Journal of Neuroscience Methods 169 (2008) 249–254 Short communication Network homogeneity reveals decreased integrity of default-mode network in ADHD Lucina Q. Uddin a,∗ , A.M. Clare Kelly a , Bharat B. Biswal b , Daniel S. Margulies a , Zarrar Shehzad a , David Shaw c , Manely Ghaffari a , John Rotrosen c , Lenard A. Adler c , F. Xavier Castellanos a,d , Michael P. Milham a,∗ a The Phyllis Green and Randolph C¯ owen Institute for Pediatric Neuroscience, New York University Child Study Center, New York, NY 10016, USA b Department of Radiology, University of Medicine and Dentistry of New Jersey, Newark, NJ 07101, USA c Department of Psychiatry, NYU School of Medicine and VA NYHHS, New York, NY 10010, USA d Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY 10962, USA Received 17 October 2007; received in revised form 28 November 2007; accepted 28 November 2007 Abstract Examination of spontaneous intrinsic brain activity is drawing increasing interest, thus methods for such analyses are rapidly evolving. Here we describe a novel measure, “network homogeneity”, that allows for assessment of cohesiveness within a specified functional network, and apply it to resting-state fMRI data from adult ADHD and control participants. We examined the default mode network, a medial-wall based network characterized by high baseline activity that decreases during attention-demanding cognitive tasks. We found reduced network homogeneity within the default mode network in ADHD subjects compared to age-matched controls, particularly between the precuneus and other default mode network regions. This confirms previously published results using seed-based functional connectivity measures, and provides further evidence that altered precuneus connectivity is involved in the neuropathology of ADHD. Network homogeneity provides a potential alternative method for assessing functional connectivity of specific large-scale networks in clinical populations. © 2007 Elsevier B.V. All rights reserved. Keywords: fMRI; Resting state networks; Functional connectivity; Precuneus; Posterior cingulate; Attention; Attention-deficit/hyperactivity disorder 1. Introduction The advent of new methods for analyzing functional neu- roimaging data in the resting state has enabled the investigation of previously overlooked aspects of intrinsic network organi- zation. In particular, investigators have identified spontaneous coherent fluctuations in functionally distinct networks even in the absence of specific cognitive instruction (De Luca et al., 2006; Fox et al., 2006; Vincent et al., 2006). This “cogni- tively unbiased” approach appears to be particularly relevant for the study of psychopathological populations, with several recent reports noting disruptions in such intrinsic organization (Greicius et al., 2004; Garrity et al., 2007). ∗ Corresponding authors at: NYU Child Study Center, 215 Lexington Avenue, 14th Floor, New York, NY 10016, USA. Tel.: +1 917 572 2268/212 263 3697; fax: +1 212 263 4675. E-mail address: milham01@med.nyu.edu (M.P. Milham). Assessment of resting brain networks can be accomplished using several recently developed methods, although two are most widely employed. These are independent components analy- sis (ICA) and region-of-interest (ROI) seed-based correlation approaches, each of which has strengths and shortcomings. ICA is a model-free approach whereby a two-dimensional (time points x voxels) data matrix is decomposed into a set of indepen- dent timeseries and consequently associated spatial maps which describe the temporal and spatial characteristics of the underly- ing signals (components) (Beckmann et al., 2005). While ICA has the power to estimate largely overlapping spatial processes, there is no clear consensus as to how best to compare compo- nents across subjects and/or between groups (Fox and Raichle, 2007) (see (Beckmann and Smith, 2004; Calhoun et al., 2005) for recent advances). Seed-based approaches involve using correla- tion or regression analyses to determine the temporal coherence between the timeseries for a particular voxel or ROI and the timeseries of all other voxels in the brain in order to identify temporally coherent “functionally connected” networks (Biswal 0165-0270/$ – see front matter © 2007 Elsevier B.V. All rights reserved. doi:10.1016/j.jneumeth.2007.11.031