Biology subjects

Pusuluri, K.

Publications and source records attributed to Pusuluri, K..

4 recordsLinked to original sources

bioRxiv2026

Dynamic Estimation of Spatially Interactive Networks (DESINE) Reveals Constrained Brain Repertoire in Schizophrenia Linked to Clinical and Cognitive Symptoms

BackgroundWhile resting-state fMRI demonstrated that brain networks are spatially dynamic (expanding, shrinking, and changing complexity over time), understanding the transient spatial network interactions that remain poorly characterized is critical for revealing the mechanisms underlying brain disorders. MethodsWe introduce DESINE (Dynamic Estimation of Spatially Interactive Networks), a novel framework using joint density distributions (2D histograms) of voxel-wise activity to quantify 4D spatial network interactions across sliding windows. We analysed transient deviations from the average functional state using root-mean-square error (RMSE) and mean absolute deviation (MAD), and characterized recurring interaction patterns using k-means clustering. We applied DESINE to 91 network pairs (14 networks) in a cohort of 508 subjects (315 healthy controls; 193 patients with schizophrenia, SZ). ResultsSZ is characterized by a significantly "constrained dynamic repertoire" of network interactions. SZ patients showed markedly lower means and standard deviations for both RMSE and MAD metrics across network pairs, particularly in regions of high activity, indicating systematic rigidity. Cluster analysis revealed significant alterations in state affinity metrics, suggesting a global breakdown in the brains capacity to preserve diverse, high-fidelity spatial configurations. Critically, these interaction metrics were associated with cognitive performance, symptom scores on the positive and negative syndrome scale, and chlorpromazine equivalent drug scores. ConclusionsThis work introduces DESINE as a global, voxel-agnostic framework for characterizing time-varying spatial interactions. Our findings highlight spatial rigidity as a fundamental feature of psychopathology, suggesting that the inability to express a diverse range of spatial interactions is a factor underlying cognitive deficits in schizophrenia.

neuroscience↗

bioRxiv2025

Diminished spatial dynamics and maladaptive spatial complexity link resting brain network disruption to cognition in schizophrenia

Resting-state fMRI studies increasingly emphasize the dynamic nature of brain networks. While most approaches examine temporal fluctuations in connectivity, we focus on the spatial dynamics and complexity at voxel level - how networks expand and contract, and change their structural complexity over time. Using dynamic independent component analysis (ICA), we investigate the hierarchical structure of the resulting time-varying spatial networks, from their broad periphery to their most active core. We combine this with fractal dimension (FrD) as a measure of a networks spatial complexity and analyze temporal changes (dynamic flexibility) in a network and synchronized fluctuations between network pairs (fractal dimension coupling, FrDC). We refer to this approach as "dynamic spatial network complexity and connectivity (dSNCC)". Using a combined cohort of 508 subjects (315 healthy controls, 193 schizophrenia patients), we found that schizophrenia is associated with higher mean FrD in several networks, suggesting more irregular patterns/boundaries and a disorganized network structure. Critically, patients showed significantly reduced dynamic flexibility, indicating their networks are "stuck" in a less adaptable state. This robust finding is evidenced by a synergistic loss of temporal standard deviations in both network volume and FrD across multiple networks and activity thresholds. This maladaptive complexity was associated with cognitive impairment, with several dSNCC measures showing significant associations with subject scores for processing speed, visual learning, and verbal learning. Higher complexity in these networks and more significantly, their reduced dynamic flexibility as seen in patients, were particularly associated with impaired performance. Furthermore, we found aberrant connectivity (FrDC) in schizophrenia, with certain network pairs exhibiting overly synchronized complexity changes. Our results demonstrate that dSNCC is a powerful tool for characterizing network dynamics and may potentially provide a measurable mechanism for maladaptation in schizophrenia, where the brains inability to fluidly change its complexity may contribute to cognitive deficits and symptoms like disorganized thought. These findings highlight the importance of studying the intrinsic spatial dynamic properties to reveal the fundamental principles of brain network organization in health and disease. Our work represents a significant leap in complex systems neuroscience and provides a novel, quantifiable biomarker framework highly relevant for understanding and targeting other complex disorders characterized by network dysfunction, such as Alzheimers disease, autism, or other mental health conditions.

neuroscience↗

bioRxiv2025

Plasticity in thoracic paravertebral sympathetic postganglionic neurons after high spinal cord transection

Various pre-sympathetic descending brain circuits recruit spinal cord preganglionic neurons to encode central sympathetic drive via their synaptic actions onto sympathetic postganglionic neurons (SPNs) - the final sympathetic output neurons. Thoracic paravertebral ganglia SPNs (tSPNs) provide distributed control over body tissue systems via functional subpopulations. High thoracic spinal cord injuries (SCIs) compromise descending excitatory drive to SPNs, causing dysautonomias including hypotension. In adult mice, we tested whether the SCI-induced chronic reduction in tSPN activity leads to homeostatic increases in their excitability. tSPN excitability spanned a >10 fold range in both sham and SCI populations, governed by a strong linear (ohmic) relationship between cell resistance and threshold depolarizing current (rheobase), with a clear trend towards increased excitability after SCI. Dendritic length was reduced, as was measured cell capacitance in Neuropeptide Y expressing (NPY+) tSPNs (putative vasoconstrictors), which represent >40% of tSPNs. NPY+ tSPNs also had changes in active membrane properties including an increased repetitive firing output gain ({uparrow}f-I slope), which modelling attributed to reduced delayed rectifier currents (IK). After SCI, spontaneous quantal excitatory synaptic frequency increased overall (226%) and in the NPY+ tSPN subpopulation (300%); their temporal summation recruited spiking in 10.5% of sham and 22.2% of SCI recordings. Computational modeling showed that spontaneous synaptic activity was particularly effective at recruiting spiking after SCI. Overall, tSPNs, including in vasoconstrictors, appear to undergo CNS-independent compensatory increases in excitability after SCI. The alterations further contribute to observed central and peripheral changes that limit hypoactivity and hypotension but exaggerate reflex responses. Significance StatementRecruited by preganglionic neurons located in thoracolumbar spinal cord, sympathetic postganglionic neurons (SPNs) represent the final step in sympathetic neural homeostatic control of target organs. The dramatic reductions or complete loss of brain pre-sympathetic drive in higher level spinal cord injuries (SCIs) contribute to emergent dysautonomias. Given the crucial role of SPNs in maintaining organismal homeostasis, we undertook comprehensive studies to test whether thoracic SPNs undergo homeostatic compensatory increases in their cellular excitability weeks after a high thoracic SCI. We observed changes consistent with increased cellular and synaptic excitability including in SPN vasoconstrictors, whose increased output gain, could mitigate hypotension but also strengthen hypertensive responses generated by afferent-driven exaggerated preganglionic drive (e.g. autonomic dysreflexia).

neuroscience↗

bioRxiv2023

4D DYNAMIC SPATIAL BRAIN NETWORKS AT REST LINKED TO COGNITION SHOW ATYPICAL VARIABILITY AND COUPLING IN SCHIZOPHRENIA

Despite increasing interest in the dynamics of functional brain networks, most studies focus on the changing relationships over time between spatially static networks or regions. Here we propose an approach to study dynamic spatial brain net-works in human resting state functional magnetic resonance imaging (rsfMRI) data and evaluate the temporal changes in the volumes of these 4D networks. Our results show significant volumetric coupling (i.e., synchronized shrinkage and growth) between networks during the scan. We find that several features of such dynamic spatial brain networks are associated with cognition, with higher dynamic variability in these networks and higher volumetric coupling between network pairs positively associated with cognitive performance. We show that these networks are modulated differently in individuals with schizophrenia versus typical controls, resulting in network growth or shrinkage, as well as altered focus of activity within a network. Schizophrenia also shows lower spatial dynamical variability in several networks, and lower volumetric coupling between pairs of networks, thus upholding the role of dynamic spatial brain networks in cognitive impairment seen in schizophrenia. Our data show evidence for the importance of studying the typically overlooked voxelwise changes within and between brain networks.

neuroscience↗