Coralia Sosa-Pérez

Background: Acquired brain injury (ABI) leads to cognitive, emotional, and social impairments that substantially affect quality of life. Although cortical lesions have traditionally received more attention, increasing evidence highlights the importance of the integrity of major white matter association tracts. However, few studies have simultaneously examined cognitive, affective, and social domains within a tractography framework. Methods: In this exploratory pilot study, ten ABI patients underwent diffusion-based tractography of the principal association tracts—the superior and inferior longitudinal fasciculi, the uncinate fasciculus, the inferior fronto-occipital fasciculus, and the cingulum—together with a comprehensive neuropsychological battery covering global cognition, executive functions, memory, emotional symptoms, and empathy. Results: Marked interindividual variability was observed in both tract profiles and neuropsychological outcomes. Findings revealed paradoxical associations, such as larger volumes of the left superior longitudinal fasciculus being linked to poorer cognitive performance, suggesting maladaptive reorganization. Hemispheric lateralization patterns were also identified, with the uncinate fasciculus showing differential contributions to immediate memory and working memory across hemispheres. Notably, empathy scores consistently correlated with volumes of the inferior longitudinal fasciculus, the uncinate fasciculus, and the cingulum, in line with recent evidence on the structural basis of socio-emotional outcomes after ABI. Conclusions: Although limited by sample size, this study provides novel evidence regarding the structure–function paradox, hemispheric specialization, and the clinical relevance of empathy in ABI. Overall, the results support the integration of tractography of the main association tracts with neuropsychological assessment as complementary tools to advance personalized neurorehabilitation.

Background: Acquired brain injury (ABI) often produces heterogeneous cognitive and emotional outcomes that are not fully explained by conventional neuropsychological testing. Diffusion tensor imaging (DTI) tractography may capture patient-specific patterns of white matter connectivity and thereby complement clinical assessment. Methods: We conducted an exploratory case series of nine patients in the subacute phase of ABI (traumatic brain injury or subarachnoid hemorrhage). Each underwent a brief cognitive-emotional battery and 1.5 T DTI with deterministic tractography of major association tracts and the corpus callosum. Tract lateralization was quantified using the Structural Asymmetry Index (SAI), and individual profiles were compared with neuropsychological and emotional data. Results: Six patients met criteria for clinically significant anxiety, and four for depression, often dissociated from global cognitive screening. Tractography revealed heterogeneous asymmetry patterns, most often in the superior longitudinal fasciculus, uncinate fasciculus, and cingulum. In several cases, structural asymmetries diverged from neuropsychological findings, suggesting dissociations between behavioral testing and connectivity-based measures. Conclusions: Within-subject tract asymmetry analysis provided preliminary, potentially clinically relevant information not captured by tests alone. These findings indicate that individualized tractography could enrich the interpretation of cognitive and emotional profiles and help guide hypothesis generation for connectivity-informed neurorehabilitation.

Background: Acquired brain injury (ABI) leads to cognitive, emotional, and social impairments that substantially affect quality of life. Although cortical lesions have traditionally received more attention, increasing evidence highlights the importance of the integrity of major white matter association tracts. However, few studies have simultaneously examined cognitive, affective, and social domains within a tractography framework.Methods: In this exploratory pilot study, ten ABI patients underwent diffusion-based tractography of the principal association tracts—the superior and inferior longitudinal fasciculi, the uncinate fasciculus, the inferior fronto-occipital fasciculus, and the cingulum—together with a comprehensive neuropsychological battery covering global cognition, executive functions, memory, emotional symptoms, and empathy. Results: Marked interindividual variability was observed in both tract profiles and neuropsychological outcomes. Findings revealed paradoxical associations, such as larger volumes of the left superior longitudinal fasciculus being linked to poorer cognitive performance, suggesting maladaptive reorganization. Hemispheric lateralization patterns were also identified, with the uncinate fasciculus showing differential contributions to immediate memory and working memory across hemispheres. Notably, empathy scores consistently correlated with volumes of the inferior longitudinal fasciculus, the uncinate fasciculus, and the cingulum, in line with recent evidence on the structural basis of socio-emotional outcomes after ABI. Conclusions: Although limited by sample size, this study provides novel evidence regarding the structure–function paradox, hemispheric specialization, and the clinical relevance of empathy in ABI. Overall, the results support the integration of tractography of the main association tracts with neuropsychological assessment as complementary tools to advance personalized neurorehabilitation.

Delayed cerebral ischemia (DCI) and vasospasm are two complications of subarachnoid hemorrhages (SAHs) which entail high risks of morbidity and mortality. However, it is unknown why only some patients who suffer SAHs will experience DCI and vasospasm. The purpose of this review is to describe the main genetic single nucleotide polymorphisms (SNPs) that have demonstrated a relationship with these complications. The SNP of the nitric oxide endothelial synthase (eNOS) has been related to the size and rupture of an aneurysm, as well as to DCI, vasospasm, and poor neurological outcome. The SNPs responsible for the asymmetric dimetilarginine and the high-mobility group box 1 have also been associated with DCI. An association between vasospasm and the SNPs of the eNOS, the haptoglobin, and the endothelin-1 receptor has been found. The SNPs of the angiotensin-converting enzyme have been related to DCI and poor neurological outcome. Studies on the SNPs of the Ryanodine Receptor yielded varying results regarding their association with vasospasm.

Hyperspectral imaging (HSI) is a non-ionizing and non-contact imaging technique capable of obtaining more information than conventional RGB (red green blue) imaging. In the medical field, HSI has commonly been investigated due to its great potential for diagnostic and surgical guidance purposes. However, the large amount of information provided by HSI normally contains redundant or non-relevant information, and it is extremely important to identify the most relevant wavelengths for a certain application in order to improve the accuracy of the predictions and reduce the execution time of the classification algorithm. Additionally, some wavelengths can contain noise and removing such bands can improve the classification stage. The work presented in this paper aims to identify such relevant spectral ranges in the visual-and-near-infrared (VNIR) region for an accurate detection of brain cancer using in vivo hyperspectral images. A methodology based on optimization algorithms has been proposed for this task, identifying the relevant wavelengths to achieve the best accuracy in the classification results obtained by a supervised classifier (support vector machines), and employing the lowest possible number of spectral bands. The results demonstrate that the proposed methodology based on the genetic algorithm optimization slightly improves the accuracy of the tumor identification in ~5%, using only 48 bands, with respect to the reference results obtained with 128 bands, offering the possibility of developing customized acquisition sensors that could provide real-time HS imaging. The most relevant spectral ranges found comprise between 440.5–465.96 nm, 498.71–509.62 nm, 556.91–575.1 nm, 593.29–615.12 nm, 636.94–666.05 nm, 698.79–731.53 nm and 884.32–902.51 nm.

The main goal of brain cancer surgery is to perform an accurate resection of the tumor, preserving as much normal brain tissue as possible for the patient. The development of a non-contact and label-free method to provide reliable support for tumor resection in real-time during neurosurgical procedures is a current clinical need. Hyperspectral imaging is a non-contact, non-ionizing, and label-free imaging modality that can assist surgeons during this challenging task without using any contrast agent. In this work, we present a deep learning-based framework for processing hyperspectral images of in vivo human brain tissue. The proposed framework was evaluated by our human image database, which includes 26 in vivo hyperspectral cubes from 16 different patients, among which 258,810 pixels were labeled. The proposed framework is able to generate a thematic map where the parenchymal area of the brain is delineated and the location of the tumor is identified, providing guidance to the operating surgeon for a successful and precise tumor resection. The deep learning pipeline achieves an overall accuracy of 80% for multiclass classification, improving the results obtained with traditional support vector machine (SVM)-based approaches. In addition, an aid visualization system is presented, where the final thematic map can be adjusted by the operating surgeon to find the optimal classification threshold for the current situation during the surgical procedure.

Hyperspectral imaging (HSI) allows for the acquisition of large numbers of spectral bands throughout the electromagnetic spectrum (within and beyond the visual range) with respect to the surface of scenes captured by sensors. Using this information and a set of complex classification algorithms, it is possible to determine which material or substance is located in each pixel. The work presented in this paper aims to exploit the characteristics of HSI to develop a demonstrator capable of delineating tumor tissue from brain tissue during neurosurgical operations. Improved delineation of tumor boundaries is expected to improve the results of surgery. The developed demonstrator is composed of two hyperspectral cameras covering a spectral range of 400–1700 nm. Furthermore, a hardware accelerator connected to a control unit is used to speed up the hyperspectral brain cancer detection algorithm to achieve processing during the time of surgery. A labeled dataset comprised of more than 300,000 spectral signatures is used as the training dataset for the supervised stage of the classification algorithm. In this preliminary study, thematic maps obtained from a validation database of seven hyperspectral images of in vivo brain tissue captured and processed during neurosurgical operations demonstrate that the system is able to discriminate between normal and tumor tissue in the brain. The results can be provided during the surgical procedure (~1 min), making it a practical system for neurosurgeons to use in the near future to improve excision and potentially improve patient outcomes.