2018-2019 Doctoral Awardee: Charles J. Lynch

“Precision Mapping and Transcranial Magnetic Stimulation of Cortical Hubs in Individuals”

Charles J. Lynch, Georgetown University, Department of Psychology


Hub nodes serve important roles in modular physical, social, and biological systems. Hub brain areas mapped using non-invasive neuroimaging could be compelling sites for intervention in a variety of psychiatric conditions. This dissertation addresses the challenges of achieving reliable mapping of hub brain areas in individuals and establishes the first causal link between integrity of hub functioning and healthy cognition. The first experiment revealed that different areas of cortex function as hubs in different individuals, but that hub estimates were highly-reproducible within an individual. The second investigation tested the hypothesis that inhibiting hub function with transcranial magnetic stimulation would disrupt information processing during working-memory, more so than inhibition of nearby non-hubs. Despite these stimulation sites being separated by two centimeters on the same gyrus in each subject, information processing was disrupted more by hub inhibition than non-hub inhibition. Inhibition of hubs linking control networks with processing systems, including the visual and dorsal somatomotor networks, was most disruptive for working-memory performance. These investigations raise the possibility of stimulating hub subtypes relevant for patient symptomatology in non-invasive brain stimulation interventions.


There is widespread interest in using non-invasive brain stimulation (NIBS) as a treatment for psychiatric conditions [1]. The outcomes of these interventions, however, are variable across treated individuals. In the most widely adopted, Federal Drug Administration approved application of NIBS – repetitive transcranial magnetic stimulation (TMS) for the treatment of medication refractory major depression – only 29% percent of patients respond positively [2]. Variation in patient response has been attributed in part to uncertainty regarding free parameters inherent to NIBS [3], including the stimulation site. Because the effects of NIBS are believed to propagate from the stimulation site in a manner constrained by the connectivity of the targeted brain area [4, 5], appropriate stimulation site selection is likely critical for therapeutic success, as it will determine whether or not stimulation effects spread throughout clinically-relevant neural circuitry.

Stimulation site selection strategies often focus upon anatomical landmarks [6, 7]. The same anatomical area, however, can exhibit different patterns of functional connectivity across individuals [8-10]. In other words, stimulating the same anatomical area in different individuals could in practice affect different downstream brain areas – which theoretically could contribute to the variable outcomes of NIBS interventions. The advent of techniques for precisely characterizing the areal [11, 12] and network organization [9, 10, 13] of individual human brains has set the stage for the development of personalized NIBS protocols, which in principle can increase the likelihood of producing better outcomes in patients [14].

A network science framework, in which brain areas (“nodes”) engage in networked communication within and across brain networks (“modules”), is theoretically well-suited for mapping stimulation sites on an individual basis. This approach can capitalize on the idea that a node’s role in a network can be inferred from its connections [15]. Of particular interest are select nodes, termed “connector hubs” (hereafter referred to as hubs), connected to multiple network modules and critical for the function of many networks found in nature [16]. Evidence from biophysical models [17, 18] and lesions in stroke patients [19, 20] indicate that hub brain areas could serve critical roles in the human brain as well. Because of their unique positioning at the intersection of multiple segregated brain networks, we theorized that administering NIBS to hubs could impact flow of information between brain networks, which in turn would affect performance on a cognitive task more than non-hub stimulation.

This dissertation includes two complimentary investigations. The first assesses the feasibility of mapping hubs in single-subjects as NIBS targets. To do so, we utilize the Midnight Scan Club (MSC) [13], a publicly-available dataset of individuals that underwent ten resting-state fMRI sessions over a period of ten consecutive days. The second investigation involved a prospective, double-blind, within-subject NIBS experiment. This experiment tested the hypothesis that administering an inhibitory form of TMS to hubs would disrupt information processing during an N-back working-memory task, as measured using a drift diffusion model, relative to non-hub inhibition.


The “Midnight Scan Club” [13] includes five hours of resting-state fMRI (rsfMRI) data collected from ten healthy adults (mean age = 29.1 years ± 3.3, 5F/5M) over a period of ten consecutive days (10 x 30 minute sessions). We characterized the amount of per-subject rsfMRI data required to achieve reproducible single-subject hub estimates using an iterative split-half procedure.

The second investigation used a prospective, within-subjects, double-blind design to test whether inhibiting the function of a hub brain area with transcranial magnetic stimulation disrupts information processing during an N-back working-memory task,
more so than inhibition of a non-hub area of the same gyrus. Twenty-four participants aged 18-28 years (mean age = 20.5 years ± 2.5, 11F/13M) were recruited for this experiment. A drift diffusion model is a well-validated computational model that is advantageous to considering accuracy and response times distributions in isolation from one another [21]. A repeated-measures 2 x 2 (load x target) ANOVA model was performed on each of three diffusion model parameters – drift rate, boundary separation, and non-decision time.


In the first investigation, we found that hub estimates were unreliable when calculated using commonly utilized quantities of per-subject rsfMRI data (e.g., 5-10 minutes). Reliability improved, however, with larger quantities of per-subject rsfMRI data. The spatial distribution of hub estimates across individuals was not similar (average rs = – 0.01 ± 0.26). Furthermore, single-subject hub estimates were not similar to their collective group-average (rs = 0.09 ± 0.21). These findings indicated that hubs can be reliably mapped on an individual basis if one acquires a sufficient quantity of rsfMRI data.

In the second investigation, we found that inhibition of a hub with continuous theta burst stimulation disrupted information processing during working-memory more than inhibition of a non-hub area (main effect of target; pcorrected = 0.006, ω2 = 0.04), despite the targets being separated by only a few centimeters on the right middle frontal gyrus of each subject. This finding demonstrated, as predicted, that hub inhibition disrupts information processing more than non-hub inhibition. An exploratory analysis revealed that inhibition of hubs linking control networks with processing systems, including the visual and dorsal somatomotor networks, was most disruptive for working-memory performance.


We present two findings in this dissertation. First, cortical hubs mapped using large quantities of per-individual rsfMRI are reproducible, but idiosyncratic features of functional brain organization. Second, inhibiting a hub area with cTBS disrupted working-memory performance, as measured using a drift diffusion model, more than inhibiting a non-hub area of the same gyrus.

This dissertation joins an emerging field of precision neuroscience [13, 22, 23] that treats idiosyncrasies in functional brain organization as neurobiologically informative rather than epiphenomena. Our findings highlight how precisely mapping individual-specific features of a connectome could be leveraged in the future to guide interventions in the human brain. NIBS therapies may be particularly well-suited to adopt this approach. Future work can evaluate whether NIBS interventions could benefit from stimulating hubs mapped using rsfMRI in single-subjects.

Impact Statement

Network theory is a powerful and increasingly widespread conceptual framework in neuroscience [24, 25]. This approach distills the complexity of the brain into simpler mathematical representations [26], which in turn allows investigators to form and test tractable hypotheses regarding how a brain might process information in a networked fashion. Validating the core predictions of this framework is a necessary step towards establishing its clinical value [27, 28]. This dissertation is a first step towards this goal and demonstrates how network neuroscience could be leveraged in the future to inform personalized interventions in humans [29], including NIBS [30], but also neurofeedback or rehabilitation.


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