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compared with the volume, is more likely to enable a different view on the data and allow us to learn something new. In a similar way, studies on subjects with brain injuries and mental disorders are effective to show the consequences if certain brain functions are lost or impaired, to explore and validate effects that the monotonous data are inadequate. To increase the variety of brain big data, the systematic studies should give thought to subjects with different genders, ages, health states, education backgrounds, and so forth.
Fig. 2.5: Systematic investigations with different types of subjects. The feature of variety of big data is more likely to further our understanding on the data. Thus, systematic studies should give thought to subjects with different genders, ages, health states, education backgrounds, and so forth.
(2) Systematic design of cognitive experiments.
Besides the top-down principle for constructing the intact brain big data sets, BI asks for a systematic consideration on the design of single experiment as well. To reduce the cost for conducting the experiments and maximize each subject‘s contribution, BI investigations collect as many as possible data for each time. For instance, it is
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Maebashi Institute of Technology, Doctor Dissertation of Engineering
Yang Yang: A Brain Informatics-Based Study on Human Cognition, Emotion, and Their Relationship
during the implementation of fMRI experiment for each time (see Figure 2.6).
Functional image is the primary target of the data collection. The underlying neural correlates of the ―thinking‖ in which the subject is engaged can be exhibited by the well-designed cognitive tasks. Meanwhile, the resting-state fMRI (rs-fMRI) has been pervasively employed to investigate the spontaneous neural fluctuations of human brain in the past few years. It has been suggested that spatial patterns and nodal graph properties in the major brain functional networks, e.g., the default mode network (DMN), might vary across the rest period before an on-task state, the on-task state, and the rest period after the on-task state (Wang et al., 2012). Thus, fMRI data over three sequential periods are measured as the routine, including pre-task resting, task-on with active and passive tasks, and post-task resting. Behavioral assessment displays the behavioral responses of subjects which provide the explicit reference for interpreting causality between internal underpinnings and external expressions. Structural images for each subject taken in fMRI experiments consist of a T1 weighted 3D image taken by the magnetization prepared rapid gradient echo (MPRAGE) sequence and a diffusion image based on diffusion tensor imaging (DTI) technique. The T1 image is supplied as a reference for the co-registration of the local structural space and the functional images.
Another use of T1 image is to enable the voxel-based morphological (VBM) analysis which is reliable to uncover the structural alterations in the brains of patients with psychiatric disorders (Ashburner and Friston, 2000). In brief, the collection of all types of available data in the fMRI experiments (especially the experiments with patients) allows the multi-angle mining of valuable information to explain the complex brain activities.
(3) Systematic human brain data management.
The development of brain science has led to a vast increase of brain data. To meet
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requirements of a systematic methodology of BI, a new conceptual model of brain data was proposed, namely Data-Brain, which explicitly represents various relationships among multiple human brain data sources, with respect to all major aspects and capabilities of human information processing systems (HIPS) (Zhong and Chen, 2012).
A multi-dimension framework and a BI methodology based ontological modeling approach have been developed to implement a Data-Brain (see Figure 2.7). The Data-Brain, Data-Brain based BI provenances, and heterogeneous brain data can be used to construct a Data-Brain based brain data center which provides a global framework to integrate data, information and knowledge coming from the whole research process for systematic BI study. Such a Data-Brain modeling approach represents a radically new way for domain-driven conceptual modeling of brain data, which models a whole process of systematically investigating human information processing mechanisms.
Fig. 2.6: Systematic consideration on the design of single fMRI experiment. It is necessary to involve behavior assessment, structural images, and functional images during the implementation of fMRI experiment for each time.
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Maebashi Institute of Technology, Doctor Dissertation of Engineering
Yang Yang: A Brain Informatics-Based Study on Human Cognition, Emotion, and Their Relationship
Fig. 2.7: Illustration of the Data-Brain. The Data-Brain that is a conceptual model of brain data provides a global framework to integrate data, information and knowledge coming from the whole research process for systematic BI study.
(4) Systematic human brain data analysis and simulation.
With respect to the human brain, recent neuroimaging techniques, including fMRI, EEG, and MEG, now allow us to probe the brain at unprecedentedly high temporal or spatial resolution without the use of invasive techniques. Multiple advanced computational methods are used to investigate and solve algorithmic image computing problems in basic and applied neuroscience. Some common grounds can be found across the data analyzing techniques: statistical analysis of neuroimages is commonly approached with intragroup or intergroup comparisons made by repeated application of univariate or multivariate tests performed on the global brain or set of the regions of interest sampled in the acquired images (Turkheimer et al., 2000); standard group analyses of fMRI data rely on spatial and temporal averaging of individuals. However, particular approaches are also necessary to refine characteristic information from each
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modality of data. The heterogeneity of brain big data sources determines the complexity in the choice of suitable analyzing approaches. BI has proposed a frame of analysis for the systematic purpose. Taking the analysis of fMRI data as an example (see Figure 2.8), behavioral data, structural data, resting-state functional data, and on-task functional data are acquired corresponding to the systematic design of experiments. First of all, separate analyses can be carried out for each type of data. Regarding the behavioral data, subjects are divided into different groups based on the demographic information in intergroup studies to allow the further contrasts between groups (this stage is skipped in intragroup studies). Averages and deviations can be calculated across all subjects over the scores of questionnaires, as well as the accuracy and reaction time of behavioral responses (e.g., button pressing) recorded when the experiment was performed. For the structural data, the VBM analysis can be employed on the T1 images to check whether altered volume of brain regions (mainly for gray matter) occurred. DTI data can be used for tracing the white matter tracts via both deterministic and probabilistic methods, as well as computing the global fractional anisotropy, to examine the possible abnormalities in microcircuit and impairments in data transmission inside the brain. In regard to the on-task functional data, brain regions with both increased and decreased activities during specific task can be identified relative to the baseline. The blood-oxygen-level dependent (BOLD) signals of regions of interest (ROI) can be used for the ROI analysis, e.g., observation and comparison on the shape of BOLD signals extracted from different ROIs, or from the same set of ROIs but during different experimental conditions. Another purpose of extracting BOLD signals is to fulfill the comparisons between observed data and simulated data. The simulation of neuroimaging data is helpful to enhance our strength in verifying the brain dynamics