Comparison of coupled-ICD and conventional approaches for detecting group-by-condition interaction for cocaine-dependent subjects (CD) and healthy controls (HC).

<p>As the comparison between CD and HC subjects involves contrasting a metric (coupled-ICD) that already measures the difference between two conditions, this result can be interpreted in a similar manner to the interaction term of a classic 2×2 two-way ANOVA. A) Coupled-ICD detected more widespread significant interactions than the two conventional approaches, B) ICD and C) wGBC. D) ROI-based matrix connectivity method also detects widespread interaction between group and condition provide support that the coupled-ICD results are not simply artifacts. Only edges that were significantly difference at p<0.05 with FDR correction are shown. The size of the node is proportional to the number of significantly different edges touching that node. A larger node has more significantly different edges.</p

Flow chart describing coupled-ICD.

<p>For data consisting of paired conditions, coupled-ICD jointly analyzes both conditions and then creates a summary of the difference in connectivity between conditions for each voxel. First, a “seed” connectivity map is created for a voxel (shown as the blue square through the flow chart) in each condition. The resulting “seed” maps are then subtracted and a histogram of the differences is computed. The survival function of the distribution of the difference (labeled as coupled-ICD curve) is calculated and modeled with a stretched exponential. This process is repeated for each voxel in the gray matter. The final output is an image where each voxel represents a summary of the difference between two “seed” maps using that voxel as the seed region.</p

Coupled Intrinsic Connectivity Distribution Analysis: A Method for Exploratory Connectivity Analysis of Paired fMRI Data

<div><p>We present a novel voxel-based connectivity approach for paired functional magnetic resonance imaging (fMRI) data collected under two different conditions labeled the Coupled Intrinsic Connectivity Distribution (coupled-ICD). Our proposed method jointly models both conditions to incorporate additional paired information into the connectivity metric. Voxel-based connectivity holds promise as a clinical tool to characterize a wide range of neurological and psychiatric diseases, and monitor their treatment. As such, examining paired connectivity data such as scans acquired pre- and post-intervention is an important application for connectivity methodologically. When presented with data from paired conditions, conventional voxel-based methods analyze each condition separately. However, summarizing each connection separately can misrepresent patterns of changes in connectivity. We show that commonly used methods can underestimate functional changes and subsequently introduce and evaluate our solution to this problem, the coupled-ICD metric, using two studies: 1) healthy controls scanned awake and under anesthesia, and 2) cocaine-dependent subjects and healthy controls scanned while being presented with relaxing or drug-related imagery cues. The coupled-ICD approach detected differences between paired conditions in similar brain regions as the conventional approaches while also revealing additional changes in regions not identified using conventional voxel-based connectivity analyses. Follow-up seed-based analyses on data independent from the voxel-based results also showed connectivity differences between conditions in regions detected by coupled-ICD. This approach of jointly analyzing paired resting-state scans provides a new and important tool with many applications for clinical and basic neuroscience research.</p></div

Examples of how conventional approaches that separately summarize each condition of a pair could misrepresent patterns of changes in connectivity.

<p>A) When a binary graph is used, changes in correlation near the threshold value (threshold ) can lead to an over/under-estimation of connectivity changes. In this example, one edge increases its correlation by 0.02 in between conditions 1 and 2, which leads an increase in degree for condition 2. However, this increase in correlation and degree is likely not meaningful. B) When a weighted graph is used, increases and decreases in connectivity between conditions could cancel each other out. In this example, half of a node's edges increase their correlation while half of its edges decrease their correlation in condition 2 compared to condition 1. When all edges are averaged together, no change between the conditions is detected, despite that a change is clearly present.</p

Comparison of coupled-ICD and conventional approaches for detecting connectivity changes due to anesthesia.

<p>Regions of large A) increased connectivity and B) decreased connectivity under anesthesia detected by coupled-ICD and thresholded using the Top Percent method. For some regions, coupled-ICD was able to detect regions with both increased <i>and</i> decreased connectivity. One of these areas (the right parietal lobe; black circle) was used for further analysis. Regions of significant change in connectivity detected by conventional voxel-based approaches are shown in C) ICD and D) wGBC. While a general correspondence was observed between all methods, the coupled-ICD results suggest a decrease in connectivity for the left frontal lobe (black circle) while the conventional approaches suggest an increase in connectivity. This region was selected for further analysis. While the conventional voxel-based approaches, C) ICD and D) wGBC, suggested more focal changes in connectivity, E) matrix connectivity and coupled-ICD suggest more widespread changes due to anesthesia. Only edges that were significantly difference at p<0.05 with FDR correction are shown. The size of the node is proportional to the number of significantly different edges touching that node such that a larger node has more significantly different edges.</p

Seed-based connectivity results using seeds detected by coupled-ICD.

<p>A) Several regions of the brain displayed evidence for both increased and decreased connectivity during the anesthesia state as detected by coupled-ICD. A follow-up seed-based analysis on independent data for one of these regions (the right parietal lobe; green region) revealed both significant (p<0.05 corrected) increases and decreases in connectivity to this region, echoing the coupled-ICD results. B) For some regions of the brain, coupled-ICD and conventional approaches showed seemingly conflicting results, with coupled-ICD suggesting decreased connectivity while conventional approaches suggest increased connectivity due to anesthesia. Seed connectivity for one of these regions (the left frontal lobe; green region) revealed both significant (p<0.05 corrected) increased and decreased connectivity, demonstrating that the different approaches may be sensitive to different aspects of changes in connectivity.</p

Follow-up seed-based connectivity results using a seed detected by coupled-ICD.

<p>A follow-up, seed-based connectivity analysis was performed using a region in the left putamen detected by coupled-ICD but not by conventional ICD and degree analysis. This region shows significant group interaction (p<0.05, corrected) in the caudate and nucleus accumbens. The subjects analyzed in the seed analysis were not used in the voxel-based analysis, providing additional evidence of coupled-ICD robustness and utility. The green region shows the location of the seed ROI.</p

Dryad BioLINCC Survey Data 16-09-01

This is the deidentified data from the 2015 cross-sectional survey of investigators who requested and received access to clinical research data from BioLINCC between 2007 and 2014

Data Dictionary BioLINCC Survey 16-09-01

This file lists and describes the variables from the 2015 cross-sectional BioLINCC survey