Support vector machine classification and characterization of age-related reorganization of functional brain networks

Abstract

Most of what is known about the reorganization of functional brain networks that accompanies normal aging is based on neuroimaging studies in which participants perform specific tasks. In these studies, reorganization is defined by the differences in task activation between young and old adults. However, task activation differences could be the result of differences in task performance, strategy, or motivation, and not necessarily reflect reorganization. Resting-state fMRI provides a method of investigating functional brain networks without such confounds. Here, a support vector machine (SVM) classifier was used in an attempt to differentiate older adults from younger adults based on their resting-state functional connectivity. In addition, the information used by the SVM was investigated to see what functional connections best differentiated younger adult brains from older adult brains. Three separate resting-state scans from 26 younger adults (18-35 yrs) and 26 older adults (55-85) were obtained from the International Consortium for Brain Mapping (ICBM) dataset made publically available in the 1000 Functional Connectomes project www.nitrc.org/projects/fcon_1000. 100 seed-regions from four functional networks with 5 mm3 radius were defined based on a recent study using machine learning classifiers on adolescent brains. Time-series for every seed-region were averaged and three matrices of z-transformed correlation coefficients were created for each subject corresponding to each individual’s three resting-state scans. SVM was then applied using leave-one-out cross-validation. The SVM classifier was 84% accurate in classifying older and younger adult brains. The majority of the connections used by the classifier to distinguish subjects by age came from seed-regions belonging to the sensorimotor and cingulo-opercular networks. These results suggest that age-related decreases in positive correlations within the cingulo-opercular and default networks, and decreases in negative correlations between the default and sensorimotor networks, are the distinguishing characteristics of age-related reorganization.

Bayesian exponential random graph modeling of whole-brain structural networks across lifespan

“…local clustering and global efficiency) over all age categories, in contrast with previous literature showing alterations in both structural (Dennis et al, 2013;Gong et al, 2009;Hagmann et al, 2010;Montembeault et al, 2012;Otte et al, 2015;Wu et al, 2012;Zhu et al, 2012) and functional brain networks across life-span (Achard and Bullmore, 2007;Betzel et al, 2014;Meier et al, 2012;Meunier et al, 2009;Nathan Spreng and Schacter, 2012;Simpson and Laurienti, 2015;Smit et al, 2016;Wang et al, 2012). Furthermore, our findings are also in contrast with significant differences found in a previous exponential random graph modeling study in functional networks , and a recently developed similar approach (also discussed below) which revealed differences in functional networks across the lifespan, such as older adults having stronger connections between highly clustered nodes, or less assortativity in visual and multisensory regions (Simpson and Laurienti, 2015).…”

“…the average shortest path length, maximum betweenness centrality or overall clustering coefficient) (Bullmore and Sporns, 2009) and/or network properties such as small-worldness, rich club connectedness (Bullmore and Sporns, 2012;Cao et al, 2014) and modularity (Rubinov and Sporns, 2010). In the past decade, multiple studies have shown that normal aging is associated with substantial alterations in NeuroImage 135 (2016) [79][80][81][82][83][84][85][86][87][88][89][90][91] structural Dennis et al, 2013;Gong et al, 2009;Hagmann et al, 2010;Lim et al, 2015;Montembeault et al, 2012;Otte et al, 2015;Wu et al, 2012;Zhu et al, 2012) and functional (Achard and Bullmore, 2007;Andrews-Hanna et al, 2007;Betzel et al, 2014;Meier et al, 2012;Meunier et al, 2009;Nathan Spreng and Schacter, 2012;Wang et al, 2012) brain networks. Some of these studies focused on specific age categories: childhood to adulthood (Dennis et al, 2013;Hagmann et al, 2010) or young and older adults (e.g.…”

Age differences in brain activity related to unsuccessful declarative memory retrieval

“…In contrast, during EM and SM trials, the aIFO seeds were functionally connected with premotor and temporal areas. It is not clear what type of mechanism this pattern reflects, but it may indicate that different search processes are engaged when participants struggle to retrieve a non-personal memory.We found no evidence for age differences in the strength of functional connectivity within the core SLN, either during the incorrect trials or in the estimate of intrinsic functional connectivity, although a number of studies have reported such age differences (Allen et al, 2011; Geerligs et al, in press;He et al, 2014;Meier et al, 2012;Onoda et al, 2012;Tomasi and Volkow, 2012). Although we did not find age differences in the primary pattern of connectivity within the SLN, we did see reductions in the strength of connectivity between the aIFO seeds and regions outside the SLN, as well as a lack of task-specificity in the pattern of connectivity across the three memory conditions in older adults.…”

“…Age differences in functional connectivity have been examined extensively for the default network (e.g., Andrews-Hanna et al, 2007;Damoiseaux et al, 2008;Grady et al, 2012), and to some extent for other brain networks (Allen et al, 2011;Campbell et al, 2012;Onoda et al, 2012;Rieckmann et al, 2011;Thomas et al, 2013;Tomasi and Volkow, 2012;Voss et al, 2010). Weaker functional connectivity has been reported specifically within the SLN or in SLN nodes in older adults (Allen et al, 2011;Geerligs et al, in press;He et al, 2014;Meier et al, 2012;Onoda et al, 2012;Tomasi and Volkow, 2012), leading us to predict age-related reductions in SLN functional connectivity in our results, although connectivity within the striatum, a node of the SLN, has been shown to increase with age (Allen et al, 2011;Tomasi and Volkow, 2012;Wang et al, 2012). However, most of these earlier studies examined resting-state functional connectivity, and it is unclear whether similar age reductions would be found during our tasks, on which there were no age differences in overall accuracy or response times (St-Laurent et al, 2011).…”

Multivariate Classification of Blood Oxygen Level-Dependent fMRI Data with Diagnostic Intention: A Clinical Perspective

“…However, misclassification might also be based on biologically and medically meaningful information like the effects of medication 110 and age. 69,85 Sex effects are a much-debated issue in fMRI as well. 111,112 Further investigation of misclassified subjects might even pose a starting point to identify biologically different disease subgroups.…”