Biology subjects

Ajmera, S.

Publications and source records attributed to Ajmera, S..

4 recordsLinked to original sources

bioRxiv2026

Multi-site MRI analysis of morphometric differences in brain regions in the presence of hearing loss and tinnitus across the adult lifespan

The impact of age and hearing loss on the brain has garnered significant attention, as both factors have been implicated in the development of cognitive impairment or dementia. In this study, we investigated the impact of hearing loss and tinnitus on gray matter in the brain, while accounting for age. We used a comprehensive secondary analysis of structural MRI data obtained from multiple research sites (256 unique individuals) using voxel-based and surface-based morphology. After harmonization of this multi-site brain data, our research replicated the previously reported finding of age-related decline in total cortical volume, but there was no significant effect of either hearing loss or tinnitus on total cortical volume. When a region of interest analysis was conducted, the hippocampus emerged as the only brain region that showed a direct impact of hearing loss, after accounting for variance associated with age. This effect on hippocampal volume was evident in our sample from age 52 years onwards; when adjusted for hearing loss, the decline began at age 56 years. For the presence of tinnitus, ventral posterior cingulate gyrus showed main effects with respect to cortical volume and surface area while medial occipito-temporal gyrus and operculum of the inferior frontal gyrus showed significant main effects only with surface area. Post-hoc analysis revealed that posterior cingulate gyrus showed significantly higher volume and larger surface area in individuals with tinnitus compared to those without tinnitus. Similarly medial occipito-temporal gyrus surface area was increased whereas surface area of the inferior frontal opercular gyrus was reduced in those with tinnitus when compared to those without tinnitus. Notably, while past studies have reported that the presence of tinnitus appeared to moderate some of these effects in certain participant groups, our results suggest a more complex relationship between sensory degradation, chronic tinnitus, and brain structure in individuals across the adult lifespan. HighlightsO_LIHearing loss and tinnitus can exacerbate regional brain atrophy in the adult lifespan. C_LIO_LIHigh-frequency hearing loss affects auditory cortex gray matter volume to a larger degree in older age. C_LIO_LIHearing loss may accelerate decline in hippocampal volume by about 4 years. C_LIO_LIChronic subjective tinnitus is associated with a larger volume of cingulate cortex, increased surface area in cingulate cortex and the lingual gyrus, and decreased surface area of frontal operculum compared to controls. C_LIO_LITinnitus-related effects on regional brain atrophy are not modified by the degree of hearing deficits. C_LI

neuroscience↗

bioRxiv2024

Return of intracranial beta oscillations and traveling waves with recovery from traumatic brain injury

Traumatic brain injury (TBI) remains a pervasive clinical problem associated with significant morbidity and mortality. However, TBI remains clinically and biophysically ill-defined, and prognosis remains difficult even with the standardization of clinical guidelines and advent of multimodality monitoring. Here we leverage a unique data set from TBI patients implanted with either intracranial strip electrodes during craniotomy or quad-lumen intracranial bolts with depth electrodes as part of routine clinical practice. By extracting spectral profiles of this data, we found that the presence of narrow-band oscillatory activity in the beta band (12-30 Hz) closely corresponds with the neurological exam as quantified with the standard Glasgow Coma Scale (GCS). Further, beta oscillations were distributed over the cortical surface as traveling waves, and the evolution of these waves corresponded to recovery from coma, consistent with the putative role of waves in perception and cognitive activity. We consequently propose that beta oscillations and traveling waves are potential biomarkers of recovery from TBI. In a broader sense, our findings suggest that emergence from coma results from recovery of thalamo-cortical interactions that coordinate cortical beta rhythms.

neuroscience↗

bioRxiv2024

Single-cell transcriptomics identifies a p21-activated kinase important for survival of the zoonotic parasite Fasciola hepatica

Knowledge on the cell types and cell-specific gene expression of multicellular pathogens facilitates drug discovery and allows gaining a deeper understanding of pathogen biology. By utilizing single-cell RNA sequencing (scRNA-seq), we analyzed 19,581 cells of a globally prevalent parasitic flatworm, the liver fluke Fasciola hepatica, which causes a neglected tropical disease and zoonosis known as fascioliasis. We identified 15 distinct clusters, including cells of the reproductive tract and gastrodermis, and report the identification and transcriptional characterization of potential differentiation lineages of stem cells within this parasite. Furthermore, a previously unrecognized ELF5- and TRPMPZQ-expressing muscle cell type was discovered, characterized by high expression of protein kinases. Among these, the p21-activated kinase PAK4 was essential for parasite survival. These data provide novel insight into the cellular composition of a complex multicellular parasite and demonstrate how gene expression at single-cell resolution can serve as a resource for the identification of new drug targets.

molecular biology↗

bioRxiv2019

Decoding task-specific cognitive states with slow, directed functional networks in the human brain

Flexible functional interactions among brain regions mediate critical cognitive functions. Such interactions can be measured from functional magnetic resonance imaging (fMRI) data with either instantaneous (zero-lag) or lag-based (time-lagged) functional connectivity; only the latter approach permits inferring directed functional interactions. Yet, the fMRI hemodynamic response is slow, and sampled at a timescale (seconds) several orders of magnitude slower than the underlying neural dynamics (milliseconds). It is, therefore, widely held that lag-based fMRI functional connectivity, measured with approaches like as Granger-Geweke causality (GC), provides spurious and unreliable estimates of underlying neural interactions. Experimental verification of this claim has proven challenging because neural ground truth connectivity is often unavailable concurrently with fMRI recordings. We address this challenge by combining machine learning with GC functional connectivity estimation. We estimated instantaneous and lag-based GC functional connectivity networks using fMRI data from 1000 participants, drawn from the Human Connectome Project database. A linear classifier, trained on either instantaneous or lag-based GC, reliably discriminated among seven different task and resting brain states, with over 80% cross-validation accuracy. With network simulations, we demonstrate that instantaneous and lag-based GC exploited interactions at fast and slow timescales, respectively, to achieve robust classification. With human fMRI data, instantaneous and lag-based GC identified distinct, cognitive core networks. Finally, variations in GC connectivity explained inter-individual variations in a variety of cognitive scores. Our findings show that instantaneous and lag-based methods reveal complementary aspects of functional connectivity in the brain, and suggest that slow, directed functional interactions, estimated with fMRI, provide robust markers of behaviorally relevant cognitive states.\n\nAuthor SummaryFunctional MRI (fMRI) is a leading, non-invasive technique for mapping networks in the human brain. Yet, fMRI signals are noisy and sluggish, and fMRI scans are acquired at a timescale of seconds, considerably slower than the timescale of neural spiking (milliseconds). Can fMRI, then, be used to infer dynamic processes in the brain such as the direction of information flow among brain networks? We sought to answer this question by applying machine learning to fMRI scans acquired from 1000 participants in the Human Connectome Project (HCP) database. We show that directed brain networks, estimated with a technique known as Granger-Geweke Causality (GC), accurately predicts individual subjects task-specific cognitive states inside the scanner, and also explains variations in a variety of behavioral scores across individuals. We propose that directed functional connectivity, as estimated with fMRI-GC, is relevant for understanding human cognitive function.

neuroscience↗