male preponderance in autism led to the hypothesis that aspects of female biology are protective against autism. Females with autism report engaging in more compensatory behaviors (i.e., camouflaging) to overcome autism-related social differences, which may be a downstream result of protective pathways. No studies have examined sex-related brain pathways supporting camouflaging in females with autism, despite its potential to inform mechanisms underlying the sex bias in autism. Methods: This study included 45 non-intellectually-disabled adults with autism (male/female: 21/24) and 40 neurotypical adults (male/female: 19/21) ages 18-71. We used group multivariate voxel pattern analysis to conduct a data-driven, connectome-wide characterization of "sex-atypical" (sex-by-diagnosis) and "sex-typical" (sex) brain functional connectivity features linked to camouflaging, and validated findings in females with autism multi-modally via structural connectometry. Exploratory associations with cognitive control, memory, emotion recognition, and depression/anxiety examined the adaptive nature of functional connectivity patterns supporting camouflaging in females with autism. Results: We found 1) "sex-atypical" functional connectivity patterns predicting camouflaging in the hypothalamus and precuneus and 2) "sex-typical" patterns in the anterior cingulate and right anterior parahippocampus. Higher hypothalamic functional connectivity with a limbic reward cluster was the strongest predictor of camouflaging in females with autism (a "sex-atypical" pattern), and also predicted better cognitive control/emotion recognition. Structural connectometry validated functional connectivity results with consistent brain pathways/effect patterns implicated across multi-modal findings in females with autism. Conclusion: This data-driven, connectome-wide characterization of "sex-atypical" and "sex-typical" brain connectivity features supporting compensatory social behavior in autism suggests hormones may play a role in the autism sex bias. Furthermore, both "male-typical" and "female-typical" brain connectivity patterns are implicated in camouflaging in females with autism in circuits associated with reward, emotion, and memory processing. "Sex-atypical" results are consistent with the fetal steroidogenic hypothesis, which would result in masculinized brain features in females with autism. However, female genetics/biology may contribute to "female-typical" patterns implicated in camouflaging.

scholarly journals Distinctive BOLD Connectivity Patterns in the Schizophrenic Brain: Machine-learning based comparison between various connectivity measures

2016 ◽  
Author(s):  
Hio-Been Han
Keyword(s):  
Functional Connectivity ◽  
Memory Task ◽  
Similarity Measures ◽  
Low Frequency ◽  
Pattern Discrimination ◽  
Brain Regions ◽  
Connectivity Pattern ◽  
Time Frequency ◽  
Connectivity Patterns ◽  
Network Properties

AbstractRecent functional magnetic resonance imaging (fMRI) studies have found distinctive functional connectivity in the schizophrenic brain. However, most of the studies focused on the correlation value to define the functional connectivity for BOLD fluctuations between brain regions, which resulted in the limited understanding to the network properties of altered wirings in the schizophrenic brain. Here I characterized the distinctiveness of BOLD connectivity pattern in the schizophrenic brain relative to healthy brain with various similarity measures in the time-frequency domain, while participants are performing the working memory task in the MRI scanner. To assess the distinctiveness of the connectivity pattern, discrimination performances of the pattern classifier machine trained with each similarity measure were compared. Interestingly, the classifier machine trained by time-lagging patterns of low frequency fluctuation (LFF) produced higher classifying sensitivity than the machines trained by other measures. Also, the classifier machine trained by coherence pattern in LFF band also made better performance than the machine trained by correlation-based connectivity pattern. These results indicate that there are unobserved but considerable features in the functional connectivity pattern of schizophrenic brain which traditional emphasis on correlation analysis does not capture.

scholarly journals Data-driven clustering reveals a link between symptoms and functional brain connectivity in depression

2018 ◽  
Author(s):  
Luigi A. Maglanoc ◽  
Nils Inge Landrø ◽  
Rune Jonassen ◽  
Tobias Kaufmann ◽  
Aldo Cordova-Palomera ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Brain Connectivity ◽  
Total Sample ◽  
Individual Variability ◽  
Functional Brain Imaging ◽  
Depression Symptoms ◽  
Imaging Data ◽  
Functional Brain ◽  
Symptom Profiles ◽  
Connectivity Patterns

AbstractBackgroundDepression is a complex disorder with large inter-individual variability in symptom profiles that often occur alongside symptoms of other psychiatric domains such as anxiety. A dimensional and symptom-based approach may help refine the characterization and classification of depressive and anxiety disorders and thus aid in establishing robust biomarkers. We assess the brain functional connectivity correlates of a symptom-based clustering of individuals using functional brain imaging data.MethodsWe assessed symptoms of depression and anxiety using Beck’s Depression and Beck’s Anxiety inventories in individuals with or without a history of depression, and high dimensional data clustering to form subgroups based on symptom profiles. To assess the biological relevance of this subtyping, we compared functional magnetic resonance imaging-based dynamic and static functional connectivity between subgroups in a subset of the total sample.ResultsWe identified five subgroups with distinct symptom profiles, cutting across diagnostic boundaries and differing in terms of total severity, symptom patterns and centrality. For instance, inability to relax, fear of the worst, and feelings of guilt were among the most severe symptoms in subgroup 1, 2 and 3, respectively. These subgroups showed evidence of differential static brain connectivity patterns, in particular comprising a fronto-temporal network. In contrast, we found no significant associations with clinical sum scores, dynamic functional connectivity or global connectivity measures.ConclusionAdding to the ongoing pursuit of individual-based treatment, the results show subtyping based on a dimensional conceptualization and unique constellations of anxiety and depression symptoms is supported by distinct brain static functional connectivity patterns.

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 Methodological aspects of analyzing high resolved brain connectivity for multiple subjects

2017 ◽  
Vol 3 (2) ◽  
pp. 417-421
Author(s):  
Britta Pester ◽  
Christoph Schmidt ◽  
Karl-Jürgen Bär ◽  
Lutz Leistritz
Keyword(s):  
Functional Connectivity ◽  
High Spatial Resolution ◽  
Brain Connectivity ◽  
Tensor Decomposition ◽  
Detection Algorithms ◽  
Module Detection ◽  
Connectivity Patterns ◽  
Meaningful Group ◽  
Methodological Aspects ◽  
Subject Comparison

AbstractAnalyzing directed interactions within brain networks of high spatial resolution is always associated with a limited interpretability due to the high amount of possible connections. Here, module detection algorithms have proven to helpfully subsume the information of the resulting networks for each proband. However, the between-subject comparison of clusters is not straightforward since identified modules are not matched to each other across different subjects. Tensor decomposition has successfully been applied for the detection of group-wide connectivity patterns. Yet, a thorough investigation of the effect of the involved analysis parameters and data properties on decomposition results has still been missing. In this study we filled this gap and found that - given appropriate parameter choices - tensor decomposition of functional connectivity data reveals meaningful, group-specific insights into the brain's information processing.

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