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Supplemental Materials Hyperconnectivity is a Fundamental Response to Neurological Disruption by F. G. Hillary et al., 2014, Neuropsychology http://dx.doi.org/10.1037/neu0000110 Table S2 Connectivity Results for Moderate and Severe TBI Author Arenivas et al., 2012* Hillary et al., 2011b Marquez de la Plata et al., 2011* Palacios et al., 2013 Sharp et al., 2011 Author Hillary et al., 2011a Kasahara et al., 2010‡ Studies of ROIs/subnetworks for Rest only studies in TBI Analysis Sample size ROIs Whole-brain correlation, sub-network analysis, and between-node connection analysis 25 TBI, 17 HC 11 TBI, 11 HCs (2 time points) RSFC seed-based Hand-drawn ROI-based correlation ICA, seed-based in DMN 20 TBI, 17 HCs ICA and dual regression, task and rest 20 TBI, 20 HC 25 TBI, 16 HC Result MFC to PCC, MFC to LLPC, MFC to RLPC, PCC to LLPC, PCC to RLPC, and LLPC to RLPC - ACC, DLPFC, PCC, MedFC + Hippocampus, ACC, DLPFC DMN, Lpar Rpar DMN, PCC Studies of ROIs/subnetworks during task in TBI Analysis Sample size ROIs -/+ + + Result Extended unified structural equation modeling 12 TBI, 12 HCs PFC, Parietal, ACC + PPI analysis, motor task 12 TBI, 9 HCs SMA, Cb, M1 - LIPG and RIFG, Kasahara et al., 2011‡ Turner et al., 2011 Author Caeyenberghs et al., 2012** Caeyenberghs et al., 2013** Karmonik et al., 2013 Nakamura et al., 2009 Pandit et al., 2013 PPI analysis, working memory task PLS, effective connectivity 8 TBI, 8 HCs Whole brain analyses and Graph Theory Analysis Sample size Graph theory, partial correlation motor switching task Graph theory, local-global switching task Graph theory, delayed match-tosample task Graph theory, Partial correlation Graph theory, unweighted network - 9 TBI, 9 HCs 23 TBI, 26 HCs 17 TBI, 16 HCs 12 TBI, 12 HCs 6 TBI, 6 HCs (2 time points) 20 TBI, 21 HCs PFC, Parietal ROIs + Result Motor switching network, 22 ROIs Motor switching network, 22 ROIs Whole brain + Whole brain + Whole brain, PCC + + -/+ *,‡=identical sample, **different data set, all 2013 subjects appear in 2012, results not duplicated for Figure 2. Note: graph theory results based upon network strength or number of connections; data included in Figure 2. Abbreviations: ACC= anterior cingulate cortex, Cb= cerebellum, DLPFC=dorsolateral prefrontal cortex, DMN=default mode network, ECN = executive control network, HC= healthy control, LIPG= left inferior parietal gyrus, LLPC=left lateral parietal cortex, M1=primary motor cortex, MFC=medial frontal cortex, PCC= posterior cingulate cortex, PFC=prefrontal cortex, PLS= partial least squares, PPI- psychophysiological interaction, RIFG= right inferior frontal gyrus, RLPC=right lateral parietal cortex, SMA= supplementary motor cortex, SN= salience network, TBI= moderate and severe traumatic brain injury. Table S3 Connectivity Results for Multiple Sclerosis Author 3a: Studies of ROIs/subnetworks Rest Only Analysis Sample size 34 RR, 14 SP, 25 HCs Basile et al., 2013 ICA- RSFC Bonavita et al., 2011 ICA spatial ICA, dual regression Faivre et al., 2012 Gallo et al., 2012 Hawellek et al., 2011 Janssen ICA Whole-brain covariance 18 CI RR MS,18 CP RRMS, 18 HCs 13 Early RR-MS, 14 HCs 16 ON-MS, 14 nON-MS, 15 HCs 16 early stage MS, 16 HCs 28 RRMS, 28 HCs ICA, dual regression 16 MS, 16 HCs Koenig RSFC seed-based Leonardi PCA-eigenconnectivities Loitfelder et al., 2012 RSFC seed-based Rocca et al., 2012b ICA, RSFC Roosendaal et al., 2010 RSFC seed-based Au duong et al., 2005b* SEM, PASAT Seed-based FC, PASAT Cader et al., 2006 Task-related ROI correlations Cerasa et al., 2012 Fera et al., 2013 Forn et al., 2012 31 MS (10 CIS, 16 RR-MS, 5 SPMS), 31 HCs 85 RR-MS, 40 HCs 25 MS, 30 HCs Studies of ROIs/subnetworks using Task Analysis Sample Author Au duong et al., 2005a* 15 MS, 13 HCs PPI PPI- Hipp seed and memory task Helekar et al., 2010 DCM Voxelwise hierarchical clustering Leavitt et al., 2012 Granger causality Rocca et al., 2012 Rocca et al., 2009a PPI ROIs Result rsfMRI, DMN SMN + DMN 8 resting networks Visual RSFC, Striate, Occipital DMN Motor, 2 visual networks PCC to whole brain +/+ +/- + - + AAL atlas 90 regions ACC +/- SN, ECN, DMN +/- Hippocampal connections - ROIs + Result 18 early stage MS, 18 HCs BA 46 18 early stage MS, 18 HCs BA 45/46 - PFC, ACC +/- Cb, Parietal - Hippocampus vs. brain MFC, ACC, IFG, IPL Whole Brain during WCST 8 task-related ROIs, PFC PFC, R Cb + 21 RR-MS, 16 HCs 12 +Cb-MS,15 -Cb MS, 16HCs 26 MS, 25 HCs 18 CIS, 15 HCs 16 RR-MS, 21 HCs 16 MS, 17 HCs 17 RR-MS, 17 benign MS, 23 SP-MS, 18 HCs 15 benign MS, 19 HCs DCM, Stroop task SensorimotorRIFG, Cb +/- + +/+ +/+/- 3b: Studies of Motor Networks Author Analysis Ceccarelli et al., 2010 FC analysis Cruz Gomez et al., 2013 ICA and seed-based Dogonowski et al., 2012** 20-min RSFC Sample ROIs 15 PPMS, 15 HCs Motor network 60 RRMS, 18 HCs SMA, PMC, thalamus Motor RSFC and subcortical 42 MS, 30 HCs Result +/- + 27 RR-MS, 15 SP-MS Dogonowski et al., 2013** Dogonowski et al., 2013** Rocca et al., 2007 Rocca et al., 2009b*** PPI, RSFC Kendall’s coefficient of concordance Task-FC DCM Rocca et al., 2010 DCM Valsasina et al., 2011*** RSFC seed-based nuclei Motor RSFC 27 RR-MS, 15 SP-MS Motor, Cerebellum 12 RR-MS, 14 HCs 61 MS, 74 HCs 17 pediatric RR-MS, 16 adult CIS, 14 adult RRMS, 10 HCs 61 MS, 74 HC Motor network SMC, SMA Sensorimotor network Motor, sensorimotor + + +/+ + 3c: Studies using Graph Theory Author Gamboa et al., 2013 Analysis Correlation matrix Sample 16 Early MS 20 HCs ROIs 116 AAL atlas Result na *,**,***=identical MS samples; Abbreviations Table 2: AAL: automated anatomical labeling, ACC= anterior cingulate cortex, Cb= cerebellum, DCM=dynamic causal modeling, DLPFC=dorsolateral prefrontal cortex, DMN=default mode network, ECN = executive control network, FC=functional connectivity (correlation), HC= healthy control, Hipp=hippocampus, ICA= independent components analysis, LIPG= left inferior parietal gyrus, MS=multiple sclerosis, nON-MS=non-optic neuritis multiple sclerosis, ON-MS=optic neuritis multiple sclerosis, PCC= posterior cingulate cortex, PFC=prefrontal cortex, PMC=primary motor cortex, PPI=psychophysiological interaction, PP-MS=primary progressive MS, RIFG= right inferior frontal gyrus, RR-MS= relapsing remitting MS, RSFC=resting state functional connectivity, RSFC= resting state functional connectivity, SEM= structural equation modeling, SL= Synchronization likelihood, SMA= supplementary motor cortex, SN= salience network. Table S3a Connectivity Results for DAT and MCI Studies Examining Whole-Brain Connectivity Using Graph Theory Author Table 3a: Graph Theory in AD and MCI Analysis Sample size ROIs Chen et al., 2013 Graph theory 30 AD, 30 HCs 116 (Talairach) - Minati et al., 2014 ICA.; graph theory Pair-wise synchronization, graph theory Graph theory Graph theory Episodic memory task, graph theory Graph theory; seed-voxel 49 MCI, 32 HCs 742 regions Whole brain, Frontal cortices - Whole brain DMN, Whole brain DMN - Sanz-Arigita et al., 2010 Supekar et al., 2008 Wang, J. et al., 2013 Wang, L. et al., 2013 Xia et al., 2013 18 mild AD, 21 HCs 21 AD, 18 HCs 37 aMCI, 47 HCs Result 25 MCI, 26 HCs 32 AD, 38 HCs posteromedial cortex +/(PMC) Whole brain, Frontal, +/-* Yao et al., 2010 Graph theory 113 MCI, 91 AD, 98 HCs Posterior +/Zhao et al., 2012 Graph theory 33 AD, 20 HCs Whole brain, DMN Note: increased path length interpreted as connectivity loss; data not included in Figure 2. Abbreviations Table 3a: AD=Alzheimer’s disease, DMN=default mode network, FTLD=frontotemporal lobe dementia, RSFC=resting state functional connectivity. Table S3b-c Connectivity Results for AD Examining ROI and Subnetworks and Task Author Agosta et al., 2012 Table 3b:Studies of Rest Only connectivity in AD Analysis Sample size ROIs RSFC seed-based DMN, Frontoparietal, Result +/- 13 AD,12 MCI,13 HC Allen et al., 2007 Balthazar et al., 2013 Binnewijzend et al., 2012 Chhatwal et al., 2013 Ciftci et al., 2011^ RSFC seed-based RSFC, ICA ICA, dual regression ICA RSFC, minimum spanning tree 8 AD, 8 HCs 20 AD, 17 HCs 39 AD, 23 MCI, 43 HCs 15 AD, 37 HCs 13 AD,14 young HC, 14 old HC Cole et al., 2011 Damoiseaux et al., 2012 RSFC seed-based 14AD, 15 HC ICA, dual regression Galvin et al., 2011 RSFC seed-based Gili et al., 2011 ICA Greicius et al., 2004 ICA 21 AD, 18 HCs 88 total - longitudinal ( 15 DLB, 35 AD, 38 HCs) 11 AD, 10 MCI, 10 HCs 13 AD, 14 YHCs, 14 HCs Jones et al., 2011 ICA and seed-based analyses Kim et al., 2013^ Li et al., 2013 Liu et al., 2013 28AD,56hc ECN, SN Hippocampus, Frontal - DMN, SN DMN, Precuneus, PCC +/- DMN PCC, Precuneus, Hippocampus Pain networks, R DLPFC Subdivisions of the DMN Precuneus DMN DMN, PCC Hippocampus aDMN, pDMN, seed analyses w/ medFC and precuneus Hippocampus and Precuneus DMN, DAN DMN + +/+/+/- - RSFC seed-based ICA RSFC seed-based, graph theory ICA 13 AD, 14 HCs 14 AD, 16 HCs 18 severe AD, 17 mild AD, 18 MCI, 21 HC 35 AD, 18 aMCI, 21 HC RSFC – whole brain RSFC seed-based RSFC, hierarchical ICA clustering analysis ICA, Bayesian modeling 14 AD, 14 HCs 13 AD, 13 HCs 32 AD, 38 HC 35 AD, 27 MCI, 27 HC 30 AD, 25 MCI, 25 HC Amygdala Hippocampus +/- Zamboni et al., 2013 Zhang et al., 2009 Zhang et al., 2010 RSFC seed-based Probabilistic ICA Task and Rest RSFC seed-based RSFC seed-based PCC DMN, PCC DMN +/+/- Zhou et al., 2008 ICA Zhou et al., 2010 Zhou et al., 2013 ICA RSFC seed-based 18 AD, 16 HCs 46 AD, 16 HCs 11 AD, 10 MCI, 13 HCs 12 AD, 12 FTD, 12 HCs 35 AD, 27 MCI, 27 HCs 10 AD, 11 aMCI, 12 HCs Song et al., 2013 Wang, K. et al., 2007 Wang, L. et al., 2006 Wang, Z. et al., 2013 Wu et al., 2011* Yao et al., 2013 Zhu et al., 2013 Author RSFC seed-based 15 AD, 16 HCs DMN, AN, LFP, Pcu, RFP, SMN, VN Whole brain, PCC Hippocampus IPL subregions DMN DMN, PCC, Salience network Thalamus Precuneus, PCC Table 3c: Studies of ROIs/subnetworks during task in AD Analysis Sample size ROIs Franciotti et al., 2013 ICA, Granger causal modeling Genon et al., 2012 Li et al., 2012 PPI, ICA ICA., Verbal fluency task Liu et al., 2012 Miao et al., 2011 ICA, granger causality Granger causal modeling 18 Lewy, 18 AD, 15 HCs 32 AD, 17 HCs 15 AD, 16 HCs 18 AD, 18 HCs 15 AD, 12 young HCs, +/+/+/+/- +/- Result DMN, PCC - Precuneus, PCC - Dorsal and Ventral Attention networks, CC, MPFC RSNs, DMN, Auditory network DMN, PCC, medFC, IPL - +/- 16 old HCs, Ries et al., 2012! Rombouts et al., 2009 PPI analysis, memory selfappraisal task Tensorial probabilistic ICA Rytsar et al., 2011 Schwindt et al., 2012 Song et al., 2013 DCM RSFC, visual task ICA 12 AD,12 HCs 18 AD, 28 MCI, 41 HCs 14 AD, 16 HCs 16 AD, 18 HCs 35 AD, 18 aMCI, 21 HC Med FC - Motor, Visual, Cognitive networks and DMN during face encoding task V1, V3 DMN DMN, AN, LFP, Pcu, RFP, SMN, VN DMN - - Power spectral analysis 15 AD, 16 HCs and Granger Causal modeling Note: all “Results” for AD samples only, for studies including AD & MCI samples, MCI findings presented below. *=identical samples included only once in Figure 2. Wen et al., 2013* Table S3d-e Connectivity Results for MCI Examining ROI and Subnetworks and Task Author Agosta et al., 2012 Table 3d: Studies of ROIs/subnetworks in MCI Method Sample size ROIs Analysis Feng et al., 2012 RSFC Regional homogeneity RSFC seed-based ICA RSFC seed-based RSFC-whole brain correlation ICA, crosscorrelation Linear correlation coefficient ICA, dual regression RSFC seed-based RSFC seed-based RSFC-whole brain correlation self-organizing ICA RSFC-whole brain correlation Gili et al., 2011 ICA 12 MCI, 12 HCs 11 AD, 10 MCI, 10 HCs Gour et al., 2011 Han et al., 2012 Jin et al., 2012 Li et al., 2013 Liang et al., 2012*** Liang et al., 2011*** Liu et al., 2013 ICA RSFC seed-based ICA ICA. RSFC seed-based RSFC seed-based RSFC seed-based, graph theory ICA 13 MCI, 12 HCs 40 MCI, 40 HCs 8 MCI,8 HCs 17 aMCI, 17 HC 14 MCI,14 HCs 14 MCI,14 HCs 18 severe AD, 17 mild AD, 18 MCI, 21 HC 14 MCI,14 HCs Bai et al., 2008 Bai et al., 2009b* Bai et al., 2011a** Bai et al., 2011b** Bai et al., 2011c** Bai et al., 2012** Bokde et al., 2006 Binnewijzend et al., 2012 Das et al., 2013 Dong et al., 2012 Drzezg et al., 2011 Esposito et al., 2013 Qi et al., 2010*** 13 AD,12 MCI, 13HC 20 aMCI, 20 HCs 30 aMCI;26hcs 26 aMCI, 18 HCs 26 aMCI, 18 HCs 26 aMCI, 18 HCs DMN, Frontoparietal, ECN, SN DMN, PCC, RIPL, R fusiform, Putamen PCC and whole brain PCC, Precuneus Cerebellum Frontal, Subcortical Self-referencing network Result +/+/+ +/+ 26 aMCI, 18 HCs +/16 MCI, 19 HCs 39 AD, 23 MCI, 43 HCs 17 MCI, 31 HCs 8 MCI ,8 SA,8 UA 12 PIB+, 12 PIB-, 13 PIB+/MCI 13 MCI, 24 HCs Right middle frontal gyrus DMN, Precuneus, PCC nd Medial temporal lobe DMN Whole brain connectivity +/+/- DMN, SMN, IPL, SMG + Whole brain +/- DMN - Anterior temporal network, DMN, ECN PCC DMN DMN DLPFC to IPC, IPS, AG, SG DMN, ECN, SN DMN + +/+/+/+/+/- DMN +/- Rombouts et al., 2009 Song et al., 2013 Tensorial probabilistic ICA ICA. Sorg et al., 2007 ICA Wang Y et al., 2013 Wang, Z. et al.,. 2011*** Wang, Z. et al., 2012a *** Wang, Z. et al., 2012b*** Xie et la., 2012* Xie et al., 2013 ICA 18 AD, 28 MCI, 41 HCs 35 AD, 18 aMCI, 21 HC 24 aMCI, 16 HCs 18 MCI, 23 CC, 16 HCs RSFC seed-based 14 MCI,14 HCs Yao et al., 2013 RSFC seed-based Functional connectivity density Probabilistic ICA., correlation, rest and task DICCCOL, functional connectome Yi et al., 2012 Zamboni et al., 2013 Zhu et al., 2013 RSFC seed-based 14 MCI,14 HCs RSFC seed-based RSFC seed-based RSFC seed-based 14 MCI,14 HCs 30 aMCI, 26 HCs 18 LLD, 17 aMCI, 12 LLD & aMCI, 25 HC 35 AD, 27 MCI, 27 HC Zhu et al., 2013 Zhou et al., 2013 RSFC seed-based RSFC seed-based Zhou et al., 2008 ICA Author Bai et al., 2009a Jacobs et al., 2012 Liu et al., 2012 Neufang et al., 2011 Yan et al., 2013 Motor, Visual, Cognitive networks, DMN RSNs: DMN, AN, LFP, Pcu, RFP, SMN, VN DMN, Hippocampus, PCC DCM ICA, Granger causal modeling - DMN, Hippocampus Hippocampus +/- Thalamus +/- PCC +/- Insula Hippocampus +/- Amygdala DMN nd - 26 MCI, 28 HCs 30 AD, 25 MCI, 25 HC Hippocampus 10 MCI, 24 at-risk MCI, 10 HCs DTI derived ROIs for functional connectomes +/- 10 AD, 11 aMCI, 12 HCs 35 AD, 27 MCI, 27 HCs 11 AD, 10 MCI, 13 HCs Precuneus, PCC nd Thalamus +/- DMN Table 3e: Studies of ROIs/subnetworks during task in MCI Analysis Sample size ROIs Cross-correlation Granger causal methods ICA, multivariate granger causal modeling - 28 aMCI, 23 HCs - Result Memory-related networks, Hippocampus Parietal connectivity +/- 8 RSNs +/- +/- 18 MCI,18 HCs 16 MCI, 18 HCs 15 pAD, 16 healthy elderly Cingulo-fronto-parietal network 18 aMCI, 18 HCs DMN +/- Note: all “Results” for MCI samples only, for studies including AD & MCI samples, AD findings presented above. *,**,***=identical samples, findings included only once in Figure 2. Abbreviations for Table 3a-d: ACC= anterior cingulate cortex, AD=Alzheimer’s disease, AG: angular gyrus, aMCI=amnestic mild cognitive impairment, DLB=dementia with Lewy bodies, DLPFC=dorsolateral prefrontal cortex, DMN=default mode network, ECN = executive control network, FC=functional connectivity (correlation), fMRI=functional magnetic resonance imaging, HC= healthy control, ICA= independent components analysis, IPC= inferior parietal cortex, IPS= intra-parietal sulcus, LIPG= left inferior parietal gyrus, nd= no difference, pAD=prodromal AD, PCC= posterior cingulate cortex, PET=positron emission tomography, PFC=prefrontal cortex, PPI-psychophysiological interaction, PP-MS=primary progressive MS, RIFG= Right inferior frontal gyrus, RIPL= right inferior parietal lobule, RR-MS= relapsing remitting MS, RSFC= resting state functional connectivity; SA=successful aging, SEM= structural equation modeling, SICE=sparse inverse covariance estimates, SG=supramarginal gyrus, SL= Synchronization likelihood, SMA= supplementary motor cortex, SN= salience network, V1,V3: visual cortex, UA: usual aging.
