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.

A connectivity model of the anatomic substrates underlying Gerstmann syndrome

“… 153 Both of these networks have been implicated in a variety of cognitive processes such as in the attentional control required for mathematic processes or writing, but particularly these functions are subserved through both intra-network connections around the intraparietal sulcus as well as extra-network interactions with other higher-order brain networks. 12 , 154 Each domain included a number of parcels which are known to affiliate with higher-order networks like the sensorimotor, salience, language, and central executive networks. Thus, while previous localizationist views often suggest neurologic deficits can be localized to a single region, such as a speech deficit following surgical injury to Broca’s area, Gerstmann’s symptoms may likely be better localized to dysfunction in a number of large-scale brain networks which dynamically interact to carry out many higher-order functions.…”

“… 14 Our connectivity model of these likely regions and their white matter connections details this anatomy in a way which can offer prognostic information for neurosurgeons during intra-axial, resective brain surgery such that we can make more informed surgical decisions while causing fewer deficits according to patient functional goals. 12 , 126 …”

“…Furthermore, given the recent focus on a disconnection hypothesis of this clinical phenomenon, we sought to detail the connectivity of a possible Gerstmann Core network through region-based deterministic tractography specifically on key parcels identified in our functional analyses across each domain. The localization of neural substrates underlying the ability to write, perform mathematics, distinguish left from right, and discriminate one’s own finger to an anatomically parcellated network could not only help address the long-standing questions surrounding Gerstmann’s clinical phenomenon but also provides actionable anatomic information for clinical applications in this region, such as for resective brain surgery, 12 modulatory treatments, 13 or early prognostication. 14 …”

12 Plagues of AI in Healthcare: A Practical Guide to Current Issues With Using Machine Learning in a Medical Context

“…A common example of this can be seen with the increased ability we now have to detect mental illness based on improved and publicly available maps of the brain connectome ( 21 , 22 ). If a ML specialist received a dataset including data on patients with Schizophrenia, their first thought may be to build a model to detect Schizophrenia.…”

A Network-Based Approach to Glioma Surgery: Insights from Functional Neurosurgery

“…Gliomas adopt mechanisms to promote progression through newly formed neuroglioma synapses that are thought to impact the electrical activity and function of existing neural pathways [ 1 , 2 ]. In parallel, an understanding of the overall structural and functional connectivity of the brain has emerged, alongside an awareness of the neurologic and neuropsychiatric effects that intra-axial mass lesions can have on neural networks [ 3 ]. As such, neurosurgical oncology strategies are increasingly taking into consideration updated models of the anatomical–functional architecture of the brain [ 4 ].…”