An Independent Component Analysis Based Tool for Exploring Functional Connections in the Brain

This thesis describes the use of independent component analysis (ICA) as a measure of voxel similarity, which allows the user to find and view statistically independent maps of correlated voxel activity. The tool developed in this work uses a specialized clustering technique, designed to find and characterize clusters of activated voxels, to compare the independent component spatial maps across patients. This same method is also used to compare SPM results across patients

Auto detection in autism

Autism is a neurobiological disorder in which, certain regions of the brain are affected. The main features of autism are impairment in communication, social interaction, language and deficit in imitation and theory of mind. Using Functional Magnetic Resonance Imaging (fMRI), haemodynamic responses during a bilateral finger tapping task are analyzed for both autistic subjects and normal control subjects. fMRI is a noninvasive technique to image the activity of the brain related to a specific task. Generally, the active voxels in the IMRI images are detected using parametric or non-parametric statistical methods in which the fMRI response is assumed to have a model. Such methods are not applicable to detect the active voxels when the fMRI response is unknown. The data driven methods are also used for analyzing the fMRI data. The data driven methods are computationally expensive. In this study, a method for detecting activated voxels without using prior knowledge of the input stimulus is presented. The assumption in this method is that the activation typically involves larger region comprising of several voxels and that these neighboring activated voxels are also temporally correlated. To validate the accuracy of this method, Principal component Analysis and Independent Component Analysis are also performed. A significant overlap in the sensorimotor cortex is found between the various methods suggesting that the automatic detecting method presented does provide accurate detection and localization

Learning and comparing functional connectomes across subjects

Functional connectomes capture brain interactions via synchronized fluctuations in the functional magnetic resonance imaging signal. If measured during rest, they map the intrinsic functional architecture of the brain. With task-driven experiments they represent integration mechanisms between specialized brain areas. Analyzing their variability across subjects and conditions can reveal markers of brain pathologies and mechanisms underlying cognition. Methods of estimating functional connectomes from the imaging signal have undergone rapid developments and the literature is full of diverse strategies for comparing them. This review aims to clarify links across functional-connectivity methods as well as to expose different steps to perform a group study of functional connectomes

Does higher sampling rate (multiband + SENSE) improve group statistics - An example from social neuroscience block design at 3T

Multiband (MB) or Simultaneous multi-slice (SMS) acquisition schemes allow the acquisition of MRI signals from more than one spatial coordinate at a time. Commercial availability has brought this technique within the reach of many neuroscientists and psychologists. Most early evaluation of the performance of MB acquisition employed resting state fMRI or the most basic tasks. In this study, we tested whether the advantages of using MB acquisition schemes generalize to group analyses using a cognitive task more representative of typical cognitive neuroscience applications. Twenty-three subjects were scanned on a Philips 3 ​T scanner using five sequences, up to eight-fold acceleration with MB-factors 1 to 4, SENSE factors up to 2 and corresponding TRs of 2.45s down to 0.63s, while they viewed (i) movie blocks showing complex actions with hand object interactions and (ii) control movie blocks without hand object interaction. Data were processed using a widely used analysis pipeline implemented in SPM12 including the unified segmentation and canonical HRF modelling. Using random effects group-level, voxel-wise analysis we found that all sequences were able to detect the basic action observation network known to be recruited by our task. The highest t-values were found for sequences with MB4 acceleration. For the MB1 sequence, a 50% bigger voxel volume was needed to reach comparable t-statistics. The group-level t-values for resting state networks (RSNs) were also highest for MB4 sequences. Here the MB1 sequence with larger voxel size did not perform comparable to the MB4 sequence. Altogether, we can thus recommend the use of MB4 (and SENSE 1.5 or 2) on a Philips scanner when aiming to perform group-level analyses using cognitive block design fMRI tasks and voxel sizes in the range of cortical thickness (e.g. 2.7 ​mm isotropic). While results will not be dramatically changed by the use of multiband, our results suggest that MB will bring a moderate but significant benefit