Biology subjects

Olchanyi, M.

Publications and source records attributed to Olchanyi, M..

3 recordsLinked to original sources

bioRxiv2026

On the accuracy of image registration in portable low-field 3D brain MRI

Portable low-field MRI offers an affordable and mobile alternative to conventional high-field scanners, enabling imaging in point-of-care and resource-limited settings. However, its lower signal-to-noise ratio, reduced resolution, and acquisition artifacts raise concerns about the accuracy of standard image registration methods. Reliable registration is critical for a wide range of emerging applications, including frequent brain monitoring, assessment of neurodegenerative disease progression, and evaluation of treatment effects such as those of Alzheimers therapeutics. In this work, we systematically evaluated state-of-the-art registration approaches on simulated low-field scans (obtained by downsampling high-field images) and on real low-field brain MRI data. We compared three representative approaches: classical optimization (NiftyReg), learning-based registration (SynthMorph), and synthesis-based registration (SynthSR+NiftyReg). Using downsampled high-field scans, all methods performed well, achieving high Dice scores and smooth deformation fields, indicating that reduced resolution alone does not hinder registration. In contrast, real low-field data exhibited lower accuracy, primarily due to geometric distortion and other acquisition-specific artifacts. Among the tested approaches, the synthesis-based pipeline achieved the most robust performance across subjects and modalities. Overall, existing algorithms can accommodate resolution limitations, however, future methods could further enhance coregistration by explicitly addressing the distortions present in low-field MRI scans.

neuroscience↗

bioRxiv2025

Functional fractionation of large-scale brain networks in the human subcortex

Brain network mapping plays a crucial role in advancing our understanding of human brain organization and the neuroanatomic foundations of cognition. Historically, the identification of large-scale brain networks has focused on the cerebral cortex. Some cortical networks have been fractionated into subnetworks, yielding valuable insights into their domain-specific cognitive functions. In contrast, functional mapping of large-scale brain networks within subcortical regions remains an emerging and challenging field, hindered by a low signal-to-noise ratio in subcortical functional MRI data and an inability to distinguish networks with substantial spatiotemporal overlap. In this study, we fractionated and identified fifteen spatially overlapped and temporally correlated subnetworks, which can be categorized into four large-scale brain networks. The subcortical functional connectivity patterns of these subnetworks exhibited distinct, yet overlapping, spatial configurations, with widely connected hub nodes identified in the caudate, putamen, hippocampus, and thalamus. These subnetworks are highly reproducible across healthy human brains and provide normative functional atlases, which we release here as a resource for the academic community. As a proof-of-principle demonstration of how the atlases can be used to elucidate the pathophysiology of neuropsychiatric disorders, we show that the spatial patterns of the subnetworks predict the level of consciousness in patients with severe traumatic brain injury. These observations indicate a highly conserved and spatially overlapped subcortical functional architecture in the human brain, providing opportunities to elucidate pathophysiologic mechanisms and develop new neuromodulatory therapies for a broad spectrum of neuropsychiatric diseases.

neuroscience↗

bioRxiv2023

Sustaining wakefulness: Brainstem connectivity in human consciousness

Consciousness is comprised of arousal (i.e., wakefulness) and awareness. Substantial progress has been made in mapping the cortical networks that modulate awareness in the human brain, but knowledge about the subcortical networks that sustain arousal is lacking. We integrated data from ex vivo diffusion MRI, immunohistochemistry, and in vivo 7 Tesla functional MRI to map the connectivity of a subcortical arousal network that we postulate sustains wakefulness in the resting, conscious human brain, analogous to the cortical default mode network (DMN) that is believed to sustain self-awareness. We identified nodes of the proposed default ascending arousal network (dAAN) in the brainstem, hypothalamus, thalamus, and basal forebrain by correlating ex vivo diffusion MRI with immunohistochemistry in three human brain specimens from neurologically normal individuals scanned at 600-750 {micro}m resolution. We performed deterministic and probabilistic tractography analyses of the diffusion MRI data to map dAAN intra-network connections and dAAN-DMN internetwork connections. Using a newly developed network-based autopsy of the human brain that integrates ex vivo MRI and histopathology, we identified projection, association, and commissural pathways linking dAAN nodes with one another and with cortical DMN nodes, providing a structural architecture for the integration of arousal and awareness in human consciousness. We release the ex vivo diffusion MRI data, corresponding immunohistochemistry data, network-based autopsy methods, and a new brainstem dAAN atlas to support efforts to map the connectivity of human consciousness. One sentence summaryWe performed ex vivo diffusion MRI, immunohistochemistry, and in vivo 7 Tesla functional MRI to map brainstem connections that sustain wakefulness in human consciousness.

neuroscience↗