Dynamic brain network reconfiguration as a potential schizophrenia genetic risk mechanism modulated by NMDA receptor function

Abstract

Schizophrenia is increasingly recognized as a disorder of distributed neural dynamics, but the molecular and genetic contributions are poorly understood. Recent work highlights a role for altered N-methyl-D-aspartate (NMDA) receptor signaling and related impairments in the excitation-inhibitory balance and synchrony of large-scale neural networks. Here, we combined a pharmacological intervention with novel techniques from dynamic network neuroscience applied to functional magnetic resonance imaging (fMRI) to identify alterations in the dynamic reconfiguration of brain networks related to schizophrenia genetic risk and NMDA receptor hypofunction. We quantified "network flexibility," a measure of the dynamic reconfiguration of the community structure of time-variant brain networks during working memory performance. Comparing 28 patients with schizophrenia, 37 unaffected first-degree relatives, and 139 healthy controls, we detected significant differences in network flexibility [F(2,196) = 6.541, P = 0.002] in a pattern consistent with the assumed genetic risk load of the groups (highest for patients, intermediate for relatives, and lowest for controls). In an observer-blinded, placebo-controlled, randomized, cross-over pharmacological challenge study in 37 healthy controls, we further detected a significant increase in network flexibility as a result of NMDA receptor antagonism with 120 mg dextromethorphan [F(1,34) = 5.291, P = 0.028]. Our results identify a potential dynamic network intermediate phenotype related to the genetic liability for schizophrenia that manifests as altered reconfiguration of brain networks during working memory. The phenotype appears to be influenced by NMDA receptor antagonism, consistent with a critical role for glutamate in the temporal coordination of neural networks and the pathophysiology of schizophrenia. S chizophrenia is a highly heritable mental disorder for which aberrant interactions between brain regions or "dysconnectivity" have been proposed as a core neural mechanism (1). Functional magnetic resonance imaging (fMRI) studies demonstrate alterations in specific neural subcircuits in schizophrenia (2, 3) that are under genetic control (4), although recent data from network neuroscience point to more widespread disturbances in the dynamics of large-scale brain networks (5, 6). Uhlhaas (5), Uhlhaas and Singer (6), and Phillips and Silverstein (7) have proposed a plausible pathophysiological mechanism for these phenomena: They argue that alterations in the cellular excitation-inhibitory balance may lead to disturbances in the neural synchrony of large-scale cell ensembles and give rise to the dysconnectivity phenomena at the level of neural ensembles.The neural excitation-inhibitory balance is highly dependent on glutamatergic N-methyl-D-aspartate (NMDA) receptor function (8), and alterations in NMDA receptor signaling have been associated with schizophrenia risk (9), disorder-related cognitive abnormalities (10, 11), and deficits in the temporal coordination of large-scale bra...

The energy landscape underpinning module dynamics in the human brain connectome

“…Module reconfiguration at rest has also been reported as a marker of aging and development (Betzel et al, 2015). These studies collectively demonstrate that module reconfiguration is a hallmark of healthy brain function (Telesford et al, 2016), and recent evidence suggests that it is a marker that is altered in psychiatric disease, even providing an intermediate phenotype of schizophrenia (Braun et al, 2016). …”

“…A natural question following the observation of these modules was “What do they do? And how are they recruited as we go through life performing a variety of functions?” To address these questions, dynamic community detection methods were developed and applied to neuroimaging data, revealing the fact that modules reconfigure in support of working memory (Braun et al, 2015, 2016), reinforcement learning (Gerraty et al, 2016), visuo-motor learning (Bassett et al, 2011, 2013b, 2015), and linguistic processing (Chai et al, 2017; Doron et al, 2012a). Module reconfiguration at rest has also been reported as a marker of aging and development (Betzel et al, 2015).…”

“…Evidence suggests that module reconfiguration may be driven by glutamatergic signaling (Braun et al, 2016), affect and arousal Betzel et al (2016a), and may provide a substrate for cognitive control (Khambhati et al, 2016), supporting a delicate balance between domain-general and domain-specific function (Fedorenko and Thompson-Schill, 2014a). Although these observations support the biophysical relevance of the phenotype, they do not provide computational theories for its existence.…”

A Network Neuroscience of Human Learning: Potential to Inform Quantitative Theories of Brain and Behavior

“…Are brains that have more or less structural network control more or less flexible [105,106]? Can one modulate brain network flexibility with mood induction [85] or pharmacological intervention [107]? Is flexible network reconfiguration modulated by NMDA [107], norepinephrine [108], or other neurotransmitters?…”

“…In recent applications to brain networks, these models have been exercised in the context of static network representations in both health [111] and disease [112], and in both humans and non-human animals [113]. Extending these tools into the temporal domain is a particularly exciting prospect which could offer fundamental insights into the mechanisms of network reconfiguration, and alterations in those mechanisms that may accompany normative neurodevelopment [114], healthy aging [115], or aberrant dynamics in neurological disease [116-118] or psychiatric disorders [107,119,120] that impact on learning. Classical network models are summarized in Box 3.…”