Abstract
The dysconnection hypothesis of schizophrenia (SZ) proposes that psychosis is best understood in terms of aberrant connectivity. Specifically, it suggests that dysconnectivity arises through aberrant synaptic modulation associated with deficits in GABAergic inhibition, excitation–inhibition balance and disturbances of high-frequency oscillations. Using a computational model combined with a graded-difficulty visual orientation discrimination paradigm, we demonstrate that, in SZ, perceptual performance is determined by the balance of excitation–inhibition in superficial cortical layers. Twenty-eight individuals with a DSM-IV diagnosis of SZ, and 30 age- and gender-matched healthy controls participated in a psychophysics orientation discrimination task, a visual grating magnetoencephalography (MEG) recording, and a magnetic resonance spectroscopy (MRS) scan for GABA. Using a neurophysiologically informed model, we quantified group differences in GABA, gamma measures, and the predictive validity of model parameters for orientation discrimination in the SZ group. MEG visual gamma frequency was reduced in SZ, with lower peak frequency associated with more severe negative symptoms. Orientation discrimination performance was impaired in SZ. Dynamic causal modeling of the MEG data showed that local synaptic connections were reduced in SZ and local inhibition correlated negatively with the severity of negative symptoms. The effective connectivity between inhibitory interneurons and superficial pyramidal cells predicted orientation discrimination performance within the SZ group; consistent with graded, behaviorally relevant, disease-related changes in local GABAergic connections. Occipital GABA levels were significantly reduced in SZ but did not predict behavioral performance or oscillatory measures. These findings endorse the importance, and behavioral relevance, of GABAergic synaptic disconnection in schizophrenia that underwrites excitation–inhibition balance.
Highlights
The neurobiological underpinnings of schizophrenia (SZ) are currently poorly understood, with several competing synaptic hypotheses; including dysfunction of GABAergic neuromodulation,[1] NMDA receptor hypofunction and aberrant dopamine regulation.[3]
Using neurophysiologically informed modeling based on dynamic causal modeling (DCM), we demonstrate how GABA-mediated local dysconnectivity leads to altered gamma oscillatory activity that predicts behavioral performance in a graded, disease-specific manner
Using a convolution-based canonical microcircuit model (CMC),[22] optimized to reflect the known properties of primary visual cortex (V1) and implemented within the dynamic causal modeling (DCM) framework of SPM8, we modeled the spectral density recorded by the MEG virtual sensors, to better characterize the neuronal population-level interactions underlying the oscillatory responses
Summary
The neurobiological underpinnings of schizophrenia (SZ) are currently poorly understood, with several competing synaptic hypotheses; including dysfunction of GABAergic neuromodulation,[1] NMDA receptor hypofunction (review2) and aberrant dopamine regulation.[3] Reconciling these various models, the dysconnection hypothesis[4] proposes that SZ results from synaptic dysconnectivity—both between brain regions and between the layers of cortical columns—caused by aberrant synaptic modulation. Synaptic modulation refers to the balance of excitation and inhibition, whereby GABAergic neurons exert control (and synchrony) over the firing of principal cells in cortex. NMDA receptor hypofunction reduces excitation of GABAergic cells, which results in a disinhibited, hyperdopaminergic state.[5] As such, cortical deficits in NMDA, GABA, or both, may underlie the pathophysiology of schizophrenia. In vivo studies of altered bulk GABA concentrations in schizophrenia are, equivocal, with a recent meta-analysis suggesting no detectable differences in GABA levels across prefrontal, parietal/occipital cortex, and striatum using proton magnetic resonance spectroscopy (1H-MRS).[8]
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