Biology subjects

Mahmood, H.

Publications and source records attributed to Mahmood, H..

5 recordsLinked to original sources

bioRxiv2024

Lactate potentiates NMDA receptor currents via an intracellular redox mechanism targeting cysteines in the C-terminal domain of GluN2B subunits: implications for synaptic plasticity

SummaryThrough the Astrocyte Neuron Lactate Shuttle, astrocyte-derived lactate fuels the high-energy demands of neurons and acts as a signaling molecule, promoting synaptic plasticity and memory consolidation. Lactate regulates neuronal excitability and modulates the expression of genes related to synaptic plasticity and neuroprotection, but the molecular mode for these signaling actions is uncertain. Using patch-clamp recordings in cultured cortical neurons, we found that lactate enhances both the amplitude and the inactivation time constant of NMDA receptor currents (INMDAR) evoked by brief applications of glutamate and glycine. Not reproduced by HCAR1 agonists, this modulation depends on monocarboxylate transporters and lactate dehydrogenase, indicating the requirement for lactate entry and metabolic conversion into pyruvate and NADH formation within neurons. Disruption of intracellular calcium dynamics or inhibition of Ca2+/calmodulin-dependent protein kinase II (CaMKII), a NMDAR-associated kinase linking Ca2+ signal to long-term potentiation (LTP), significantly diminishes the effects of lactate on INMDAR. We identified two redox-sensitive cysteine- containing sequences in the intrinsically disordered intracellular C-terminal domain of the GluN2B subunit that play a role in the potentiation of NMDAR by lactate. In a compelling set of experiments using HEK cells, we observed that the presence of functional CaMKII and GluN2B-containing NMDARs is necessary for the lactate-enhancing effects. Mutations in GluN2B that prevent CaMKII binding or redox regulation via cysteines abrogate the modulatory action of lactate. Immunoprecipitation experiments in neurons attest that lactate increases the association between CaMKII and GluN2B. This interaction is crucial for the potentiation of INMDAR amplitude by lactate. Proximity ligation assays between GluN2B and the postsynaptic density marker PSD-95 revealed that lactate induced an accumulation of GluN2B in dendritic spines, an effect that was prevented by a CaMKII peptide inhibitor. These results highlight a mechanistic pathway whereby lactate boosts NMDAR function through intracellular metabolic conversion and redox-sensitive interactions requiring CaMKII, establishing a link between astrocyte metabolism and synaptic modulation in neurons.

neuroscience↗

bioRxiv2024

Biologically Realistic Computational Primitives of Neocortex Implemented on Neuromorphic Hardware Improve Vision Transformer Performance

Understanding the computational principles of the brain and replicating them on neuromorphic hardware and modern deep learning architectures is crucial for advancing neuro-inspired AI (NeuroAI). Here, we develop an experimentally-constrained biophysical network model of neocortical circuit motifs, focusing on layers 2-3 of the primary visual cortex (V1). We investigate the role of four major cortical interneuron classes in a competitive-cooperative computational primitive and validate these circuit motifs implemented soft winner-take-all (sWTA) computation for gain modulation, signal restoration, and context-dependent multistability. Using a novel parameter mapping technique, we configured IBMs TrueNorth (TN) chip to implement sWTA computations, mirroring biological neural dynamics. Retrospectively, we observed a strong correspondence between the biophysical model and the TN hardware parameters, particularly in the roles of four key inhibitory neuron classes: Parvalbumin (feedforward inhibition), Somatostatin (feedback inhibition), VIP (disinhibition), and LAMP5 (gain normalization). Moreover, the sparse coupling of this sWTA motif was also able to simulate a two-state neural state machine on the TN chip, replicating working memory dynamics essential for cognitive tasks. Additionally, integrating the sWTA computation as a preprocessing layer in the Vision Transformer (ViT) enhanced its performance on the MNIST digit classification task, demonstrating improved generalization to previously unseen data and suggesting a mechanism akin to zero-shot learning. Our approach provides a framework for translating brain-inspired computations to neuromorphic hardware, with potential applications on platforms like Intels Loihi2 and IBMs Northpole. By integrating biophysically accurate models with neuromorphic hardware and advanced machine learning techniques, we offer a comprehensive roadmap for embedding neural computation into NeuroAI systems.

neuroscience↗

bioRxiv2024

NeuroAtlas: An Artificial Intelligence-based Framework for Annotation, Segmentation and Registration of Large Scale Biomedical Imaging Data

With increasing neuroimaging modalities and data diversity, mapping brain regions to a standard atlas template has become a challenging problem. Machine learning in general and deep learning, in particular, have been providing robust solutions for several neuroimaging tasks, including brain image registration and segmentation. However, these methods require a large amount of data for groundtruth labels, annotated by human experts, which is time-consuming. In this work, we introduce NeuroAtlas, an AI-based framework for atlas generation and brain region segmentation. We showcase an end-to-end solution for brain registration and segmentation by providing i) a deep learning modeling suite with a variety of high-performing model architectures to map a brain atlas onto the input brain section and ii) a Graphical User Interface (GUI)-based plugin for large-scale data annotation with a feature of modifying the predicted labels for active learning. We demonstrate a robust performance of our framework on the human brains, captured through various imaging modalities and age groups, and demonstrate its application for mouse brains as well. NeuroAtlas tool will be open-sourced and entirely compatible with both local as well as cloud-based computing so that users can easily adapt to their neuroimaging custom datasets.

neuroscience↗

bioRxiv2024

Multimodal 3D Image Registration for Mapping Brain Disorders

We introduce an AI-driven approach for robust 3D brain image registration, addressing challenges posed by diverse hardware scanners and imaging sites. Our model trained using an SSIM-driven loss function, prioritizes structural coherence over voxel-wise intensity matching, making it uniquely robust to inter-scanner and intra-modality variations. This innovative end-to-end framework combines global alignment and non-rigid registration modules, specifically designed to handle structural, intensity, and domain variances in 3D brain imaging data. Our approach outperforms the baseline model in handling these shifts, achieving results that align closely with clinical ground-truth measurements. We demonstrate its efficacy on 3D brain data from healthy individuals and dementia patients, with particular success in quantifying brain atrophy, a key biomarker for Alzheimers disease and other brain disorders. By effectively managing variability in multisite, multi-scanner neuroimaging studies, our approach enhances the precision of atrophy measurements for clinical trials and longitudinal studies. This advancement promises to improve diagnostic and prognostic capabilities for neurodegenerative disorders.

neuroscience↗

bioRxiv2016

Mapping Autosomal Recessive Intellectual Disability: Combined Microarray and Exome Sequencing Identifies 26 Novel Candidate Genes in 192 Consanguineous Families

Approximately 1% of the global population is affected by intellectual disability (ID), and the majority receive no molecular diagnosis. Previous studies have indicated high levels of genetic heterogeneity, with estimates of more than 2500 autosomal ID genes, the majority of which are autosomal recessive (AR). Here, we combined microarray genotyping, homozygosity-by-descent (HBD) mapping, copy number variation (CNV) analysis, and whole exome sequencing (WES) to identify disease genes/mutations in 192 multiplex Pakistani and Iranian consanguineous families with non-syndromic ID. We identified definite or candidate mutations (or CNVs) in 51% of families in 72 different genes, including 26 not previously reported for ARID. The new ARID genes include nine with loss-of-function mutations (ABI2, MAPK8, MPDZ, PIDD1, SLAIN1, TBC1D23, TRAPPC6B, UBA7, and USP44), and missense mutations include the first reports of variants in BDNF or TET1 associated with ID. The genes identified also showed overlap with de novo gene sets for other neuropsychiatric disorders. Transcriptional studies showed prominent expression in the prenatal brain. The high yield of AR mutations for ID indicated that this approach has excellent clinical potential and should inform clinical diagnostics, including clinical whole exome and genome sequencing, for populations in which consanguinity is common. As with other AR disorders, the relevance will also apply to outbred populations.

genetics↗