Empirical validation of directed functional connectivity

2016 ◽  
Author(s):  
Ravi D Mill ◽  
Anto Bagic ◽  
Walter Schneider ◽  
Michael W Cole
Keyword(s):  
Sensory Modality ◽  
Sensory Information ◽  
Ground Truth ◽  
Connectivity Pattern ◽  
Bayes Network ◽  
Brain Connectome ◽  
Connectivity Patterns ◽  
Reactivation Effect ◽  
Practical Guidelines ◽  
Task Conditions

Mapping directions of influence in the human brain connectome represents the next phase in understanding its functional architecture. However, a host of methodological uncertainties have impeded the application of directed connectivity methods, which have primarily been validated via 'ground truth' connectivity patterns embedded in simulated functional MRI (fMRI) and magneto-/electro-encephalography (MEG/EEG) datasets. Such simulations rely on many generative assumptions, and we hence utilized a different strategy involving empirical data in which a ground truth directed connectivity pattern could be anticipated with confidence. Specifically, we exploited the established 'sensory reactivation' effect in episodic memory, in which retrieval of sensory information reactivates regions involved in perceiving that sensory modality. Subjects performed a paired associate task in separate fMRI and MEG sessions, in which a ground truth reversal in directed connectivity between auditory and visual sensory regions was instantiated across task conditions. This directed connectivity reversal was successfully recovered across different algorithms, including Granger causality and Bayes network (IMAGES) approaches, and across fMRI ('raw' and deconvolved) and source-modeled MEG. These results extend simulation studies of directed connectivity, and offer practical guidelines for the use of such methods in clarifying causal mechanisms of neural processing.

scholarly journals Autonomous feature type selection based on environment using expectation maximization in self-localization

2018 ◽  
Vol 15 (6) ◽  
pp. 172988141881470
Author(s):  
Nezih Ergin Özkucur ◽  
H Levent Akın
Keyword(s):  
Expectation Maximization ◽  
Autonomous Robots ◽  
Expectation Maximization Algorithm ◽  
Sensory Information ◽  
Ground Truth ◽  
Local Environment ◽  
Localization Algorithm ◽  
Data Set ◽  
Public Data ◽  
The Individual

Self-localization in autonomous robots is one of the fundamental issues in the development of intelligent robots, and processing of raw sensory information into useful features is an integral part of this problem. In a typical scenario, there are several choices for the feature extraction algorithm, and each has its weaknesses and strengths depending on the characteristics of the environment. In this work, we introduce a localization algorithm that is capable of capturing the quality of a feature type based on the local environment and makes soft selection of feature types throughout different regions. A batch expectation–maximization algorithm is developed for both discrete and Monte Carlo localization models, exploiting the probabilistic pose estimations of the robot without requiring ground truth poses and also considering different observation types as blackbox algorithms. We tested our method in simulations, data collected from an indoor environment with a custom robot platform and a public data set. The results are compared with the individual feature types as well as naive fusion strategy.

scholarly journals Benchmarking joint multi-omics dimensionality reduction approaches for cancer study

2020 ◽  
Author(s):  
Laura Cantini ◽  
Pooya Zakeri ◽  
Celine Hernandez ◽  
Aurelien Naldi ◽  
Denis Thieffry ◽  
...  
Keyword(s):  
Dimensionality Reduction ◽  
Ground Truth ◽  
Systematic Evaluation ◽  
Omics Data ◽  
Biological Processes ◽  
Cancer Data ◽  
Practical Guidelines ◽  
Cell Data ◽  
Omics Data Integration

AbstractHigh-dimensional multi-omics data are now standard in biology. They can greatly enhance our understanding of biological systems when effectively integrated. To achieve this multi-omics data integration, Joint Dimensionality Reduction (jDR) methods are among the most efficient approaches. However, several jDR methods are available, urging the need for a comprehensive benchmark with practical guidelines.We performed a systematic evaluation of nine representative jDR methods using three complementary benchmarks. First, we evaluated their performances in retrieving ground-truth sample clustering from simulated multi-omics datasets. Second, we used TCGA cancer data to assess their strengths in predicting survival, clinical annotations and known pathways/biological processes. Finally, we assessed their classification of multi-omics single-cell data.From these in-depth comparisons, we observed that intNMF performs best in clustering, while MCIA offers a consistent and effective behavior across many contexts. The full code of this benchmark is implemented in a Jupyter notebook - multi-omics mix (momix) - to foster reproducibility, and support data producers, users and future developers.

scholarly journals Multimodal integration and stimulus categorization in putative mushroom body output neurons of the honeybee

2018 ◽  
Vol 5 (2) ◽  
pp. 171785 ◽  
Author(s):  
Martin F. Strube-Bloss ◽  
Wolfgang Rössler
Keyword(s):  
Visual Information ◽  
Mushroom Body ◽  
Visual Cues ◽  
Sensory Modality ◽  
Sensory Information ◽  
Insect Brain ◽  
Pollinating Insects ◽  
Output Neurons ◽  
Electrophysiological Characterization ◽  
Nonlinear Integration

Flowers attract pollinating insects like honeybees by sophisticated compositions of olfactory and visual cues. Using honeybees as a model to study olfactory–visual integration at the neuronal level, we focused on mushroom body (MB) output neurons (MBON). From a neuronal circuit perspective, MBONs represent a prominent level of sensory-modality convergence in the insect brain. We established an experimental design allowing electrophysiological characterization of olfactory, visual, as well as olfactory–visual induced activation of individual MBONs. Despite the obvious convergence of olfactory and visual pathways in the MB, we found numerous unimodal MBONs. However, a substantial proportion of MBONs (32%) responded to both modalities and thus integrated olfactory–visual information across MB input layers. In these neurons, representation of the olfactory–visual compound was significantly increased compared with that of single components, suggesting an additive, but nonlinear integration. Population analyses of olfactory–visual MBONs revealed three categories: (i) olfactory, (ii) visual and (iii) olfactory–visual compound stimuli. Interestingly, no significant differentiation was apparent regarding different stimulus qualities within these categories. We conclude that encoding of stimulus quality within a modality is largely completed at the level of MB input, and information at the MB output is integrated across modalities to efficiently categorize sensory information for downstream behavioural decision processing.

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 Developmental Recovery of Impaired Multisensory Processing in Autism and the Cost of Switching Sensory Modality

2019 ◽  
Author(s):  
Michael J. Crosse ◽  
John J. Foxe ◽  
Sophie Molholm
Keyword(s):  
Response Times ◽  
Multisensory Processing ◽  
Developmental Trajectories ◽  
Sensory Modality ◽  
Computational Modelling ◽  
Sensory Information ◽  
Autism Spectrum ◽  
Audiovisual Stimuli ◽  
The Cost ◽  
Multisensory Information

AbstractChildren with autism spectrum disorder (ASD) are often impaired in their ability to cope with and process multisensory information, which may contribute to some of the social and communicative deficits that are prevalent in this population. Amelioration of such deficits in adolescence has been observed for ecologically-relevant stimuli such as speech. However, it is not yet known if this recovery generalizes to the processing of nonsocial stimuli such as more basic beeps and flashes, typically used in cognitive neuroscience research. We hypothesize that engagement of different neural processes and lack of environmental exposure to such artificial stimuli leads to protracted developmental trajectories in both neurotypical (NT) individuals and individuals with ASD, thus delaying the age at which we observe this “catch up”. Here, we test this hypothesis using a bisensory detection task by measuring human response times to randomly presented auditory, visual and audiovisual stimuli. By measuring the behavioral gain afforded by an audiovisual signal, we show that the multisensory deficit previously reported in children with ASD recovers in adulthood by the mid-twenties. In addition, we examine the effects of switching between sensory modalities and show that teenagers with ASD incur less of a behavioral cost than their NT peers. Computational modelling reveals that multisensory information interacts according to different rules in children and adults, and that sensory evidence is weighted differently too. In ASD, weighting of sensory information and allocation of attention during multisensory processing differs to that of NT individuals. Based on our findings, we propose a theoretical framework of multisensory development in NT and ASD individuals.

scholarly journals Cross-species cortical alignment identifies different types of anatomical reorganization in the primate temporal lobe

eLife ◽  
2020 ◽  
Vol 9 ◽  
Author(s):  
Nicole Eichert ◽  
Emma C Robinson ◽  
Katherine L Bryant ◽  
Saad Jbabdi ◽  
Mark Jenkinson ◽  
...  
Keyword(s):  
Temporal Lobe ◽  
Parietal Cortex ◽  
Connectivity Pattern ◽  
Arcuate Fasciculus ◽  
Computational Neuroanatomy ◽  
Brain Organization ◽  
Cortical Organization ◽  
Connectivity Patterns ◽  
Different Types ◽  
Brain Architecture

Evolutionary adaptations of temporo-parietal cortex are considered to be a critical specialization of the human brain. Cortical adaptations, however, can affect different aspects of brain architecture, including local expansion of the cortical sheet or changes in connectivity between cortical areas. We distinguish different types of changes in brain architecture using a computational neuroanatomy approach. We investigate the extent to which between-species alignment, based on cortical myelin, can predict changes in connectivity patterns across macaque, chimpanzee, and human. We show that expansion and relocation of brain areas can predict terminations of several white matter tracts in temporo-parietal cortex, including the middle and superior longitudinal fasciculus, but not the arcuate fasciculus. This demonstrates that the arcuate fasciculus underwent additional evolutionary modifications affecting the temporal lobe connectivity pattern. This approach can flexibly be extended to include other features of cortical organization and other species, allowing direct tests of comparative hypotheses of brain organization.

Increased structural white and grey matter network connectivity compensates for functional decline in early multiple sclerosis

2016 ◽  
Vol 23 (3) ◽  
pp. 432-441 ◽  
Author(s):  
Vinzenz Fleischer ◽  
Adriane Gröger ◽  
Nabin Koirala ◽  
Amgad Droby ◽  
Muthuraman Muthuraman ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Grey Matter ◽  
Diffusion Tensor ◽  
Disease Onset ◽  
Structural Connectivity ◽  
Connectivity Pattern ◽  
Structural Adaptation ◽  
Theoretical Approaches ◽  
Connectivity Patterns ◽  
Clinical Impairment

Background: The pathology of multiple sclerosis (MS) consists of demyelination and neuronal injury, which occur early in the disease; yet, remission phases indicate repair. Whether and how the central nervous system (CNS) maintains homeostasis to counteract clinical impairment is not known. Objective: We analyse the structural connectivity of white matter (WM) and grey matter (GM) networks to understand the absence of clinical decline as the disease progresses. Methods: A total of 138 relapsing–remitting MS patients (classified into six groups by disease duration) and 32 healthy controls were investigated using 3-Tesla magnetic resonance imaging (MRI). Networks were analysed using graph theoretical approaches based on connectivity patterns derived from diffusion-tensor imaging with probabilistic tractography for WM and voxel-based morphometry and regional-volume-correlation matrix for GM. Results: In the first year after disease onset, WM networks evolved to a structure of increased modularity, strengthened local connectivity and increased local clustering while no clinical decline occurred. GM networks showed a similar dynamic of increasing modularity. This modified connectivity pattern mainly involved the cerebellum, cingulum and temporo-parietal regions. Clinical impairment was associated at later disease stages with a divergence of the network patterns. Conclusion: Our findings suggest that network functionality in MS is maintained through structural adaptation towards increased local and modular connectivity, patterns linked to adaptability and homeostasis.

scholarly journals Stop signals delay synchrony more for finger tapping than vocalization: a dual modality study of rhythmic synchronization in the stop signal task

PeerJ ◽  
2018 ◽  
Vol 6 ◽  
pp. e5242 ◽  
Author(s):  
Leidy J. Castro-Meneses ◽  
Paul F. Sowman
Keyword(s):  
Response Times ◽  
Sensory Modality ◽  
Sensory Information ◽  
Reaction Times ◽  
Proactive Inhibition ◽  
Stop Signal ◽  
Finger Tapping ◽  
Stop Signal Task ◽  
Auditory Modality ◽  
The Brain

Background A robust feature of sensorimotor synchronization (SMS) performance in finger tapping to an auditory pacing signal is the negative asynchrony of the tap with respect to the pacing signal. The Paillard–Fraisse hypothesis suggests that negative asynchrony is a result of inter-modal integration, in which the brain compares sensory information across two modalities (auditory and tactile). The current study compared the asynchronies of vocalizations and finger tapping in time to an auditory pacing signal. Our first hypothesis was that vocalizations have less negative asynchrony compared to finger tapping due to the requirement for sensory integration within only a single (auditory) modality (intra-modal integration). However, due to the different measurements for vocalizations and finger responses, interpreting the comparison between these two response modalities is problematic. To address this problem, we included stop signals in the synchronization task. The rationale for this manipulation was that stop signals would perturb synchronization more in the inter-modal compared to the intra-modal task. We hypothesized that the inclusion of stop signals induce proactive inhibition, which reduces negative asynchrony. We further hypothesized that any reduction in negative asynchrony occurs to a lesser degree for vocalization than for finger tapping. Method A total of 30 participants took part in this study. We compared SMS in a single sensory modality (vocalizations (or auditory) to auditory pacing signal) to a dual sensory modality (fingers (or tactile) to auditory pacing signal). The task was combined with a stop signal task in which stop signals were relevant in some blocks and irrelevant in others. Response-to-pacing signal asynchronies and stop signal reaction times were compared across modalities and across the two types of stop signal blocks. Results In the blocks where stopping was irrelevant, we found that vocalization (−61.47 ms) was more synchronous with the auditory pacing signal compared to finger tapping (−128.29 ms). In the blocks where stopping was relevant, stop signals induced proactive inhibition, shifting the response times later. However, proactive inhibition (26.11 ms) was less evident for vocalizations compared to finger tapping (58.06 ms). Discussion These results support the interpretation that relatively large negative asynchrony in finger tapping is a consequence of inter-modal integration, whereas smaller asynchrony is associated with intra-modal integration. This study also supports the interpretation that intra-modal integration is more sensitive to synchronization discrepancies compared to inter-modal integration.

scholarly journals A Hypercube-Based Encoding for Evolving Large-Scale Neural Networks

2009 ◽  
Vol 15 (2) ◽  
pp. 185-212 ◽  
Author(s):  
Kenneth O. Stanley ◽  
David B. D'Ambrosio ◽  
Jason Gauci
Keyword(s):  
Neural Networks ◽  
Visual Discrimination ◽  
Large Scale ◽  
Dimensional Space ◽  
Connectivity Pattern ◽  
New Class ◽  
Connectivity Patterns ◽  
Indirect Encoding ◽  
Compositional Pattern ◽  
Lower Dimensional Space

Research in neuroevolution—that is, evolving artificial neural networks (ANNs) through evolutionary algorithms—is inspired by the evolution of biological brains, which can contain trillions of connections. Yet while neuroevolution has produced successful results, the scale of natural brains remains far beyond reach. This article presents a method called hypercube-based NeuroEvolution of Augmenting Topologies (HyperNEAT) that aims to narrow this gap. HyperNEAT employs an indirect encoding called connective compositional pattern-producing networks (CPPNs) that can produce connectivity patterns with symmetries and repeating motifs by interpreting spatial patterns generated within a hypercube as connectivity patterns in a lower-dimensional space. This approach can exploit the geometry of the task by mapping its regularities onto the topology of the network, thereby shifting problem difficulty away from dimensionality to the underlying problem structure. Furthermore, connective CPPNs can represent the same connectivity pattern at any resolution, allowing ANNs to scale to new numbers of inputs and outputs without further evolution. HyperNEAT is demonstrated through visual discrimination and food-gathering tasks, including successful visual discrimination networks containing over eight million connections. The main conclusion is that the ability to explore the space of regular connectivity patterns opens up a new class of complex high-dimensional tasks to neuroevolution.

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.

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