Data mining in brain imaging

Graph Neural Networks for Parkinson's Disease Monitoring and Alerting

Graph neural networks (GNNs) have been increasingly employed in the field of Parkinson's disease (PD) research. The use of GNNs provides a promising approach to address the complex relationship between various clinical and non-clinical factors that contribute to the progression of PD. This review paper aims to provide a comprehensive overview of the state-of-the-art research that is using GNNs for PD. It presents PD and the motivation behind using GNNs in this field. Background knowledge on the topic is also presented. Our research methodology is based on PRISMA, presenting a comprehensive overview of the current solutions using GNNs for PD, including the various types of GNNs employed and the results obtained. In addition, we discuss open issues and challenges that highlight the limitations of current GNN-based approaches and identify potential paths for future research. Finally, a new approach proposed in this paper presents the integration of new tasks for the engineering of GNNs for PD monitoring and alert solutions.

BIARAM: A process for analyzing correlated brain regions using association rule mining

BACKGROUND AND OBJECTIVE

Because examining correlated (vs. individual) brain activity is a superior method for locating neural correlates of a stimulus, using a network approach for analyzing brain activity is gaining interest. In this study, we propose and illustrate the use of association rule mining (ARM) to analyze brain regions that are activated simultaneously. ARM is commonly used in marketing and other disciplines to help determine items that might be purchased together. We apply this technique toward identifying correlated brain regions that may respond simultaneously to specific stimuli. Our objective is to introduce ARM, describe a process for converting neural images into viable datasets (for analyses), and suggest how to apply this process for generating insights about the brain's responses to specific stimuli (e.g. technology-associated interruptions).

METHODS

We analyze electroencephalogram (EEG) data collected from 46 participants; convert brain waves into images via a source localization algorithm known as sLORETA (i.e., standardized low-resolution brain electromagnetic tomography); reorganize these into a "transactional" dataset; and generate association rules through ARM.

RESULTS

We compare the results with more conventional methods for analyzing neuroimaging data. We show that there is a stronger correlation between frontal lobe and sublobar/insula regions after interruptions. This result would not be obvious from independent analysis of each region.

CONCLUSIONS

The main contribution of this paper is introducing ARM as a method for analyzing multiple images. We suggest that the biomedical community may apply this commonly available data mining technique to develop further insights about correlated regions affected by specific stimuli.

Graphical neuroimaging informatics: application to Alzheimer's disease

The Informatics Visualization for Neuroimaging (INVIZIAN) framework allows one to graphically display image and meta-data information from sizeable collections of neuroimaging data as a whole using a dynamic and compelling user interface. Users can fluidly interact with an entire collection of cortical surfaces using only their mouse. In addition, users can cluster and group brains according in multiple ways for subsequent comparison using graphical data mining tools. In this article, we illustrate the utility of INVIZIAN for simultaneous exploration and mining a large collection of extracted cortical surface data arising in clinical neuroimaging studies of patients with Alzheimer's Disease, mild cognitive impairment, as well as healthy control subjects. Alzheimer's Disease is particularly interesting due to the wide-spread effects on cortical architecture and alterations of volume in specific brain areas associated with memory. We demonstrate INVIZIAN's ability to render multiple brain surfaces from multiple diagnostic groups of subjects, showcase the interactivity of the system, and showcase how INVIZIAN can be employed to generate hypotheses about the collection of data which would be suitable for direct access to the underlying raw data and subsequent formal statistical analysis. Specifically, we use INVIZIAN show how cortical thickness and hippocampal volume differences between group are evident even in the absence of more formal hypothesis testing. In the context of neurological diseases linked to brain aging such as AD, INVIZIAN provides a unique means for considering the entirety of whole brain datasets, look for interesting relationships among them, and thereby derive new ideas for further research and study.

A performance evaluation framework for association mining in spatial data

The evaluation of the process of mining associations is an important and challenging problem in database systems and especially those that store critical data and are used for making critical decisions. Within the context of spatial databases we present an evaluation framework in which we use probability distributions to model spatial regions, and Bayesian networks to model the joint probability distribution and the structural relationships among spatial and non-spatial predicates. We demonstrate the applicability of the proposed framework by evaluating representatives from two well-known approaches that are used for learning associations, i.e., dependency analysis (using statistical tests of independence) and Bayesian methods. By controlling the parameters of the framework we provide extensive comparative results of the performance of the two approaches. We obtain measures of recovery of known associations as a function of the number of samples used, the strength, number and type of associations in the model, the number of spatial predicates associated with a particular non-spatial predicate, the prior probabilities of spatial predicates, the conditional probabilities of the non-spatial predicates, the image registration error, and the parameters that control the sensitivity of the methods. In addition to performance we investigate the processing efficiency of the two approaches.

An improved brain image classification technique with mining and shape prior segmentation procedure

The shape prior segmentation procedure and pruned association rule with ImageApriori algorithm has been used to develop an improved brain image classification system are presented in this paper. The CT scan brain images have been classified into three categories namely normal, benign and malignant, considering the low-level features extracted from the images and high level knowledge from specialists to enhance the accuracy in decision process. The experimental results on pre-diagnosed brain images showed 97% sensitivity, 91% specificity and 98.5% accuracy. The proposed algorithm is expected to assist the physicians for efficient classification with multiple key features per image.

Morphometric analysis of brain images with reduced number of statistical tests: a study on the gender-related differentiation of the corpus callosum

OBJECTIVE

We evaluate the feasibility of applying dynamic recursive partitioning (DRP), an image analysis technique, to perform morphometric analysis. We apply DRP to detect and characterize discriminative morphometric characteristics between anatomical brain structures from different groups of subjects. Our method reduces the number of statistical tests, commonly required by pixel-wise statistics, alleviating the effect of the multiple comparison problem.

METHODS AND MATERIALS

The main idea of DRP is to partition the two-dimensional (2D) image adaptively into progressively smaller subregions until statistically significant discriminative regions are detected. The partitioning process is guided by statistical tests applied on groups of pixels. By performing statistical tests on groups of pixels rather than on individual pixels, the number of statistical tests is effectively reduced. This reduction of statistical tests restricts the effect of the multiple comparison problem (i.e., type-I error). We demonstrate an application of DRP for detecting gender-related morphometric differentiation of the corpus callosum. DRP was applied to template deformation fields computed from registered magnetic resonance images of the corpus callosum to detect regions of significant expansion or contraction between female and male subjects.

RESULTS

DRP was able to detect regions comparable to those of pixel-wise analysis, while reducing the number of required statistical tests up to almost 50%. The detected regions were in agreement with findings previously reported in the literature. Statistically significant discriminative morphological variability was detected in the posterior corpus callosum region, the isthmus and the anterior corpus callosum. In addition, by operating on groups of pixels, DRP appears to be less prone to detecting spatially diffused and isolated outlier pixels as significant.

CONCLUSION

DRP can be a viable approach for detecting discriminative morphometric characteristics among groups of subjects, having the potential to alleviate the multiple comparisons' effect by significantly reducing the number of required statistical tests.

Data mining through simulation

Data integration is particularly difficult in neuroscience; we must organize vast amounts of data around only a few fragmentary functional hypotheses. It has often been noted that computer simulation, by providing explicit hypotheses for a particular system and bridging across different levels of organization, can provide an organizational focus, which can be leveraged to form substantive hypotheses. Simulations lend meaning to data and can be updated and adapted as further data come in. The use of simulation in this context suggests the need for simulator adjuncts to manage and evaluate data. We have developed a neural query system (NQS) within the NEURON simulator, providing a relational database system, a query function, and basic data-mining tools. NQS is used within the simulation context to manage, verify, and evaluate model parameterizations. More importantly, it is used for data mining of simulation data and comparison with neurophysiology.

Independent component analysis of functional MRI: what is signal and what is noise?

Many sources of fluctuation contribute to the functional magnetic resonance imaging (fMRI) signal, complicating attempts to infer those changes that are truly related to brain activation. Unlike methods of analysis of fMRI data that test the time course of each voxel against a hypothesized waveform, data-driven methods, such as independent component analysis and clustering, attempt to find common features within the data. This exploratory approach can be revealing when the brain activation is difficult to predict beforehand, such as with complex stimuli and internal shifts of activation that are not time-locked to an easily specified sensory or motor event. These methods can be further improved by incorporating prior knowledge regarding the temporal and spatial extent of brain activation.