The default mode network (DMN) has been identified reliably during rest, as well as during the performance of tasks such as episodic retrieval and future imagining. It remains unclear why this network is engaged across these seemingly distinct conditions, though many hypotheses have been proposed to account for these effects. Prior to generating hypotheses explaining common DMN involvement, the degree of commonality in the DMN across these conditions, within individuals, must be statistically determined to test whether or not the DMN is truly a unitary network, equally engaged across rest, retrieval and future imagining. To provide such a test, we used comparable paradigms (self-directed, uninterrupted thought of equal duration) across the three conditions (rest, retrieval, and future imagining) in a within-participant design. We found lower than expected pattern similarity in DMN functional connectivity across the three conditions. Similarity in connectivity accounted for only 40-50% of the total variance. Partial Least Squares (PLS) analyses revealed the medial temporal regions of the DMN were preferentially coupled with one another during episodic retrieval and future imagining, whereas the non-medial temporal regions of the DMN (e.g., medial prefrontal cortex, lateral temporal cortex, and temporal pole) were preferentially coupled during rest. These results suggest that DMN connectivity may be more flexible than previously considered. Our findings are in line with emerging evidence that the DMN is not a static network engaged commonly across distinct cognitive processes, but is instead a dynamic system, topographically changing in relation to ongoing cognitive demands. Hum Brain Mapp 38:1155-1171, 2017. © 2016 Wiley Periodicals, Inc.

Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disorder that progressively affects bulbar and limb function. Despite increasing recognition of the disease as a multinetwork disorder characterized by aberrant structural and functional connectivity, its integrity agreement and its predictive value for disease diagnosis remain to be fully elucidated. In this study, we recruited 37 ALS patients and 25 healthy controls (HCs). High-resolution 3D T1-weighted imaging and resting-state functional magnetic resonance imaging were, respectively, applied to construct multimodal connectomes. Following strict neuroimaging selection criteria, 18 ALS and 25 HC patients were included. Network-based statistic (NBS) and the coupling of grey matter structural–functional connectivity (SC–FC coupling) were performed. Finally, the support vector machine (SVM) method was used to distinguish the ALS patients from HCs. Results showed that, compared with HCs, ALS individuals exhibited a significantly increased functional network, predominantly encompassing the connections between the default mode network (DMN) and the frontoparietal network (FPN). The increased structural connections predominantly involved the inter-regional connections between the limbic network (LN) and the DMN, the salience/ventral attention network (SVAN) and FPN, while the decreased structural connections mainly involved connections between the LN and the subcortical network (SN). We also found increased SC–FC coupling in DMN-related brain regions and decoupling in LN-related brain regions in ALS, which could differentiate ALS from HCs with promising capacity based on SVM. Our findings highlight that DMN and LN may play a vital role in the pathophysiological mechanism of ALS. Additionally, SC–FC coupling could be regarded as a promising neuroimaging biomarker for ALS and shows important clinical potential for early recognition of ALS individuals.