Table 3.
Selected studies related to effect of intrinsic state on dynamic rsfMRI. Columns include citation, the method used for calculating changes in the fMRI signal, the disease or other intrinsic state studied, and selected observations. Note that several of these studies included a task in addition to resting state, see the individual studies for specifics.
| Citation | Method | Disease | Selected observations |
|---|---|---|---|
| Sakoglu et al., 2010 | Windowed correlation (64 s) | Schizophrenia | More differences seen in dynamic vs. static connectivity, task modulation different between groups |
| Jones et al., 2012 | Windowed correlation (6–297 s) | Alzheimer's disease | Less time spent in default mode network modules in Alzheimer's disease |
| Damaraju et al., 2014 | Windowed correlation (44 s) clustered into states, low-frequency power within states | Schizophrenia | Increases or decreases based on region, not all observed in static connectivity, low frequency power altered in subcortical regions |
| Ma et al., 2014 | Windowed independent vector analysis (75 s) | Schizophrenia | Greater spatial fluctuations in schizophrenia group |
| Rashid et al., 2014 | Windowed correlation (33 s) clustered into states | Schizophrenia, bipolar disorder | Differences between states and transitions particular to each disease, not captured by static rsfMRI |
| Shen et al., 2014 | Windowed correlation (40 s) and low frequency power | Schizophrenia | Low frequency power in connected networks classifies disease versus health |
| Price et al., 2014 | Windowed correlation (20–240 s) | Autism | (Conference paper) Dynamic information increases predictive accuracy of disease |
| Ou et al., 2014 | Bayesian detection of change points (~160 s segments) | Attention deficit hyperactivity disorder | Some interaction states only present in ADHD, these located abnormal networks which distinguished ADHD |
| Li et al., 2014b | Windowed correlation (28 s) | Post-traumatic stress disorder | Some states appear only in PTSD, can be diagnosed with high selectivity |
| Laufs et al., 2014 | Windowed correlation (30 s) | Epilepsy | Greater variance of connectivity in epilepsy group |
| Liao et al., 2014 | Windowed correlation (100 s) | Epilepsy | Connectivity changes prior to and after seizures |
| Yu et al., 2015 | Windowed correlation (40 s) clustered into states | Schizophrenia | Graph theoretical metrics are lower and there is less variance in schizophrenia |
| Madhyastha et al., 2015 | Windowed correlation (41 s) | Parkinson's disease | No static rsfMRI differences, performance differences in disease (related to dynamic rsfMRI) |
| Douw et al., 2015 | Windowed correlation (102 s) | Epilepsy | Dynamic rsfMRI, but not static rsfMRI or connectivity during task linked to memory disturbance |
| Morgan et al., 2015 | Windowed correlation (60 s) | Epilepsy | Greater variance and greater covariance with seizure network connectivity as disease progresses |
| Nedic et al., 2015 | Entropic analysis of BOLD amplitude autocorrelation | Epilepsy | Less chaotic dynamics in patients |
| Cassidy et al., 2016 | Linear regression between network amplitudes | Schizophrenia | Altered connectivity in schizophrenia, linked to dopamine in schizophrenia only |
| Du et al., 2016 | Windowed correlation (40 s) clustered into states | Schizophrenia | In default mode network, lower connectivity and graph theoretical measures in schizophrenia, duration of states different |
| Miller et al., 2016 | Sums of windowed correlation (44 s) clustered into states | Schizophrenia | Less dynamism in schizophrenia, more pronounced with high levels of hallucinatory behavior |
| Kaiser et al., 2016 | Windowed correlation (36 s) | Depression | Increases or decreases in dynamism based on region |
| Wee et al., 2016 | Windowed correlation (270 s) | Mild cognitive impairment | Altered graph theoretical network properties in mild cognitive impairment |
| Falahpour, et al. 2016 | Windowed correlation (30 s) | Autism | Dynamic rsfMRI indicates connectivity not reduced, but more variable, in autism, whereas static rsfMRI indicates reduction |
| Wang et al., 2017 | Windowed correlation (30–120 s), Wavelet coherence | Chronic headache | Greater wavelet coherence and less dynamism in chronic headache |
| Jin et al., 2017 | Windowed correlation with change points detected with Augmented Dickey-Fuller test (non-fixed length, 20–100 s) | Post-traumatic stress disorder | Better classification with dynamic than static analysis, better classification using varying window length |
| Liu et al., 2017 | Windowed correlation (20–150 s) clustered into states | Epilepsy | Characteristics of states vary more from control as disease duration or seizure frequency increases |
| Ridley et al., 2017 | Windowed nonlinear covariance (90 s) | Epilepsy | Networks involved in generating seizures and spikes have increased static but decreased dynamic rsfMRI versus spike-only networks |
| Intrinsic difference | |||
| Qin et al., 2015 | Windowed correlation (36 s) | Age | Increased variance in connectivity (weighted by salience) as age increases |
| Yaesoubi et al., 2015 | Wavelet coherence | Males vs. females | State occupancy rates differ in males vs. females |
| Shen et al., 2016 | Windowed correlation (56 s) clustered into states | Taxi drivers vs. non drivers | Dynamic rsfMRI, but not static rsfMRI higher in taxi drivers in vigilance network, dwell time in states altered |