Letter: Elucidating the Principles of Brain Network Organization Through Neurosurgery
Bibliographic record
Abstract
To the Editor: The human brain comprises nearly 100 billion neurons that are highly interconnected and communicate with each other. It is this very interconnectedness that gives rise to functional brain networks that govern complex cognition and human behavior. To date, the human brain mapping community has largely gleaned insights into the principles of brain network organization from large-scale imaging consortia (ie, Human Connectome Project) and small-scale observational and interventional studies. We have learned that, in common with other naturally occurring networks, brain networks demonstrate 3 topological network features1: (i) small-worldness, (ii) existence of hubs, and (iii) community structure. In addition, we have learned that 2 major driving principles of brain network organization are minimizing the energetic costs of wiring while investing resources that promote network efficiency.2 In disease, these principles are stretched from normality, but persist to maintain the classical features of complex networks. Insights into the human connectome have largely been derived from merging neuroimaging with network science, and more recently transcriptomics.3 However, neurosurgical practice has barely been utilized to unmask the principles of brain network organization. Given that minute (ie, thalamotomy) to massive (ie, temporal lobectomy) volumes of brain parenchyma are routinely removed for various clinical indications,4 neurosurgery provides a unique scientific perspective on the human connectome. Recently, Kliemann and colleagues5 investigated how functional brain networks were organized in 6 adults who underwent hemispherectomy (HS) as children. The main objective of their study was to determine how functional brain networks differed between HS patients and controls by quantifying within-network and between-network connectivity. The authors5 recruited a historical HS cohort with a mean age of 24.33 yr. The timing of HS ranged from minutes after birth to early adolescence. Of the 6 HS patients, 4 patients underwent complete functional hemispherectomy, while 2 patients underwent complete anatomic hemispherectomy. The investigators acquired high-resolution resting-state functional magnetic resonance imaging (MRI) scans and compared the brain's intrinsic functional architecture between adult HS patients (n = 6) and healthy controls (n = 6). To aid in generalizability, the authors used a normative functional connectome (n = 1482) as a second control dataset. Despite radical surgery, resting-state networks in HS subjects remained in typical configurations with normal levels of within-network connectivity, but increased between-network connectivity. Specifically, they report that the HS cohort had significantly increased between-network connectivity, outside the range seen in controls, in all 7 of Yeo's empirically derived functional brain networks6 (ie, default mode, frontoparietal, etc). Finally, compared to controls, the authors demonstrated that global efficiency (a measure of functional integration) increases and modularity (a measure of functional segregation) remained stable in HS patients. This study highlights several important findings relevant to the neurosurgical community. First, the authors curated an interesting, hard-to-acquire dataset that provides insight into how large-scale networks organize and communicate when the brain experiences a major physical alteration in early life. They demonstrated that functional brain networks can be reconstructed normally, albeit unilaterally, and that the healthy hemisphere can resume normal function. Second, the authors found normal communication within networks but increased synchronicity between networks, suggesting that HS brains in adulthood work harder to integrate neural activity. The clear implication here is that bilateral hemispheres promote brain network segregation. Third, this study provides weight to using brain stimulation to promote functional remapping and recovery in the contralateral hemisphere after an injury (ie, stroke)7 because canonical functional networks can be recapitulated despite highly atypical anatomy. Finally, Kliemann and colleagues5 imply that following HS in early life, brains undergo compensation to regain function. However, without longitudinal data, it is unclear of how these networks topologically reorganize acutely postsurgery and during subsequent rehabilitation. Moreover, it appears that some in the HS cohort were actually hemispheretomies,8,9 and partial bilateral communication could have been preserved despite the functional isolation of the healthy hemisphere. It is important to place the study within a broader neuroscientific context, especially with regard to compensation and network communication. First, on the grounds of this study, there is very little evidence of any kind of reorganization of brain networks caused by HS, and perhaps the preservation of functional networks is what is surprising. While it is reasonable to assume network plasticity following HS in the developing brain, the authors do not demonstrate this concept due to the absence of longitudinal imaging and cognitive data. The most obvious explanation for their findings is that large-scale functional brain networks are broadly fixed in very early life within their spatial/anatomic configuration to generate internal synchronicity. Moreover, the brain “switches” the configuration and emphasis of its network communication in response to cognitive demands,10 and thus any form of compensation can only truly be discussed within the framework of task-related activation. Finally, if we interpret increased between-network communication above the normal range as “compensation,” then presumably a priori we would posit that the greater the deviation of activation, the greater the degree of compensation, and thus better cognitive outcomes. While the authors do point out that there were insufficient data to be definitive, the available evidence suggests the opposite; namely, HS patients with the greatest cognitive challenges had increased connectivity across functional networks. Thus, in our cautious view, inferring cognitive “compensation” in the context of network connectivity in a retrospective study needs to be tempered by the available evidence. In summary, connectomics is still an evolving field of research, although there is reasonable evidence that certain emerging themes may prove to be both reproducible and useful. Neurosurgeons can help elucidate the principles of brain network organization given the highly distorted anatomy we work with; specifically, predicting surgical morbidity, mechanisms of network plasticity, and the natural history of recovery curves may bidirectionally advance basic neurophysiology and neurosurgical care. Kliemann and colleagues5 make a positive first step towards these aims with additional studies on the way. Ultimately, we hope neurosurgeons partner with neuroscientists and continue to play an active role in deciphering not only the principles of brain network organization, but also mechanisms of cerebral plasticity, as there is still much to unravel. Disclosures Anujan Poologaindran is supported by the Alan Turing Institute and the National Science and Engineering Research Council of Canada. The authors have no personal, financial, or institutional interest in any of the drugs, materials, or devices described in this article.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.016 | 0.024 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".