Biology subjects

Zeineh, M. M.

Publications and source records attributed to Zeineh, M. M..

7 recordsLinked to original sources

bioRxiv2026

Diffusion MRI Tractography Predicts Electrophysiological Connectivity and Explains Spectral Signatures of Evoked Potentials in the Human Brain

White matter fiber bundles are the structural conduits of information flow in the human brain and thereby mediate the spatial trajectories and timings of the electrophysiological signaling. Here, we combined diffusion magnetic resonance imaging (dMRI) and stereoelectroencephalography (SEEG) recordings in neurosurgical patients to develop an integrated framework for predicting and interpreting causal electrophysiological connectivity patterns between pairs of brain regions. We used repeated single-pulse electrical stimulation in 40 participants implanted with a total of 5794 intracranial electrodes throughout the human brain, encompassing both cortical and multiple thalamic nuclei. A nonlinear, time-frequency manifold learning approach was used to define electrophysiological connectivity, which was then compared with subject-specific and atlas-based structural connectivity. Across 150,000 electrode pairs, we found that the presence of a structural connection predicted causal electrophysiological connectivity with a probability of ~0.95; and its absence predicted the lack of the direct electrophysiological connectivity with a probability of ~0.8. We also show evidence of indirect/polysynaptic pathways supported by both modalities and reported neural features from time-frequency decomposition that distinguished between direct and indirect signaling. We demonstrated that an early phase-locked broadband component (10-70 ms) marked direct structural pathways, whereas delayed and slower components reflected indirect propagation. Notably, we reported that thalamic involvement within an indirect pathway results in increased latency (> 200 ms) and enhanced late oscillatory behavior, despite increased conduction velocity measures along thalamo-cortical pathways. Therefore, our multimodal framework maps human brain connectivity, bridging structural architecture, causal electrophysiological dynamics, and network-level communication.

neuroscience↗

bioRxiv2026

Plasma proteomics reveals divergent sex-specific senescence and bone biology signatures across neurodegenerative diseases

Neurodegenerative diseases are often accompanied by systemic comorbidities, including changes in bone health, but the molecular relationship between neurodegeneration, skeletal decline, and cellular senescence remains poorly understood. In this study, we investigated sex-specific changes in circulating bone- and senescence-related proteins across the spectrum of Alzheimers disease(AD), Lewbody dementia(LB) and Parkinsons disease(PD). Plasma proteomic profiling was performed on samples from 408 participants deeply phenotyped for neurodegenerative diseases, followed by differential protein and pathway analyses. This study reveals sex-dependent alterations in bone and senescence-related circulating proteins in AD-related, PD and LB-related neurodegenerative diseases, providing insights into the complex relationship between neurodegeneration and bone health. Several candidate proteins were also associated with established plasma neurodegeneration biomarkers, particularly pTau181. Pathway analyses revealed shared mitochondrial and metabolic dysfunction across neurodegenerative diseases, with disease-specific features including vesicle trafficking disruption in AD and inflammatory-senescence pathways in LB, plus sex-divergent patterns in inflammatory signaling and bone-related pathways.

neuroscience↗

bioRxiv2025

Denoising 7T Structural MRI with Conditional Generative Diffusion Models

Purpose7T MRI offers ultra-high resolution and improved sensitivity for iron deposition in neurodegenerative disorders, but commonly used acquisitions are long and hence challenging, especially for elderly subjects. Efficiently denoising a short acquisition to achieve the image quality of a longer acquisition would be of translational benefit. Materials and MethodsWe introduce a conditional diffusion model derived from generative AI (a 7T Conditional Diffusion Model, 7TCDM) that was trained on native single-acquisition 2D reconstructions and referenced multi-repetition images to guide the denoising process and improve SNR and contrast. 7TCDM model was tested on 2D T2-weighted gradient-echo imaging from 19 participants, including healthy controls and individuals with mild cognitive impairment or Alzheimers disease (AD). 7TCDMs performance was assessed using Mean Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), and comprehensive reader studies. ResultsReferencing the multi-repetition ground truth, 7TCDM improved the single-acquisition original image by 29.1% in MSE, 5.8% in PSNR, and 9.4% in SSIM, and outperformed convolutional neural network-based models in all metrics. Expert rater evaluations confirmed superior image quality, with significantly enhanced detail and contrast preservation in regions such as the hippocampi, white matter lesions, and small cortical veins. The model also demonstrated robust performance in both the concurrently acquired and publicly available 3D multi-echo gradient echo acquisitions, which the model was not trained on. ConclusionsThe 7T Conditional Diffusion Model provides high-quality denoised images from shorter scans, increasing the feasibility of scanning patients in shorter times while preserving essential anatomical and pathological details.

neuroscience↗

bioRxiv2024

Uncovering microstructural architecture from histology

Mapping the brains fiber network is crucial for understanding its function and malfunction, but resolving nerve trajectories over large fields of view is challenging. Here, we show that computational scattered light imaging (ComSLI) can map fiber networks in histology independent of sample preparation, also in formalin-fixed paraffin-embedded (FFPE) tissues including whole human brain sections. We showcase this method in new and archived, animal and human brain sections, for different sample preparations (in paraffin, deparaffinized, various stains, unstained fresh-frozen). We convert microscopic orientations to microstructure-informed fiber orientation distributions (FODs). Adapting tractography tools from diffusion magnetic resonance imaging (dMRI), we trace axonal trajectories revealing white and gray matter connectivity. These allow us to identify altered microstructure or deficient tracts in demyelinating or neurodegenerating pathology, and to show key advantages over dMRI, polarization microscopy, and structure tensor analysis. Finally, we map fibers in non-brain tissues, including muscle, bone, and blood vessels, unveiling the tissues function. Our cost-effective, versatile approach enables micron-resolution studies of intricate fiber networks across tissues, species, diseases, and sample preparations, offering new dimensions to neuroscientific and biomedical research.

pathology↗

bioRxiv2022

Using light and X-ray scattering to untangle complex neuronal orientations and validate diffusion MRI

Disentangling human brain connectivity requires an accurate description of neuronal trajectories. However, a detailed mapping of axonal orientations is challenging because axons can cross one another on a micrometer scale. Diffusion magnetic resonance imaging (dMRI) can be used to infer neuronal connectivity because it is sensitive to axonal alignment, but it has limited resolution and specificity. Scattered Light Imaging (SLI) and small-angle X-ray scattering (SAXS) reveal neuronal orientations with microscopic resolution and high specificity, respectively. Here, we combine both techniques to achieve a cross-validated framework for imaging neuronal orientations, with comparison to dMRI. We evaluate brain regions that include unidirectional and crossing fiber tracts in human and vervet monkey brains. We find that SLI, SAXS, and dMRI all agree regarding major fiber pathways. SLI and SAXS further quantitatively agree regarding fiber crossings, while dMRI overestimates the amount of crossing fibers. In SLI, we find a reduction of peak distance with increasing out-of-plane fiber angles, confirming theoretical predictions, validated against both SAXS and dMRI. The combination of scattered light and X-ray imaging can provide quantitative micrometer 3D fiber orientations with high resolution and specificity, enabling detailed investigations of complex tract architecture in the animal and human brain.

neuroscience↗

bioRxiv2022

Imaging crossing fibers in mouse, pig, monkey, and human brain using small-angle X-ray scattering

Myelinated axons (nerve fibers) efficiently transmit signals throughout the brain via action potentials. Multiple methods that are sensitive to axon orientations, from microscopy to magnetic resonance imaging, aim to reconstruct the brains structural connectome. As billions of nerve fibers traverse the brain with various possible geometries at each point, resolving fiber crossings is necessary to generate accurate structural connectivity maps. However, doing so with specificity is a challenging task because signals originating from oriented fibers can be influenced by brain (micro)structures unrelated to myelinated axons. X-ray scattering can specifically probe myelinated axons due to the periodicity of the myelin sheath, which yields distinct peaks in the scattering pattern. Here, we show that small-angle X-ray scattering (SAXS) can be used to detect myelinated, axon-specific fiber crossings. We first demonstrate the capability using strips of human corpus callosum to create artificial double- and triple-crossing fiber geometries, and we then apply the method in mouse, pig, vervet monkey, and human brains. Given its specificity, capability of 3-dimensional sampling and high resolution, SAXS can serve as a ground truth for validating MRI as well as microscopy-based methods. Statement of SignificanceTo study how the nerve fibers in our brain are interconnected, scientists need to visualize their trajectories, which often cross one another. Here, we show the unique capacity of small-angle X-ray scattering (SAXS) to study these fiber crossings without use of labelling, taking advantage of SAXSs specificity to myelin - the insulating sheath that is wrapped around nerve fibers. We use SAXS to detect double and triple crossing fibers and unveil intricate crossings in mouse, pig, vervet monkey, and human brains. This non-destructive method can uncover complex fiber trajectories and validate other less specific imaging methods (e.g., MRI or microscopy), towards accurate mapping of neuronal connectivity in the animal and human brain. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=88 SRC="FIGDIR/small/510198v2_ufig1.gif" ALT="Figure 1"> View larger version (62K): org.highwire.dtl.DTLVardef@479368org.highwire.dtl.DTLVardef@11eb0c9org.highwire.dtl.DTLVardef@1e883adorg.highwire.dtl.DTLVardef@1ce024_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗

bioRxiv2021

Towards a comprehensive delineation of white matter tract-related deformation

Finite element (FE) models of the human head are valuable instruments to explore the mechanobiological pathway from external loading, localized brain response, and resultant injury risks. The injury predictability of these models depends on the use of effective criteria as injury predictors. The FE-derived normal deformation along white matter (WM) fiber tracts (i.e., tract-oriented strain) has recently been suggested as an appropriate predictor for axonal injury. However, the tract-oriented strain only represents a partial depiction of the WM fiber tract deformation. A comprehensive delineation of tract-related deformation may improve the injury predictability of the FE head model by delivering new tract-related criteria as injury predictors. Thus, the present study performed a theoretical strain analysis to comprehensively characterize the WM fiber tract deformation by relating the strain tensor of the WM element to its embedded fiber tract. Three new tract-related strains with exact analytical solutions were proposed, measuring the normal deformation perpendicular to the fiber tracts (i.e., tract-perpendicular strain), and shear deformation along and perpendicular to the fiber tracts (i.e., axial-shear strain and lateral-shear strain, respectively). The injury predictability of these three newly-proposed strain peaks along with the previously-used tract-oriented strain peak and maximum principal strain (MPS) were evaluated by simulating 151 impacts with known outcome (concussion or non-concussion). The results preliminarily showed that four tract-related strain peaks exhibited superior performance than MPS in discriminating concussion and non-concussion cases. This study presents a comprehensive quantification of WM tract-related deformation and advocates the use of orientation-dependent strains as criteria for injury prediction, which may ultimately contribute to an advanced mechanobiological understanding and enhanced computational predictability of brain injury.

bioengineering↗