Biology subjects

Saha, D. K.

Publications and source records attributed to Saha, D. K..

8 recordsLinked to original sources

bioRxiv2024

Analysis of Longitudinal Change Patterns in Developing Brain Using Functional and Structural Magnetic Resonance Imaging via Multimodal Fusion

Functional and structural magnetic resonance imaging (fMRI and sMRI) are complementary approaches that can be used to study longitudinal brain changes in adolescents. Each individual modality offers distinct insights into the brain. Each individual modality may overlook crucial aspects of brain analysis. By combining them, we can uncover hidden brain connections and gain a more comprehensive understanding. In previous work, we identified multivariate patterns of change in whole-brain function during adolescence. In this work, we focus on linking functional change patterns (FCPs) to brain structure. We introduce two approaches and applied them to data from the Adolescent Brain and Cognitive Development (ABCD) dataset. First, we evaluate voxelwise sMRI-FCP coupling to identify structural patterns linked to our previously identified FCPs. Our approach revealed multiple interesting patterns in functional network connectivity (FNC) and gray matter volume (GMV) data that were linked to subject level variation. FCP components 2 and 4 exhibit extensive associations between their loadings and voxel-wise GMV data. Secondly, we leveraged a symmetric multimodal fusion technique called multiset canonical correlation analysis (mCCA) + joint independent component analysis (jICA). Using this approach, we identify structured FCPs such as one showing increased connectivity between visual and sensorimotor domains and decreased connectivity between sensorimotor and cognitive control domains, linked to structural change patterns (SCPs) including alterations in the bilateral sensorimotor cortex. Interestingly, females exhibit stronger coupling between brain functional and structural changes than males, highlighting sex-related differences. The combined results from both asymmetric and symmetric multimodal fusion methods underscore the intricate sex-specific nuances in neural dynamics. By utilizing two complementary multimodal approaches, our study enhances our understanding of the dynamic nature of brain connectivity and structure during the adolescent period, shedding light on the nuanced processes underlying adolescent brain development.

neuroscience↗

bioRxiv2024

Neuromark PET: A multivariate method for Estimating and comparing whole brain functional networks and connectomes from fMRI and PET data

Positron emission tomography (PET) and magnetic resonance imaging (MRI) are both widely used neuroimaging techniques to study brain functional and molecular connec-tivity. Although whole brain resting functional MRI (fMRI) connectomes (a matrix describing the inter-regional connectivity patterns) are widely used, the integration or association of whole brain molecular connectomes with PET data are rarely done. This likely stems from the fact that PET data is typically analyzed by using a region of inter-est approach, while whole brain spatial networks and their connectivity (covariation) receive much less attention. As a result, to date, there have been little focus on di-rectly comparing whole brain PET and fMRI connectomes. In this study, we present a method that uses spatially constrained independent component analysis (scICA) (uti-lizing fMRI components as spatial priors) to estimate corresponding (Amyloid) PET and fMRI connectomes and examine the relationship between them using datasets that include individuals with mild cognitive impairment (MCI). Our results demonstrate highly modularized PET connectome patterns that complement those identified from resting fMRI. In particular, fMRI showed strong intra-domain connectivity with inter-domain anticorrelation in sensorimotor and visual domains as well as default mode network. PET amyloid data showed similar strong intra-domain effects, but showed much higher correlations within cognitive control and default mode domains, as well as anticorrelation between cerebellum and other domains. The estimated fMRI informed PET networks have similar, but not identical, network spatial patterns to the resting fMRI networks, with the fMRI informed PET networks being slightly smoother and, in some cases, showing variations in subnodes. To further compare the two modalities, we also analyzed the differences between individuals with MCI receiving medication versus a placebo. Results show both common and modality specific treatment effects on fMRI and PET connectomes. From our fMRI analysis, we observed higher connec-tivity differences in various regions, such as the connection between the thalamus and middle occipital gyrus, as well as the insula and right middle occipital gyrus. Mean-while, the PET analysis revealed increased activation between the anterior cingulate cortex and the left inferior parietal lobe, along with other regions, in individuals who received medication versus placebo. In sum, our novel approach identifies correspond-ing whole-brain fMRI informed PET and fMRI networks and connectomes. While we observed common patterns of network connectivity, our analysis of the MCI treatment and placebo groups revealed that each modality captures modality and group specific information about brain networks, highlighting differences between the two groups in both network expression and network connectivity.

neuroscience↗

bioRxiv2023

In Silico Investigations of Adaptive Therapy Using a Single Cytotoxic or a Single Cytostatic Drug

Adaptive therapy, as per the dose modulation, dose-skipping, or intermittent treatment protocol works well for treatment using a single cytotoxic drug, under a wide range of scenarios and parameter settings. In contrast, adaptive therapy works well only under a limited number of scenarios and parameter settings when using a single cytostatic drug. In general, adaptive therapy works best under conditions of higher fitness cost, higher replacement rate, higher turnover. Adaptive therapy works best when drug dosages are changed as soon as a change in tumor burden is detected. In general, it is better to pause treatment sooner than later, when the tumor is shrinking If the amount of drug used is too low, it is unable to control the sensitive cells and the tumor grows. However, if the drug dose is too high, it quickly selects for resistant cells and eventually the tumor grows out of control. However, there appears to be intermediate levels of dosing, which we call the minimum effective dose, which is able to control the sensitive cells but is not high enough to select for the resistant cells to grow out of control.

bioinformatics↗

bioRxiv2023

In Silico Investigations of Adaptive Therapy Using Two Cytotoxic or Two Cytostatic Drugs

While the dose modulation (DM) protocols (DM Cocktail Tandem, DM Ping-Pong Alternate Every Cycle, DM Ping-Pong on Progression) involves adjusting drug dosages when the tumor burden changes, the fixed-dose (FD) protocols involves administering a specific, constant dosage of the drug only when the tumor is growing (Dose-Skipping) or when the absolute tumor burden is above the baseline level until it reduces to a certain percentage of the baseline (Intermittent). Moreover, two different drugs can be administered simultaneously (cocktail), or the drugs can be switched such that only one drug is applied at a given time (ping-pong), either every cycle (ping-pong alternate every cycle) or when the tumor grows (ping-pong on progression). The dose modulation protocols work well when treated with two cytotoxic drugs, however, the ping-pong protocols (DM Ping-Pong Alternate Every Cycle, DM Ping-Pong on Progression, FD Ping-Pong Intermittent) work well when treated with two cytostatic drugs. In general, adaptive therapy, using either two cytotoxic or two cytostatic drugs works best under conditions of high competition, such as high fitness cost, high replacement rates, and high turnover, although treatment using two cytostatic drugs works best under low turnover in many cases. Adaptive therapy works best when drug dosages are changed as soon as a change in tumor burden is detected, and it is best to pause treatment sooner rather than later when the tumor is shrinking. Adaptive therapy works best when an intermediate level of drug dosage is used, and both treatment with too little or too much drug leads to poor survival outcome.

bioinformatics↗

bioRxiv2022

dcSBM: A federated constrained source-based morphometry approach for multivariate brain structure mapping

The examination of multivariate brain morphometry patterns has gained attention in recent years, especially for their powerful exploratory capabilities in the study of differences between patients and controls. Among many existing methods and tools for analysis of brain anatomy based on structural magnetic resonance imaging (sMRI) data, data-driven source based morphometry (SBM) focuses on the exploratory detection of such patterns. Constrained source-based morphometry (constrained SBM) is a widely used semi-blind extension of SBM that enables extracting maximally independent reference-alike sources using the constrained independent component analysis (ICA) approach. In order to operate, constrained SBM needs the data to be locally accessible. However, there exist many reasons (e.g., the concerns of revealing identifiable rare disease information, or violating strict IRB policies) that may preclude access to data from different sites. In this scenario, constrained SBM fails to leverage the benefits of decentralized data. To mitigate this problem, we present a novel approach: decentralized constrained source-based morphometry (dcSBM). In dcSBM, the original data never leaves the local site. Each site operates constrained ICA on their private local data while using a common distributed computation platform. Then, an aggregator/master node aggregates the results estimated from each local site and applies statistical analysis to find out the significant sources. In our approach, we first use UK Biobank sMRI data to investigate the reliability of our dcSBM algorithm. Finally, we utilize two additional multi-site patient datasets to validate our model by comparing the resulting group difference estimates from both centralized and decentralized constrained SBM.

neuroscience↗

bioRxiv2020

Statelets: high dimensional predominant shapes in dynamic functional network connectivity

Dynamic functional network connectivity (dFNC) analysis is a widely used approach for capturing brain activation patterns, connectivity states, and network organization. However, a typical sliding window plus clustering (SWC) approaches for analyzing dFNC continuously models the system through a fixed set of connectivity patterns or states. It assumes these patterns are span throughout the brain, but in practice, they are more spatially constrained and temporally short-lived. Thus, SWC is not designed to capture transient dynamic changes nor heterogeneity across subjects/time. Here, we adapt time series motifs to model the temporal dynamics of functional connectivity. We propose a state-space data mining approach that combines a probabilistic pattern summarization framework called Statelets -- a subset of high dimensional state-shape prototypes capturing the dynamics. We handle scale differences using the earth mover distance and utilize kernel density estimation to build a probability density profile for local motifs. We apply the framework to study dFNC collected from patients with schizophrenia (SZ) and healthy control (HC). Results demonstrate SZ subjects exhibit reduced modularity in their brain network organization relative to HC. These statelets in the HC group show more recurrence across the dFNC time-course compared to the SZ. An analysis of the consistency of the connections across time reveals significant differences within visual, sensorimotor, and default mode regions where HC subjects show higher consistency than SZ. The introduced statelet-approach also enables the handling of dynamic information in cross-modal applications to study healthy and disordered brains and multi-modal fusion within a single dataset.

neuroscience↗

bioRxiv2020

A classification-based approach to estimate the number of resting fMRI dynamic functional connectivity states

Recent work has focused on the study of dynamic (vs static) brain connectivity in resting fMRI data. In this work, we focus on temporal correlation between time courses extracted from coherent networks or components called functional network connectivity (FNC). Dynamic functional network connectivity (dFNC) is most commonly estimated using a sliding window-based approach to capture short periods of FNC change. These data are then clustered to estimate transient connectivity patterns or states. Determining the number of states is a challenging problem. The elbow criterion is a widely used approach to determine the optimal number of states. In our work, we present an alternative approach that evaluates classification (e.g. healthy controls versus patients) as a measure to select the optimal number of states (clusters). We apply different classification strategies to perform classification between healthy controls (HC) and patients with schizophrenia (SZ) for different numbers of states (i.e. varying the model order in the clustering algorithm). We compute cross-validated accuracy for different model orders to evaluate the classification performance. Our results are consistent with our earlier work which shows that overall accuracy improves when dynamic connectivity measures are used separately or in combination with static connectivity measures. Results also show that the optimal model order for classification is different from that using the standard k-means model selection method and that such optimization improves resulting in cross-validated accuracy. The optimal model order obtained from the proposed approach also gives significantly improved classification performance over the traditional model selection method. In sum, the observed results suggest that if ones goal is to perform classification, using the proposed approach as a criterion for selecting the optimal number of states in dynamic connectivity analysis leads to improved accuracy in hold-out data.

neuroscience↗

bioRxiv2019

dSNE: a visualization approach for use with decentralized data

Privacy concerns for rare disease data, institutional or IRB policies, access to local computational or storage resources or download capabilities are among the reasons that may preclude analyses that pool data to a single site. A growing number of multi-site projects and consortia were formed to function in the federated environment to conduct productive research under constraints of this kind. In this scenario, a quality control tool that visualizes decentralized data in its entirety via global aggregation of local computations is especially important, as it would allow the screening of samples that cannot be jointly evaluated otherwise. To solve this issue, we present two algorithms: decentralized data stochastic neighbor embedding, dSNE, and its differentially private counterpart, DP-dSNE. We leverage publicly available datasets to simultaneously map data samples located at different sites according to their similarities. Even though the data never leaves the individual sites, dSNE does not provide any formal privacy guarantees. To overcome that, we rely on differential privacy: a formal mathematical guarantee that protects individuals from being identified as contributors to a dataset. We implement DP-dSNE with AdaCliP, a method recently proposed to add less noise to the gradients per iteration. We introduce metrics for measuring the embedding quality and validate our algorithms on these metrics against their centralized counterpart on two toy datasets. Our validation on six multi-site neuroimaging datasets shows promising results for the quality control tasks of visualization and outlier detection, highlighting the potential of our private, decentralized visualization approach.

neuroscience↗