Reducing the Cognitive Footprint of Brain Tumor Surgery

Abstract

The surgical management of brain tumors is based on the principle that the extent of resection improves patient outcomes. Traditionally, neurosurgeons have considered that lesions in "non-eloquent" cerebrum can be more aggressively surgically managed compared to lesions in "eloquent" regions with more known functional relevance. Furthermore, advancements in multimodal imaging technologies have improved our ability to extend the rate of resection while minimizing the risk of inducing new neurologic deficits, together referred to as the "onco-functional balance." However, despite the common utilization of invasive techniques such as cortical mapping to identify eloquent tissue responsible for language and motor functions, glioma patients continue to present post-operatively with poor cognitive morbidity in higher-order functions. Such observations are likely related to the difficulty in interpreting the highly-dimensional information these technologies present to us regarding cognition in addition to our classically poor understanding of the functional and structural neuroanatomy underlying complex higher-order cognitive functions. Furthermore, reduction of the brain into isolated cortical regions without consideration of the complex, interacting brain networks which these regions function within to subserve higher-order cognition inherently prevents our successful navigation of true eloquent and non-eloquent cerebrum. Fortunately, recent large-scale movements in the neuroscience community, such as the Human Connectome Project (HCP), have provided updated neural data detailing the many intricate macroscopic connections between cortical regions which integrate and process the information underlying complex human behavior within a brain "connectome." Connectomic data can provide us better maps on how to understand convoluted cortical and subcortical relationships between tumor and human cerebrum such that neurosurgeons can begin to make more informed decisions during surgery to maximize the onco-functional balance. However, connectome-based neurosurgery and related applications for neurorehabilitation are relatively nascent and require further work moving forward to optimize our ability to add highly valuable connectomic data to our surgical armamentarium. In this manuscript, we review four concepts with detailed examples which will help us better understand post-operative cognitive outcomes and provide a guide for how to utilize connectomics to reduce cognitive morbidity following cerebral surgery.

Keywords: brain tumor; cognition; connectome; machine learning; neuroimaging; neurorehabilitation; neurosurgery.

Figure 1

The core of the default mode network (DMN). While a multi-lobar network, the core of the DMN is provided, including its key brain parcellations and connecting structural fibers. Individual regions have been visualized in 3D space and therefore some parcellations have been covered or require multiple views for correct spatial understanding due to parallax. Visualization of tracts have been minimized (thickness and volume) to maximize the visibility of parcellations.

Figure 2

The core of the salience network (SN). Similar to the DMN, the SN is a multi-lobar network and here we show its core. Individual regions have been visualized in 3D space and therefore some parcellations have been covered or require multiple views for correct spatial understanding due to parallax. Visualization of tracts have been minimized (thickness and volume) to maximize the visibility of parcellations.

Figure 3

The core of the central executive network (CEN). The CEN is a key multi-lobar control network necessary for effective cognitive functioning and its main core is provided here. Functional regions have been visualized in 3D space and therefore some parcellations have been covered or require multiple views for correct spatial understanding due to parallax. An example of this is the provided with brain parcellation area TE1m (temporal area 1 middle), which is correctly found on the lateral surface of the middle aspect of the middle temporal gyrus (83). Visualization of tracts have been minimized (thickness and volume) to maximize the visibility of parcellations.

Figure 4

The prefrontal cognitive initiation “axis.” Our model of the initiation axis suggests that the DMN, connected via the cingulum, and the SN, connected via the frontal aslant tract (FAT), create a structural chain that extends up to the SMA in the medial frontal lobe (77). In fact, the SMA and salience networks share a node known as SCEF (supplementary and cingulate eye field). The identity and functional relevance of these connections have been supported by multiple lines of evidence (85), together suggesting this initiation “axis” is likely responsible for internally modeling goal initiation, such as with the initiation of speech and motor planning. Unsurprisingly, disruption of the integrity of this “axis” when operating on a tumor in the medial frontal lobe can lead to akinetic mutism and abulia (14, 82, 85).

Figure 5

Disconnection surgery. The neurosurgical community should move toward thinking of tumor surgery as a series of cortical and subcortical disconnections to minimize unnecessary network disturbances based on tumor location and patient goals. This figure presents a case of a medial anterior, left frontal glioma which demonstrates complex relationships with adjacent anatomy, such as the corpus collosum and falx cerebri. (A) While a complex case, it can be reduced to a series of disconnections that can be defined against three known brain networks: the DMN on medial boundary, the CEN on the lateral boundary, and the sensorimotor network on the posterior boundary. (B) Understanding information on the spatial relationship of the tumor to relevant white matter tracts and major networks can allow the neurosurgeon to make more informed decisions during surgery, such as where or how far to disconnect (blue lines) normal tissue infiltrated by a tumor up until certain key fibers or nodes are met. This decision should be made based on patient pre-defined goals and patient prognosis among other factors, and further work will hopefully clarify how much of specific networks can be safely disconnected without compromising certain functions (C).

Figure 6

Core parcellations and fibers involved in speech production. It is important to be aware of the core parcellations involved in speech production, as well as the main fibers interconnecting those regions such as the second layer of the superior longitudinal fasciculus (SLF-II), the arcuate fasciculus (AF), and the frontal aslant tract (FAT). For instance, area 55b is a recently discovered region that increasing evidence has suggested is necessary for phonation and motor control of the larynx (90). 55b is connected to the posterior temporal language areas via the SLF-II in the left hemisphere. If unaware of such connections, transgressing 55b leads to apraxia of speech (91).

Figure 7

The transdiagnostic hypothesis is often applied to explain the core psychopathological symptoms across a range of psychiatric disorders. Thus, while certain disorders may be clinically grouped together with vague classifications, unique symptoms likely localize to unique brain networks providing the need for addressing them individually.