Table_1_Functional Connectivity of the Chemosenses: A Review.docx

Functional connectivity approaches have long been used in cognitive neuroscience to establish pathways of communication between and among brain regions. However, the use of these analyses to better understand how the brain processes chemosensory information remains nascent. In this review, we conduct a literature search of all functional connectivity papers of olfaction, gustation, and chemesthesis, with 103 articles discovered in total. These publications largely use approaches of seed-based functional connectivity and psychophysiological interactions, as well as effective connectivity approaches such as Granger Causality, Dynamic Causal Modeling, and Structural Equation Modeling. Regardless of modality, studies largely focus on elucidating neural correlates of stimulus qualities such as identity, pleasantness, and intensity, with task-based paradigms most frequently implemented. We call for further “model free” or data-driven approaches in predictive modeling to craft brain-behavior relationships that are free from a priori hypotheses and not solely based on potentially irreproducible literature. Moreover, we note a relative dearth of resting-state literature, which could be used to better understand chemosensory networks with less influence from motion artifacts induced via gustatory or olfactory paradigms. Finally, we note a lack of genomics data, which could clarify individual and heritable differences in chemosensory perception.</p

Table_2_Functional Connectivity of the Chemosenses: A Review.docx

Functional connectivity approaches have long been used in cognitive neuroscience to establish pathways of communication between and among brain regions. However, the use of these analyses to better understand how the brain processes chemosensory information remains nascent. In this review, we conduct a literature search of all functional connectivity papers of olfaction, gustation, and chemesthesis, with 103 articles discovered in total. These publications largely use approaches of seed-based functional connectivity and psychophysiological interactions, as well as effective connectivity approaches such as Granger Causality, Dynamic Causal Modeling, and Structural Equation Modeling. Regardless of modality, studies largely focus on elucidating neural correlates of stimulus qualities such as identity, pleasantness, and intensity, with task-based paradigms most frequently implemented. We call for further “model free” or data-driven approaches in predictive modeling to craft brain-behavior relationships that are free from a priori hypotheses and not solely based on potentially irreproducible literature. Moreover, we note a relative dearth of resting-state literature, which could be used to better understand chemosensory networks with less influence from motion artifacts induced via gustatory or olfactory paradigms. Finally, we note a lack of genomics data, which could clarify individual and heritable differences in chemosensory perception.</p

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

Map showing brain areas where there is a main effect of age on degree of connectivity in the subset of data where age was not correlated with head motion, displayed at a whole brain corrected p<0.05 level.

<p>Red/yellow areas indicate regions where connectivity increases with age, while blue/purple areas indicate regions where connectivity decreases with age. Slices are shown using radiological convention (i.e. left is on the right).</p

Areas showing increased connectivity to the medial temporal ROI with age.

<p>Slices are displayed using radiological convention (i.e., left is on the right).</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

Regions with a positive relationship between degree of connectivity and subject age.

<p>Coordinates indicate center of mass of region in Talairach space.</p

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

Regions with a negative relationship between degree of connectivity and subject age.

<p>Coordinates indicate center of mass of region in Talairach space.</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