Tracts in the limbic system show microstructural alterations post COVID-19 recovery.

Delirium, memory loss, attention deficit and fatigue are frequently reported by COVID survivors, yet the neurological pathways underlying these symptoms are not well understood. To study the possible mechanisms for these long-term sequelae after COVID-19 recovery, we investigated the microstructural properties of white matter in Indian cohorts of COVID-recovered patients and healthy controls. For the cross-sectional study presented here, we recruited 44 COVID-recovered patients and 29 healthy controls in New Delhi, India. Using deterministic whole-brain tractography on the acquired diffusion MRI scans, we traced 20 white matter tracts and compared fractional anisotropy, axial, mean and radial diffusivity between the cohorts. Our results revealed statistically significant differences (PFWE < 0.01) in the uncinate fasciculus, cingulum cingulate, cingulum hippocampus and arcuate fasciculus in COVID survivors, suggesting the presence of microstructural abnormalities. Additionally, in a subsequent subgroup analysis based on infection severity (healthy control, non-hospitalized patients and hospitalized patients), we observed a correlation between tract diffusion measures and COVID-19 infection severity. Although there were significant differences between healthy controls and infected groups, we found no significant differences between hospitalized and non-hospitalized COVID patients. Notably, the identified tracts are part of the limbic system and orbitofrontal cortex, indicating microstructural differences in neural circuits associated with memory and emotion. The observed white matter alterations in the limbic system resonate strongly with the functional deficits reported in Long COVID. Overall, our study provides additional evidence that damage to the limbic system could be a neuroimaging signature of Long COVID. The findings identify targets for follow-up studies investigating the long-term physiological and psychological impact of COVID-19.

Assistive Completion of Agrammatic Aphasic Sentences: Amalgamation of NLP and Neurolinguistics-based Synthetic Dataset.

Damage to the inferior frontal gyrus (Broca's area) can cause agrammatic aphasia wherein patients, although able to comprehend, lack the ability to form complete sentences. This inability leads to communication gaps which cause difficulties in their daily lives. The usage of assistive devices can help in mitigating these issues and enable the patients to communicate effectively. However, due to lack of large scale studies of linguistic deficits in aphasia, research on such assistive technology is relatively limited. In this work, we present two contributions that aim to re-initiate research and development in this field. Firstly, we propose a model that uses linguistic features from small scale studies on aphasia patients and generates large scale datasets of synthetic aphasic utterances from grammatically correct datasets. We show that the mean length of utterance, the noun/verb ratio, and the simple/complex sentence ratio of our synthetic datasets correspond to the reported features of aphasic speech. Further, we demonstrate how the synthetic datasets may be utilized to develop assistive devices for aphasia patients. The pre-trained T5 transformer is fine-tuned using the generated dataset to suggest 5 corrected sentences given an aphasic utterance as input. We evaluate the efficacy of the T5 model using the BLEU and cosine semantic similarity scores. Affirming results with BLEU score of 0.827/1.00 and semantic similarity of 0.904/1.00 were obtained. These results provide a strong foundation for the concept that a synthetic dataset based on small scale studies on aphasia can be used to develop effective assistive technology.Clinical relevance- We demonstrate the utilization of Natural Language Processing (NLP) for developing assistive technology for Aphasia patients. While disorders like Broca's aphasia offer a small sample size of patients and data, synthetic linguistic models like ours offer extensive scope for developing assistive technology and rehabilitation monitoring.

Structural Connectomes of COVID-Survivors Show Disruption in Global Integration and Small-Worldness.

Even after recovery from the COVID-19 infection, there have been a multitude of cases reporting post-COVID neurological symptoms including memory loss, brain fog, and attention deficit. Many studies have observed localized microstructural damages in the white matter regions of COVID survivors, indicating potential damage to the axonal pathways in the brain. Therefore, in this study, we have investigated the global impact of localized damage to white matter tracts using graph theoretical analysis of the structural connectome of 45 COVID-recovered subjects and 30 Healthy Controls (HCs). We have implemented Diffusion Tensor Imaging based reconstruction followed by deterministic tractography to extract structural connections among different regions of the brain. Interpreting this structural connectivity as weighted undirected graphs, we have used graph theoretical measures like global efficiency, characteristic path length (CPL), clustering coefficient (CC), modularity, Fiedler value, and assortativity coefficient to quantify the global integration, segregation, and robustness of the brain networks. We statistically compare the cohorts based on these graph measures by employing permutation testing for 100,000 permutations. Post multiple comparisons error correction, we find that the COVID-recovered cohort shows a reduction in global efficiency and CC while they exhibit higher modularity and CPL. This disruption of the balance between global integration and segregation indicates the loss of small-world property in COVID survivors' connectomes which has been linked with other disorders such as cognitive impairment and Alzheimer's. Overall, our study sheds light on the alterations in structural connectivity and its role in post-COVID symptoms.