1. Seed-based correlation studies Seed-based techniques have an a priori assumption that the node or region involved in the ICN is known and these regions are used to extract the low-frequency fluctuations in the BOLD signal used in further read more analysis. The seed regions may consist of individual voxels, small collections of voxels within a spheroid seed, or large functionally/anatomically derived regions of interest (for example, Brodmann areas). The low-frequency fluctuations within this defined seed or region can then be correlated with every other voxel in the brain in a voxel-wise exploratory analysis to understand the seed-to-brain connectivity (Figure ?(Figure1a)1a) or can be correlated only with another region or seed to analyze seed-to-seed connectivity.
The sensory-motor ICN was first demonstrated in fMRI by using a seed-based methodology , as was the prominent ICN known as the default mode network (DMN)  (see section B, ‘Task-free functional magnetic resonance imaging Alzheimer’s disease studies’). An example of the positive correlations in a seed-to-brain analysis using a spheroid seed of 6-mm radius in the posterior cingulate – MNI (Montreal Neurological Institute) coordinates = (-3, -51, 24) – is shown in Figure ?Figure1a,1a, and the negative correlations to the same seed are shown in Figure ?Figure2a2a. Figure 1 The task-negative network (TNN), also known as the default mode network (DMN), with both seed-based and low-dimensional independent component analysis (ICA) (20 components) in a group analysis of 341 elderly healthy control subjects.
(a) Regions with … Figure 2 The task-positive network (TPN) with both seed-based and low-dimensional independent component analysis (ICA) (20 components) in a group analysis of 341 elderly healthy control subjects. (a) Regions with negative correlations, also known as anti-correlations, … Alternatively, the correlations of the low-frequency fluctuations within a series of regions or seeds can be organized into a connectivity matrix (as shown in Figure ?Figure3)3) and subjected to graph theoretical or network analysis . Network analysis is a powerful tool that enables us to characterize the global as well as local functional connectivity characteristics of a group of nodes in the brain and provides us with a simple way to comprehensively compare the functional connectivity organization of the brain between patients and controls.
The network metrics that GSK-3 can be characterized are thoroughly discussed in  and include functional segregation, integration, and resilience of the network to insult. Figure 3 Extracting low-frequency fluctuations in a single subject’s preprocessed task-free functional magnetic resonance imaging (TF-fMRI) data within a series of selleck chemicals Oligomycin A seeds to be used in graph theoretical analyses.