Behavioral Impact and Neural Correlates of Network-based Brain Stimulation in Schizophrenia

The primary aims of this study are to investigate the behavioral effects of applying targeted transcranial magnetic stimulation (TMS) to a node of the brain network subserving attention in schizophrenia and schizoaffective disorder and to correlate these effects to changes in brain network connectivity.

Assumptions:

• Cerebral activity can be investigated through the correlated activity of cortical nodes that constitute functional brain networks
• Behavior can be correlated to the strength of correlated activity between and within nodes of these cortical networks
• TMS to targeted nodes leads to changes in patterns of activity in these cortical networks

Hypothesis:

TMS to a node of the attention network in schizophrenia or schizoaffective disorder will influence network connectivity leading to measurable and predictable effects on attention.

Aim 1: Test if network connectivity reflects cognitive performance using an independent sample of individuals with schizophrenia or schizoaffective disorder (n=50). Hypothesis 1A: Network connectivity will predict attentional performance in schizophrenia and schizoaffective disorder. Hypothesis 1B: The magnitude of attentional performance improvement after TMS is also explained by change in network connectivity in schizophrenia and schizoaffective disorder.

Exploratory Aim: As an exploratory aim, the TMS test if TMS affects patterns of cigarette craving and if changes in cigarette craving are correlated to changes in functional connectivity. It is hypothesize that craving will decrease following iTBS administration. It is hypothesized that a decrease in cigarette craving will correlate with changes in brain network functional connectivity.

Psychotic disorders such as schizophrenia are disabling, lifelong illnesses that afflict over three million people in the US. The life expectancy of individuals with schizophrenia is ~25 years less than the general population. Tobacco use is the top preventable cause of early mortality in schizophrenia. The prevalence of smoking in schizophrenia is over 60%. It is unknown why nicotine dependence is so prevalent in schizophrenia, and nicotine dependence is not treated in schizophrenia differently than in the general population.

There is a significant gap in the understanding of nicotine dependence in psychotic disorders and how this diagnosis affects its treatment. Most neuroimaging studies of nicotine dependence are hypothesis-driven study designs focusing on reward circuitry. Studies of nicotine dependence in a psychotic population have typically used the same experimental design. This approach may be missing important circuits involved in nicotine dependence specific to psychotic disorders.

Her an agnostic, data-driven approach is used to investigate this problem. It is observed that individual variation in the severity of nicotine use is linked to individual variation in default mode network (DMN) organization.

Group 13, Grouped object Traditional imaging studies yield results which are correlational and do not test associations between findings. Therefore causality of nicotine-network interactions was tested. In an independent cohort, it was observed that acute nicotine administration reverses DMN hyperconnectivity in schizophrenia in a dose-dependent relationship. This is consistent with a model in which nicotine may correct schizophrenia-specific pathology.

The central hypothesis is this: Brain circuits most relevant to nicotine dependence in schizophrenia are distinct from pathways identified in a non-schizophrenia population. DMN disruption in schizophrenia is a well-replicated observation. Based on the data, it is hypothesized that individual variation in the magnitude of this network disruption gives rise to: a) variation in the severity of network disorganization and b) variation in severity of nicotine use. Further, it is proposed that the DMN mediates the relationship between nicotine use and cognition in schizophrenia. DMN hyperconnectivity is linked to poor attentional performance and, in healthy controls, nicotine can both reduce DMN connectivity and improve attention. It is proposed that DMN connectivity mediates the relationship between nicotine use and attentional performance and that disruption of this network in schizophrenia drives the need for nicotine to correct network pathology (and cognition). This model can be empirically tested: If the effect of nicotine on cognition is truly mediated by these networks, then 1) modulation of network connectivity should affect attentional performance and 2) the degree of network modulation should explain performance change irrespective of the method used to manipulate network connectivity.

To test the reproducibility of the results, multiple cohorts of participants with validated measures of nicotine dependence will be analyzed, clinical characterization, and brain imaging. To test the rigor of the scientific model, it is determined that if modulating these networks using a different (non-nicotine) intervention (Transcranial Magnetic Stimulation, TMS) can replicate nicotine's effects on cognitive performance.

In this study, transcranial magnetic stimulation (TMS) neuromodulation was used to target previously identified network pathophysiology unique to nicotine dependence in psychotic disorders. Here, a model is tested that integrates brain network pathophysiology and cognition to 1) explain the prevalence of nicotine use in schizophrenia and 2) identify a target for engagement in future clinical trials in schizophrenia and schizoaffective disorder.

1. Functional connectivity as a measure of neural function

The human brain has evolved to perform different behaviors under different sets of conditions and as such must flexibly combine the functional processing capabilities of disparate neuroanatomical regions. Collections of structurally interconnected brain areas must interact dynamically to process a large and ever-changing set of inputs in the ultimate attempt to generate an appropriate behavioral output. Functional networks can therefore be defined as the collection of interconnected brain areas that interact to perform these circumscribed functions.

To identify the critical nodes involved in these different functional networks, much of the work in the field of functional neuroimaging has used the fMRI blood-oxygen-level-dependent (BOLD) signal as a marker of cerebral perfusion, which in turn is known to reflect cerebral activity. fMRI BOLD activation patterns have revealed network nodes that are involved in such cognitive functions as attention, working memory, language, and others.

In the resting state--while a subject is asked simply to look straight without performing a specific task in the MRI- analyses of the time-course of BOLD activation have revealed that activity patterns of several of these cerebral nodes exhibit coordinated fluctuations. As such, the activity patterns between particular nodes appear to be highly correlated in time while others seem relatively independent of one another. Taken together, these coordinated nodes can be thought of as a coupled functional network.

Interestingly, the degree to which these nodes appear correlated depends on task demands. In certain conditions, nodes will be highly correlated while, in others, these very same nodes appear rather independent. This degree of functional coupling within and between nodes of specific functional networks is therefore thought to reflect a change in distributed processing that is essential in mediating a change in cognitive functioning.

In the domain of attention in particular, several groups have shown a correlation between the strength of connectivity in specific networks and performance on tests that rely on this specific domain. Examples include functional connectivity analyses in healthy controls attention deficit hyperactivity disorder, neglect, and Parkinson's disease. Studies such as these have reliably demonstrated a correlation between measures of attention and the connectivity patterns of several key networks including the default mode network, the salience network (SN), the dorsal attention network (DAN) and the ventral attention network (VAN).

Using this paradigm, complex cognitive functions such as attention can now be understood and investigated by monitoring the dynamic coupling and uncoupling of nodes within these pre-defined networks.

1. TMS effects on behavior and attention

TMS, (Transcranial Magnetic Stimulation) is a non-invasive, easily transportable, and relatively inexpensive procedure that utilizes magnetic fields to create electric currents in discrete brain regions. TMS is based on Faraday's principle of electromagnetic induction and features application of rapidly changing magnetic field pulses to the scalp via a copper wire coil connected to a magnetic stimulator. These brief pulsed magnetic fields painlessly pass through the skull and create electric currents in discrete brain regions of sufficient magnitude to depolarize neurons. Applied in single pulses (single-pulse TMS) appropriately delivered in time and space, the currents induced in the brain can be of sufficient magnitude to depolarize a population of neurons. When applied to the motor cortex, this depolarization results in a series of descending (direct and indirect) corticospinal waves that can sum-up at the spinal segmental level, depolarize alpha motor neurons and lead to the contraction of contralateral muscles. This can be measured, for example, as motor evoked potentials (MEPs) using electromyography (EMG). Applied to any cortical region, TMS evokes a local field potential that can be recorded with EEG, or other brain imaging techniques, and reveals a measure of cortical reactivity to TMS. TMS intensity can be parametrically varied in its intensity thus allowing the establishment of a dose-response curve and providing insights into cortical excitability.

Trains of repeated TMS pulses (rTMS) at various stimulation frequencies and patterns can induce a lasting modification of activity in the targeted brain region which can outlast the effects of the stimulation itself. Such lasting changes presumably represent alterations in plasticity mechanisms.

Therefore, TMS can be used with multiple pulses to facilitate activity in a region of the brain or suppress activity in a region of the brain. In this study,a specific form of rTMS called theta burst stimulation (TBS) is used.

This study seeks to provide evidence that targeted stimulation of a brain network leads to both altered network activity and a concomitant functional change in attention in individuals with schizophrenia and schizoaffective disorder. This study will test a model that integrates brain network pathophysiology and cognition to 1) explain the prevalence of nicotine use in schizophrenia and 2) identify a target for engagement in schizophrenia. This study seeks to establish a neuroscientific framework to guide future treatment-oriented studies aimed at improving attention and nicotine use in individuals with schizophrenia and schizoaffective disorder.