Multi-modal data fusion has recently emerged as a comprehensive neuroimaging analysis approach, which usually uses canonical correlation (CCA). However, the current CCA-based approaches face problems like high-dimensionality, multi-collinearity, unimodal feature selection, asymmetry, and loss of spatial information in reshaping imaging into vectors. This paper proposes structured sparse CCA (ssCCA) technique novel method to overcome above problems. To investigate performance proposed...

Emotion regulation plays a key role in human behavior and overall well-being. Neurofeedback is non-invasive self-brain training technique used for emotion to enhance brain function treatment of mental disorders through behavioral changes. Previous neurofeedback research often focused on using activity from single region as measured by fMRI or power one two EEG electrodes. In new study, we employed connectivity-based recalling positive autobiographical memories simultaneous upregulate...

Despite the existence of several emotion regulation studies using neurofeedback, interactions among a small number regions were evaluated, and therefore, further investigation is needed to understand brain involved in regulation. We implemented electroencephalography (EEG) neurofeedback with simultaneous functional magnetic resonance imaging (fMRI) modified happiness-inducing task through autobiographical memories upregulate positive emotion. Then, an explorative analysis whole was done...

Understanding the causal interactions in simple brain tasks, such as face detection, remains a challenging and ambiguous process for researchers. In this study, we address issue by employing novel discovery method -- Directed Acyclic Graphs via M-matrices Acyclicity (DAGMA) to investigate structure of brain's face-selective network gain deeper insights into its mechanism. Using natural movie stimuli, extract regions analyze how frames containing faces influence network. Our findings reveal...

Spontaneous blood oxygen level-dependent signals can be indirectly recorded in different brain regions with functional magnetic resonance imaging. In this study resting-state imaging was used to measure the differences connectivity and activation seen major depressive disorder (MDD) patients without suicidal ideation control group. For our investigation, a atlas containing 116 of interest used. We also four voxel-based models, including degree centrality, fractional amplitude low-frequency...

Schizophrenia (SZ) is a severe mental disorder characterized by failure of functional integration (aka dysconnection) across the brain. Recent connectivity (FC) studies have adopted parcellations to define subnetworks large-scale networks, and characterize (dys)connection between them, in normal clinical populations. While FC examines statistical dependencies observations, model-based effective (EC) can disclose causal influences that underwrite observed dependencies. In this study, we...

Abstract Humans can recognize others’ actions in the social environment. This action recognition ability is rarely hindered by movement of people The neural basis this position tolerance for observed not fully understood. Here, we aimed to identify brain regions capable generalizing representations across different positions and investigate representational content these regions. In a functional magnetic resonance imaging experiment, participants viewed point-light displays human actions....

In the context of EEG/MEG, term 'volume conduction (VC) effects' refers to recording an instantaneous linear mixture multiple brain source activities by each EEG/MEG channel. VC effects may lead detection spurious functional/effective couplings among channels that are not caused interactions. It is importance determine which detected indicators interactions and originate from artefacts. this paper, a quantitative framework proposed explore origin channel using two types surrogate datasets....

In this paper, we study temporal couplings between interictal events of spatially remote regions in order to localize the leading epileptic from intracerebral EEG (iEEG). We aim assess whether quantitative graph analysis during period may be helpful predict seizure onset zone ictal iEEG. Using wavelet transform, cross-correlation coefficient, and multiple hypothesis test, propose a differential connectivity (DCG) represent connections that change significantly nonepileptic states as defined...

Background: Emotion regulation by neurofeedback involves interactions among multiple brain regions, including prefrontal cortex and subcortical regions. Previous studies focused on connections of specific regions such as amygdala with other New method: Electroencephalography (EEG) is used to upregulate positive emotion retrieving autobiographical memories functional magnetic resonance imaging (fMRI) data acquired simultaneously. A global data-driven approach, group independent component...

Divisive normalization of the neural responses by activity neighboring neurons has been proposed as a fundamental operation in nervous system based on its success predicting recorded primate electrophysiology studies. Nevertheless, experimental evidence for existence this human brain is still scant. Here, using functional MRI, we examined role across visual hierarchy cortex. Using stimuli form two categories bodies and houses, presented objects isolation or clutter asked participants to...

We propose a novel approach for evaluating the performance of activation detection in real (experimental) datasets using new mutual information (MI)-based metric and compare its sensitivity to several existing metrics both simulated datasets. The proposed is based on measuring approximate MI between fMRI time-series validation dataset calculated map (thresholded label or continuous map) from an independent training dataset. used measure amount preserved during extraction experimentally...