In-depth Investigation of Brain Network Interactions

Research Statement SIGNIFICANCE Memory impairments are common to several neurological and psychiatric disorders, including Alzheimer's disease and depression, and these impose a heavy burden on patients, families and society (Dickerson and Eichenbaum, 2007). Novel treatment and diagnostic strategies are needed, and these may arise from a deeper understanding of the brain basis of episodic memory (Tulving, 1983). Group-averaged neuroimaging studies have revealed that a distributed network, known as the 'default network' (DN), increases activity during the recollection of past events (Buckner et al. 2008). This network occupies regions including posteromedial cortex (PMC), posterior parietal cortex (PPC), and the medial temporal lobe (MTL), as well as lateral temporal and lateral and medial prefrontal cortices. Building on recent advances in functional magnetic resonance imaging (fMRI; Poldrack et al., 2015; Laumann et al., 2015), recent evidence has shown that when functional anatomy is defined in individuals, the DN comprises at least two juxtaposed networks, named DN-A and DN-B for convenience (Figure 1). This finding forces us to reconsider the role of the DN in episodic processes (see also: Dastjerdi et al., 2011; Andrews-Hanna et al., 2010). Here we propose experiments to deepen our understanding of these networks using a multimodal approach that provides high spatiotemporal resolution and whole-brain network definition. We will combine within-individual fMRI mapping with intracranial electroencephalography (iEEG) and electrical brain stimulation (EBS). We will directly record local field potentials from precisely mapped network regions, and apply electrical stimulation with millimeter precision. This will provide novel information regarding episodic memory in two domains that cannot be gathered by fMRI alone: i) characterizing fast temporal dynamics of network recruitment during episodic recollection, and ii) establishing causal interactions between brain regions during recollection. INNOVATION Methodologically, this project will provide proof of principle that precision fMRI mapping can be performed in a clinical population and successfully combined with invasive recordings and stimulation. Theoretical innovation will be obtained through a deeper understanding of the task-response dynamics, coupling, and causal relationships between regions of distributed networks, including how neural engagement changes during memory recollection. Finally, this proposal provides translational innovation by directly testing whether precision-fMRI guided intracranial stimulation can be used modulate memory performance. APPROACH General methods: Participants in the proposed experiments will be neurosurgical patients with presumed focal epilepsy that are to undergo implantation with intracranial electrodes for localizing seizure foci. The proposal will be carried out at the Northwestern University Feinberg School of Medicine. Patients scheduled for intracranial seizure monitoring will be invited to enroll in the study and will undergo 1 to 4 sessions of fMRI prior to surgical implantation of electrodes. After surgery, patients are typically monitored for \~7 days in the Northwestern Memorial Hospital Comprehensive Epilepsy Centre (CEC), during which they will be invited to participate in the proposed experiments. All subjects must provide informed consent before participating. Enrollment: A minimum of 40-50 patients are expected to be monitored at the CEC over the next 3 years. Electrode locations are determined by the clinical needs of the patient. 60-70% of patients are typically implanted with dense coverage of the medial temporal lobes achieved through depth electrodes with trajectories that allow sampling of lateral temporal cortices. A small number of electrodes are also typically implanted in posterior cingulate, lateral inferior parietal and ventromedial prefrontal cortex. Due to the distributed nature of the networks under investigation, which contain regions in multiple cortical zones, it is likely that we will have coverage over relevant brain regions in many cases. Some patients are also likely to be implanted with broader cortical coverage using subdural grids. Preliminary results have shown that even when a patient is implanted only with depth electrodes, which are not placed on the cortical surface but penetrate into the brain, coverage of different candidate network regions was often achieved along the electrode trajectory. With conservative estimates, 20-30 subjects will be good candidates for the project aims outlined below. Given the high signal-to-noise ratio of iEEG (usually a 200-300% task-evoked increase in signal from baseline; Parvizi and Kastner, 2017), reliable effects can typically be found within individuals. All proposed analyses will be carried out within individuals, hence multiple subjects are required to generalize the findings, not increase statistical power. Therefore, a small number of subjects (as low as n = 12) would be sufficient (e.g. Braga and Buckner, 2017; Foster et al., 2013). Neuroimaging acquisition: MR scans will be collected in 1-4 sessions from each patient. Preliminary data has shown that in this clinical population 2-3 MRI sessions are desirable to allow exclusion of non-compliant runs (e.g. those containing excess head motion). We will collect 6-8 runs of fMRI data per session, resulting in between 42 - 224 mins of fMRI data per patient. This will allow robust and reliable estimates of network topography. Subject sleepiness will be monitored through an in-scanner eye-tracking camera. Compliance may be improved by allowing patients to watch movies inside the scanner when needed, with pilot analyses showing comparable maps are obtained using movie and visual fixation task data. Hence both tasks will be administered to improve compliance. Network definition within individuals: Networks will be defined within individuals using two methods to ensure robustness. MRI preprocessing will be performed using a custom pipeline 'iProc' that optimizes within-subject alignment and minimizes blurring. Individual seed regions will be hand-selected and correlation maps will be thresholded at r \> 0.2 to remove regions of low certainty. The networks of interest, DN-A and DN-B, will be targeted and identified using the expected anatomical distribution of each network (described in detail in Braga and Buckner, 2017). Once candidate seed regions are selected, definition of networks will be performed again in each individual using data-driven clustering, which reduces potential experimenter bias. Networks from the clustering analysis that most closely match up with the networks defined by hand will be selected and labelled as DN-A and DN-B. Network maps will be used to label electrode contacts (each 'electrode' can have multiple 'contacts' along its shaft or grid) by their approximate location within or near each network. Electrode localization: Electrode locations will be determined using a computerized tomography (CT) scan. Estimates of the center of each contact in CT space will be obtained using BioImage Suite. The CT image will be registered to the anatomical T1 image (containing brain tissue locations) using a linear transform, allowing coordinates of each contact to be projected to the T1 space. Preliminary data has shown that the inter-rater error in this localization process is typically \~1mm. A 2-mm radius sphere will be generated centered on each contact coordinate to approximate the sampling volume of each contact, which is extended due to tissue conductance. Contacts that are predominantly sampling white matter will be removed by excluding contacts whose sphere does not overlap with the gray matter ribbon (estimated using FreeSurfer). The overlap between spheres and gray matter will be used for surface-based and volume-based functional connectivity (FC) analyses. FC maps will be created for each contact, and the resulting maps will be visualized. If a contact fails to produce a FC map with distant regions of high correlation (indicating that the contact is sampling a distributed network), the contact will be excluded. If the contact's FC map resembles DN-A and DN-B, as defined using the clustering and manually defined seed-based analyses, this contact will be labelled as sampling DN-A and DN-B and included for further analysis. Two nearby electrodes, one situated in DN-A and one in DN-B, will be selected a priori in two different cortical zones (e.g. PMC vs. PPC, based on coverage). iEEG processing: All contacts within the epileptic zone or corrupted by external noise will be removed from further analysis. Raw signals will be notch filtered at 60, 120 and 180 Hz to remove electrical noise and harmonics. Notch-filtered signals will be re-referenced by subtracting the common average, after removal of pathogenic or spiky signals, as well as those presenting as clear outliers in power spectra plots. Data will be bandpass filtered to extract amplitude and phase information at different frequency bands. The high-frequency broadband (HFB; 70-140 Hz) signal is an important surrogate for local neuronal population activity and corresponds to low-frequency correlations of the blood oxygenation-level dependent signal (Logothetis et al., 2001). HFB band-limited power will be calculated and low-pass filtered at \<0.1 Hz. Pair-wise correlations in HFB power will be used to estimate functional connectivity. Direct cortical stimulation: Risks associated with the research stimulation protocol are considered incremental and are further reduced by carrying out the stimulation under supervision of a clinical researcher, when patients are on antiepileptic medication, and keeping stimulation to within safety limits. Low frequency (1 Hz) single pulse stimulation will be applied to regions of DN-A and DN-B to map cortico-cortical evoked potentials (CCEPs). This will be used to estimate the strength, as well as provide data on the directionality of connections between regions. In a departure from original plans, based on recent findings (Hermiller et al. 2019), theta-burst stimulation (gamma-band stimulation applied intermittently at theta frequencies) will be applied to regions of DN-A regions in lateral temporal, posteromedial and prefrontal cortices during a recollection task to test if stimulation of distant DN-A regions can lead to improvements in hippocampus-mediated episodic memory recollection. Currents will be administered at a threshold below that which causes after-discharges (usually around 6-8 mA).