Biology subjects

Thakuri, D. S.

Publications and source records attributed to Thakuri, D. S..

3 recordsLinked to original sources

bioRxiv2022

Amyloid-beta biomarkers in Braak stages and their predictive relationships with cognitive impairment: Support vector machine and deep learning approaches

Amyloid-beta (A{beta}) and tau tangles are hallmarks of Alzheimers disease. A{beta} distributions in the tau-defined Braak staging regions and their multivariate predictive relationships with mild cognitive impairment (MCI) are not known. In this study, we used PiB PET data from 60 participants (33 with MCI and 27 healthy controls (HC)), quantified A{beta} as distribution volume ratio (DVR) in Braak regions, and compared between MCI and controls to test the hypothesis that DVR alters with declining cognition. We found elevated DVR in participants with MCI, especially in the spatial distribution of Braak stages III-IV and V-VII, while an alteration in Braak stage I-II was near the statistical significance. DVR markers correlated with cognitive status, especially in Braak stages III-IV and VI-V. To evaluate whether these markers are predictive of cognitive dysfunction, we designed support vector machine and artificial neural network models. These methods showed predictive multivariate relationships between A{beta} makers of Braak regions and cognitive impairment. Overall, these results highlight the importance of computer-aided research efforts for understanding AD pathophysiology.

neuroscience↗

bioRxiv2021

Neuro-anatomical and neuro-molecular markers in salience network and their integration in machine learning-based prediction of cognitive dysfunction in mild cognitive impairment

Recent studies indicate disrupted functional mechanisms of salience network regions, especially right anterior insula (RAI), left AI (LAI), and anterior cingulate cortex (ACC), in mild cognitive impairment (MCI). However, the underlying neuro-anatomical and neuro-molecular mechanisms in these regions are not clearly understood yet. It is also unknown whether integration of multi-modal neuro-anatomical and neuro-molecular markers could predict cognitive impairment better in MCI. Herein we quantified neuro-anatomical volumetric markers via structural magnetic resonance imaging (sMRI) and neuro-molecular amyloid markers via positron emission tomography with Pittsburgh compound B (PET PiB) in SN regions of MCI (n = 33) and healthy controls (n = 27). We found that neuro-anatomical markers are significantly reduced while neuro-molecular markers are significantly elevated in SN nodes of MCI compared to healthy controls (p < 0.05). These altered markers in MCI patients were associated with their worse cognitive performance (p < 0.05). Our machine learning-based modeling further suggested that the integration of multi-modal markers predicts cognitive impairment in MCI superiorly compared to using single modality-specific markers. Overall, these findings shed light on the underlying neuro-anatomical volumetric and neuro-molecular amyloid alterations in SN regions and show the significance of multi-modal markers integration approach in better predicting cognitive impairment in MCI.

neuroscience↗

bioRxiv2021

Disrupted controlling mechanism of salience network on default-mode network and central-executive network in schizophrenia

Neuroimaging studies suggest that the human brain consists of intrinsically organized large-scale neural networks. Among those networks, the interplay among default-mode network (DMN), salience network (SN), and central-executive network (CEN)has been widely employed to understand the functional interaction patterns in health and diseases. This triple network model suggests that SN causally controls DMN and CEN in healthy individuals. This interaction is often referred to as the dynamic controlling mechanism of SN. However, such interactions are not well understood in individuals with schizophrenia. In this study, we leveraged resting state functional magnetic resonance imaging (fMRI) data of schizophrenia (n = 67) and healthy controls (n = 81) to evaluate the functional interactions among DMN, SN, and CEN using dynamical causal modeling. In healthy controls, our analyses replicated previous findings that SN regulates DMN and CEN activities (Mann-Whitney U test; p < 10-8). In schizophrenia, however, our analyses revealed the disrupted SN-based controlling mechanism on DMN and CEN (Mann-Whitney U test; p < 10-16). These results indicate that the disrupted controlling mechanism of SN on two other neural networks may be a candidate neuroimaging phenotype in schizophrenia.

neuroscience↗