Connectivity strength‐weighted sparse group representation‐based brain network construction for MCI classification

Abstract

Brain functional network analysis has shown great potential in understanding brain functions and also in identifying biomarkers for brain diseases, such as Alzheimer's disease (AD) and its early stage, mild cognitive impairment (MCI). In these applications, accurate construction of biologically meaningful brain network is critical. Sparse learning has been widely used for brain network construction; however, its l1-norm penalty simply penalizes each edge of a brain network equally, without considering the original connectivity strength which is one of the most important inherent linkwise characters. Besides, based on the similarity of the linkwise connectivity, brain network shows prominent group structure (i.e., a set of edges sharing similar attributes). In this article, we propose a novel brain functional network modeling framework with a “connectivity strength-weighted sparse group constraint.” In particular, the network modeling can be optimized by considering both raw connectivity strength and its group structure, without losing the merit of sparsity. Our proposed method is applied to MCI classification, a challenging task for early AD diagnosis. Experimental results based on the resting-state functional MRI, from 50 MCI patients and 49 healthy controls, show that our proposed method is more effective (i.e., achieving a significantly higher classification accuracy, 84.8%) than other competing methods (e.g., sparse representation, accuracy = 65.6%). Post hoc inspection of the informative features further shows more biologically meaningful brain functional connectivities obtained by our proposed method.

A toolbox for brain network construction and classification (BrainNetClass)

“…From the methodological point of view, such a brain functional network‐based classification is essentially a pattern recognition problem, where discriminative features (e.g., FC links or network properties) can be jointly learned from the brain networks and weighted in a multivariate manner toward a classification outcome. It not only helps with better patient‐control separation but also benefits imaging biomarker detection for better understanding brain diseases (Jie et al, ; Yu et al, ; Zhang, Zhang, et al, ).…”

Decoding brain functional connectivity implicated in AD and MCI

“…We compared the performance of our classifier with several studies performed for classification of AD and its prodromal stage, MCI, using functional connectivity features [1,17,2,7,11,16]. By evaluating and selecting features leading to classification, we made the following novel contributions:…”

Quantification of Structural Brain Connectivity via a Conductance Model

“…As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wpcontent/uploads/how to apply/ADNI Acknowledgement List.pdf 2009; Prasad et al, 2015;Yu et al, 2017), late-life major depressive disorder (Smagula and Aizenstein, 2016), epilepsy (Taylor et al, 2015), Parkinsons's disease (Canu et al, 2015;Shah et al, 2017), schizophrenia (Arbabshirani et al, 2013;Cabral et al, 2013) and other psychosis (van Dellen et al, 2015;Mighdoll et al, 2015). Brain connectivity analysis has also proven to be useful in the study of the effects of aging on the brain (Damoiseaux, 2017;Wu et al, 2013;Salat, 2011;Fjell et al, 2016).…”

“…Differences in brain connectivity patterns between healthy and diseased populations are an indicator of change in the brain "wiring" and function due to the disease. In particular, AD has been found to impact connectivity (Rose et al, 2000;Buckner et al, 2005Buckner et al, , 2009Prasad et al, 2015;Yu et al, 2017). The progressive neurodegeneration suffered in AD, possibly caused by the spread and accumulation of misfolded proteins along structural connections in the brain (Iturria-Medina et al, 2014), affects the functional networks detected with rs-fMRI (Buckner et al, 2005(Buckner et al, , 2009Yu et al, 2017) and the brain connections reconstructed with dMRI (Rose et al, 2000;Prasad et al, 2015).…”