Biology subjects

Hafiz, R.

Publications and source records attributed to Hafiz, R..

5 recordsLinked to original sources

bioRxiv2024

Mapping Serotonergic Dynamics using Drug-Modulated Molecular Connectivity

Understanding the complex workings of the brain is one of the most significant challenges in neuroscience, providing insights into normal brain function, neurological diseases, and the effects of potential therapeutics. A major challenge in this field lies in the limitations of traditional brain imaging techniques, which often capture only fragments of the complex puzzle of brain function. Our research employs a novel approach termed "molecular connectivity" (MC), which combines the strengths of various imaging methods to provide a comprehensive view of how specific molecules, such as the serotonin transporter, interact across different brain regions and influence brain function. This innovative technique bridges the gap between functional magnetic resonance imaging (fMRI), known for its ability to monitor brain activity by tracking blood flow, and positron emission tomography (PET), which visualizes specific molecular changes. By integrating these methods, we can better understand how drugs influence brain function. Our study focuses on the application of dynamic [11C]DASB PET scans to map the distribution of serotonin transporters, key players in regulating mood and emotions and examines how these transporters are altered following exposure to methylenedioxymethamphetamine (MDMA), which is commonly known as ecstasy. Through a detailed comparison of MCs with traditional measures of brain connectivity, we reveal significant patterns that closely align with physiological changes. Our results revealed clear changes in molecular connectivity after a single dose of MDMA, establishing a direct link between the effects of drugs on serotonin transporter occupancy and changes in the functional brain network. This work offers a novel methodology for the in-depth study of brain function at the molecular level and opens new pathways for understanding how drugs modulate brain activity.

neuroscience↗

bioRxiv2023

'Pscore' - A Novel Percentile-Based Metric to Accurately Assess Individual Deviations in Non-Gaussian Distributions of Quantitative MRI Metrics

BACKGROUNDQuantitative MRI metrics could be used in personalized medicine to assess individuals against normative distributions. Conventional Zscore analysis is inadequate in the presence of non-Gaussian distributions. Therefore, if quantitative MRI metrics deviate from normality, an alternative is needed. PURPOSETo confirm non-Gaussianity of diffusion MRI (dMRI) metrics on a publicly available dataset, and to propose a novel percentile-based method, Pscore to address this issue. STUDY TYPERetrospective cohort POPULATION961 healthy young-adults (age:22-35 years, Females:53%) from the Human Connectome Project FIELD STRENGTH/SEQUENCE3-T, spin-echo diffusion echo-planar imaging, T1-weighted: MPRAGE ASSESSMENTThe dMRI data were preprocessed using the TORTOISE pipeline. Forty-eight regions of interest (ROIs) from the JHU-atlas were redrawn on a study-specific diffusion tensor (DT) template and average values were computed from various DT and mean apparent propagator (MAP) metrics. For each ROI, percentile ranks across participants were computed to generate Pscores- which normalized the difference between the median and a participants value with the corresponding difference between the median and the 5th/95th percentile values. STATISTICAL TESTSROI-wise distributions were assessed using Log transformations, Zscore, and the Pscore methods. The percentages of extreme values above-95th and below-5th percentile boundaries (PEV>95(%),PEV<5(%)) were also assessed in the overall white matter. Bootstrapping was performed to test the reliability of Pscores in small samples (n=100) using 100 iterations. RESULTSThe dMRI metric distributions were systematically non-Gaussian, including positively skewed (e.g., mean and radial diffusivity) and negatively skewed (e.g., fractional and propagator anisotropy) metrics. This resulted in unbalanced tails in Zscore distributions (PEV>95=5%,PEV<5=5%) whereas Pscore distributions were symmetric and balanced (PEV>95=PEV<5=5%); even for small bootstrapped samples (average [Formula]. DATA CONCLUSIONThe inherent skewness observed for dMRI metrics may preclude the use of conventional Zscore analysis. The proposed Pscore method may help estimating individual deviations more accurately in skewed normative data, even from small datasets.

neuroscience↗

bioRxiv2022

ALFF Response Interaction With Learning During Feedback in Individuals With Multiple Sclerosis

Amplitude of low-frequency fluctuations (ALFF) is defined as changes of BOLD signal during resting state (RS) brain activity. Previous studies identified differences in RS activation between healthy and multiple sclerosis (MS) participants. However, no research has investigated the relationship between ALFF and learning in MS. We thus examine this here. Twenty-five MS and 19 healthy participants performed a paired-associate word learning task where participants were presented with extrinsic or intrinsic performance feedback. Compared to healthy participants, MS participants showed higher local brain activation in the right thalamus. We also observed a positive correlation in the MS group between ALFF and extrinsic feedback within the left inferior frontal gyrus, and within the left superior temporal gyrus in association with intrinsic feedback. Healthy participants showed a positive correlation in the right fusiform gyrus between ALFF and extrinsic feedback. Findings suggest that while MS participants do not show a feedback learning impairment compared to the healthy participants, ALFF differences might suggest a general maladaptive pattern of task unrelated thalamic activation and adaptive activation in frontal and temporal regions. Results indicate that ALFF can be successfully used at capturing pathophysiological changes in local brain activation in MS in association with learning through feedback.

neuroscience↗

bioRxiv2022

Assessing functional connectivity differences and work-related fatigue in surviving COVID-negative patients.

The Coronavirus Disease 2019 (COVID-19) has affected all aspects of life around the world. Neuroimaging evidence suggests the novel coronavirus can attack the central nervous system (CNS), causing cerebro-vascular abnormalities in the brain. This can lead to focal changes in cerebral blood flow and metabolic oxygen consumption rate in the brain. However, the extent and spatial locations of brain alterations in COVID-19 survivors are largely unknown. In this study, we have assessed brain functional connectivity (FC) using resting-state functional MRI (RS-fMRI) in 38 (25 males) COVID patients two weeks after hospital discharge, when PCR negative and 31 (24 males) healthy subjects. FC was estimated using independent component analysis (ICA) and dual regression. When compared to the healthy group, the COVID group demonstrated significantly enhanced FC in the basal ganglia and precuneus networks (family wise error (fwe) corrected, pfwe < 0.05), while, on the other hand, reduced FC in the language network (pfwe < 0.05). The COVID group also experienced higher fatigue levels during work, compared to the healthy group (p < 0.001). Moreover, within the precuneus network, we noticed a significant negative correlation between FC and fatigue scores across groups (Spearmans {rho} = - 0.47, p = 0.001, r2 = 0.22). Interestingly, this relationship was found to be significantly stronger among COVID survivors within the left parietal lobe, which is known to be structurally and functionally associated with fatigue in other neurological disorders. Significance StatementEarly neuroimaging studies have mostly focused on structural MRI imaging to report brain abnormalities in acutely ill COVID-19 patients. It is not clear whether functional abnormalities co-exist with structural alterations in patients who have survived the infection and have been discharged from the hospital. A few recent studies have emerged which attempted to address the structural/functional alterations. However, further investigations across different sites are necessary for more conclusive inference. More importantly, fatigue is a highly prevalent symptom among COVID survivors, therefore, the relations of brain imaging abnormalities to fatigue should be investigated. In this study, we try to address these gaps, by collecting imaging data from COVID survivors, now PCR negative, and healthy subjects from a single site - the Indian Institute of Technology (IIT), Delhi, India. Furthermore, this is a continuation of an ongoing study. We have recently shown structural abnormalities and stronger gray matter volume (GMV) correlates of self-reported fatigue in this group of COVID survivors compared to healthy subjects (Hafiz et al., 2022).

neuroscience↗

bioRxiv2021

Striatal and prefrontal D2R and SERT distributions contrastingly correlate with default-mode connectivity

The molecular substrate of resting-state functional connectivity (rs-FC) remains poorly understood. We aimed to elucidate interactions of dopamine D2 receptor (D2R) and serotonin transporter (SERT) availabilities in main dopaminergic and serotonergic projection areas with the default-mode network (DMN) and two other resting-state networks (RSNs), the salience (SN) and sensorimotor networks (SMN). We performed simultaneous PET/fMRI scans in rats using [11C]raclopride and [11C]DASB to image D2R and SERT distributions, showing for the first time direct relationships between rs-FC and molecular properties of the rodent brain. We found negative associations between CPu D2R availability and all RSNs investigated. Strikingly, medial prefrontal SERT correlated both positively with anterior DMN rs-FC and negatively with rs-FC between the other networks, underlining serotonins intricate role in this region. By further elucidating the link between molecular brain properties and its network-level function, our data support future diagnostic and therapeutic strategies. TeaserSimultaneous PET/fMRI indicates direct associations between monoaminergic neurotransmission and brain functional networks.

neuroscience↗