The Frontal Aslant Tract and Supplementary Motor Area Syndrome: Moving towards a Connectomic Initiation Axis
TL;DR: In this paper, the authors demonstrate how utilizing tractography and a network-based approach decreases the likelihood of transient SMA syndrome during medial frontal glioma surgery, which may be important for functional outcomes and patient quality of life.
Abstract: Connectomics is the use of big data to map the brain's neural infrastructure; employing such technology to improve surgical planning may improve neuro-oncological outcomes. Supplementary motor area (SMA) syndrome is a well-known complication of medial frontal lobe surgery. The 'localizationist' view posits that damage to the posteromedial bank of the superior frontal gyrus (SFG) is the basis of SMA syndrome. However, surgical experience within the frontal lobe suggests that this is not entirely true. In a study on n = 45 patients undergoing frontal lobe glioma surgery, we sought to determine if a 'connectomic' or network-based approach can decrease the likelihood of SMA syndrome. The control group (n = 23) underwent surgery avoiding the posterior bank of the SFG while the treatment group (n = 22) underwent mapping of the SMA network and Frontal Aslant Tract (FAT) using network analysis and DTI tractography. Patient outcomes were assessed post operatively and in subsequent follow-ups. Fewer patients (8.3%) in the treatment group experienced transient SMA syndrome compared to the control group (47%) (p = 0.003). There was no statistically significant difference found between the occurrence of permanent SMA syndrome between control and treatment groups. We demonstrate how utilizing tractography and a network-based approach decreases the likelihood of transient SMA syndrome during medial frontal glioma surgery. We found that not transecting the FAT and the SMA system improved outcomes which may be important for functional outcomes and patient quality of life.
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TL;DR: In this paper, a detailed cortical model elucidating the white matter connectivity associated with this area could improve our understanding of the interacting brain networks that underlie complex human processes and postoperative outcomes related to vision and language.
48 citations
TL;DR: In this paper, the authors 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.
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.
35 citations
TL;DR: In this article, a detailed understanding of the subcortical white matter tracts connected within the MFG can facilitate improved navigation of white matter lesions in and around this gyrus and explain the postoperative morbidity after surgery.
30 citations
Abstract: Purpose Advances in neuroimaging have provided an understanding of the precuneus’(PCu) involvement in functions such as visuospatial processing and cognition. While the PCu has been previously determined to be apart of a higher-order default mode network (DMN), recent studies suggest the presence of possible dissociations from this model in order to explain the diverse functions the PCu facilitates, such as in episodic memory. An improved structural model of the white-matter anatomy of the PCu can demonstrate its unique cerebral connections with adjacent regions which can provide additional clarity on its role in integrating information across higher-order cerebral networks like the DMN. Furthermore, this information can provide clinically actionable anatomic information that can support clinical decision making to improve neurologic outcomes such as during cerebral surgery. Here, we sought to derive the relationship between the precuneus and underlying major white-mater bundles by characterizing its macroscopic connectivity. Methods Structural tractography was performed on twenty healthy adult controls from the Human Connectome Project (HCP) utilizing previously demonstrated methodology. All precuneus connections were mapped in both cerebral hemispheres and inter-hemispheric differences in resultant tract volumes were compared with an unpaired, corrected Mann–Whitney U test and a laterality index (LI) was completed. Ten postmortem dissections were then performed to serve as ground truth by using a modified Klingler technique with careful preservation of relevant white matter bundles. Results The precuneus is a heterogenous cortical region with five major types of connections that were present bilaterally. (1) Short association fibers connect the gyri of the precuneus and connect the precuneus to the superior parietal lobule and the occipital cortex. (2) Four distinct parts of the cingulum bundle connect the precuneus to the frontal lobe and the temporal lobe. (3) The middle longitudinal fasciculus from the precuneus connects to the superior temporal gyrus and the dorsolateral temporal pole. (4) Parietopontine fibers travel as part of the corticopontine fibers to connect the precuneus to pontine regions. (5) An extensive commissural bundle connects the precuneus bilaterally. Conclusion We present a summary of the anatomic connections of the precuneus as part of an effort to understand the function of the precuneus and highlight key white-matter pathways to inform surgical decision-making. Our findings support recent models suggesting unique fiber connections integrating at the precuneus which may suggest finer subsystems of the DMN or unique networks, but further study is necessary to refine our model in greater quantitative detail.
22 citations
TL;DR: In this paper, the Glasser HCP atlas was used as a prior to calculate the streamline connectivity between each voxel and each parcel of the atlas and then iteratively adjusted the prior to create patient-specific maps independent of brain shape and pathological distortion.
Abstract: For over a century, neuroscientists have been working toward parcellating the human cortex into distinct neurobiological regions. Modern technologies offer many parcellation methods for healthy cortices acquired through magnetic resonance imaging. However, these methods are suboptimal for personalized neurosurgical application given that pathology and resection distort the cerebrum. We sought to overcome this problem by developing a novel connectivity-based parcellation approach that can be applied at the single-subject level. Utilizing normative diffusion data, we first developed a machine-learning (ML) classifier to learn the typical structural connectivity patterns of healthy subjects. Specifically, the Glasser HCP atlas was utilized as a prior to calculate the streamline connectivity between each voxel and each parcel of the atlas. Using the resultant feature vector, we determined the parcel identity of each voxel in neurosurgical patients (n = 40) and thereby iteratively adjusted the prior. This approach enabled us to create patient-specific maps independent of brain shape and pathological distortion. The supervised ML classifier re-parcellated an average of 2.65% of cortical voxels across a healthy dataset (n = 178) and an average of 5.5% in neurosurgical patients. Our patient dataset consisted of subjects with supratentorial infiltrating gliomas operated on by the senior author who then assessed the validity and practical utility of the re-parcellated diffusion data. We demonstrate a rapid and effective ML parcellation approach to parcellation of the human cortex during anatomical distortion. Our approach overcomes limitations of indiscriminately applying atlas-based registration from healthy subjects by employing a voxel-wise connectivity approach based on individual data.
21 citations
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TL;DR: The authors showed that the revised ALE‐algorithm overcomes conceptual problems of former meta‐analyses and increases the specificity of the ensuing results without loosing the sensitivity of the original approach, and may provide a methodologically improved tool for coordinate‐based meta-analyses on functional imaging data.
Abstract: A widely used technique for coordinate-based meta-analyses of neuroimaging data is activation likelihood estimation (ALE). ALE assesses the overlap between foci based on modeling them as probability distributions centered at the respective coordinates. In this Human Brain Project/Neuroinformatics research, the authors present a revised ALE algorithm addressing drawbacks associated with former implementations. The first change pertains to the size of the probability distributions, which had to be specified by the used. To provide a more principled solution, the authors analyzed fMRI data of 21 subjects, each normalized into MNI space using nine different approaches. This analysis provided quantitative estimates of between-subject and between-template variability for 16 functionally defined regions, which were then used to explicitly model the spatial uncertainty associated with each reported coordinate. Secondly, instead of testing for an above-chance clustering between foci, the revised algorithm assesses above-chance clustering between experiments. The spatial relationship between foci in a given experiment is now assumed to be fixed and ALE results are assessed against a null-distribution of random spatial association between experiments. Critically, this modification entails a change from fixed- to random-effects inference in ALE analysis allowing generalization of the results to the entire population of studies analyzed. By comparative analysis of real and simulated data, the authors showed that the revised ALE-algorithm overcomes conceptual problems of former meta-analyses and increases the specificity of the ensuing results without loosing the sensitivity of the original approach. It may thus provide a methodologically improved tool for coordinate-based meta-analyses on functional imaging data.
1,609 citations
TL;DR: The previous permutation algorithm is replaced with a faster and more rigorous analytical solution for the null-distribution and comprehensively address the issue of multiple-comparison corrections.
1,140 citations
TL;DR: It is suggested that the modified ALE algorithm is theoretically advantageous compared with the current algorithm, and that the alternate organization of datasets is the most conservative approach for typical ALE analyses and other CBMA methods.
Abstract: Activation Likelihood Estimation (ALE) is an objective, quantitative technique for coordinate- based meta-analysis (CBMA) of neuroimaging results that has been validated for a variety of uses. Stepwise modifications have improved ALE's theoretical and statistical rigor since its introduction. Here, we evaluate two avenues to further optimize ALE. First, we demonstrate that the maximum con- tribution of an experiment makes to an ALE map is related to the number of foci it reports and their proximity. We present a modified ALE algorithm that eliminates these within-experiment effects. How- ever, we show that these effects only account for 2-3% of cumulative ALE values, and removing them has little impact on thresholded ALE maps. Next, we present an alternate organizational approach to datasets that prevents subject groups with multiple experiments in a dataset from influencing ALE val- ues more than others. This modification decreases cumulative ALE values by 7-9%, changes the rela- tive magnitude of some clusters, and reduces cluster extents. Overall, differences between results of the standard approach and these new methods were small. This finding validates previous ALE reports against concerns that they were driven by within-experiment or within-group effects. We sug- gest that the modified ALE algorithm is theoretically advantageous compared with the current algo- rithm, and that the alternate organization of datasets is the most conservative approach for typical ALE analyses and other CBMA methods. Combining the two modifications minimizes both within- experiment and within-group effects, optimizing the degree to which ALE values represent concord- ance of findings across independent reports. Hum Brain Mapp 33:1-13, 2012. V C 2011 Wiley Periodicals, Inc.
916 citations
TL;DR: The proposed GQI method can be applied to grid or shell sampling schemes and can provide directional and quantitative information about the crossing fibers.
Abstract: Based on the Fourier transform relation between diffusion magnetic resonance (MR) signals and the underlying diffusion displacement, a new relation is derived to estimate the spin distribution function (SDF) directly from diffusion MR signals. This relation leads to an imaging method called generalized q-sampling imaging (GQI), which can obtain the SDF from the shell sampling scheme used in q-ball imaging (QBI) or the grid sampling scheme used in diffusion spectrum imaging (DSI). The accuracy of GQI was evaluated by a simulation study and an in vivo experiment in comparison with QBI and DSI. The simulation results showed that the accuracy of GQI was comparable to that of QBI and DSI. The simulation study of GQI also showed that an anisotropy index, named quantitative anisotropy, was correlated with the volume fraction of the resolved fiber component. The in vivo images of GQI demonstrated that SDF patterns were similar to the ODFs reconstructed by QBI or DSI. The tractography generated from GQI was also similar to those generated from QBI and DSI. In conclusion, the proposed GQI method can be applied to grid or shell sampling schemes and can provide directional and quantitative information about the crossing fibers.
741 citations
TL;DR: This work confirms that the salience network drives the switching between default mode and central executive networks and that the novel technique is repeatable.
533 citations