Biology subjects

Cruces, R. R.

Publications and source records attributed to Cruces, R. R..

6 recordsLinked to original sources

bioRxiv2026

HIPPOCAMPAL TAU RELATED SIGNAL INDEXES HYPEREXCITABILITY AND NREM SLEEP MEMORY DYSFUNCTION IN TEMPORAL LOBE EPILEPSY

Background Sleep and memory disturbances are common in temporal lobe epilepsy (TLE), yet their relationship with tau-targeted neuroimaging measures remains unclear. We investigated whether in-vivo temporal 18F-MK6240 tracer retention relates to seizure burden, non-rapid eye movement (NREM) sleep microstructure, and memory performance in TLE. Methods This cross-sectional study was conducted from 2019 to 2024 at the Montreal Neurological Institute-Hospital. Eligible patients had unilateral TLE diagnosed according to International League Against Epilepsy criteria and underwent overnight electroencephalography (EEG) recordings, research-protocol 3T magnetic resonance imaging, and 18F-MK6240 positron emission tomography. Regional 18F-MK6240 standardized uptake value ratios (SUVRs), using the cerebellum as the reference region, were quantified in the ipsilateral hippocampus (defined a priori as the primary region of interest), as well as in peri-hippocampal and lateral temporal regions. Electroclinical and neuropsychological measures, including seizure burden (high vs low), N2 interictal epileptiform discharge frequency (frequent vs non-frequent), and memory performance (impaired vs intact), were classified according to predefined clinical criteria. N2 spindle rate and N3 slow-wave rate were quantified from overnight EEG recordings. Findings Of 70 eligible patients, 25 were included (eight [32%] females; mean age 35.2 years [SD 12.1]). Higher ipsilateral hippocampal 18F-MK6240 uptake was observed in participants with greater focal seizure burden (median [IQR] 0.70 [0.67-0.78] vs 0.64 [0.58-0.65], rank-biserial correlation=-0.58, 95% CI -0.82 to -0.19; p=0.02), was associated with lower N2 spindle rate (r=-0.42, 95% CI -0.70 to -0.03; p=0.04) and higher N3 slow-wave rate (r=0.47; 95% CI 0.09 to 0.73; p=0.02), and was also higher in participants with impaired than intact memory performance (n=21; mean [SD] 0.80 [0.12] vs 0.68 [0.10], Cohen's d=1.15, 95% CI 0.07 to 2.21; p=0.04). Associations outside the hippocampus were generally less consistent and less precise, with the largest non-hippocampal estimate observed in peri-hippocampal regions. Interpretation Higher hippocampal 18F-MK6240 uptake was associated with greater focal seizure burden, altered NREM sleep microstructure, and impaired memory performance in TLE. These findings suggest that hippocampal 18F-MK6240 uptake may represent a candidate marker of circuit vulnerability associated with NREM sleep-memory dysfunction in TLE, warranting validation in larger longitudinal studies.

neuroscience↗

bioRxiv2026

MICAFlow: Fast and Robust MRI Preprocessing Bridging Research Neuroimaging and Clinical Practice

MICAFlow is a fully automated MRI preprocessing pipeline designed to translate advanced neuroimaging workflows from research into routine clinical practice. The pipeline emphasizes speed, robustness, and ease of use, focusing on structural and diffusion MRI. Key innovations include a Label-Augmented Modality-Agnostic Registration (LAMAReg) technique driven by deep learning segmentations for reliable cross-modal alignment, integration of state-of-the-art distortion corrections, and adherence to reproducible standards (Snakemake workflow, BIDSApp specifications). We describe the design of MICAFlow and evaluate its performance across heterogeneous datasets. First, accessibility: MICAFlow processes a multimodal MRI exam in minutes with clinically accessible hardware and without requiring GPU access, making it feasible for same-day clinical use. Second, registration accuracy: LAMAReg achieves cutting-edge multi-modal registration accuracy, yielding accurate alignment of diffusion MRI, FLAIR, and intra-subject T1-weighted images while remaining generally robust to common artifacts. Third, data reliability: Using identifiability, we show MICAFlow maintains consistent performance across diverse datasets, including subjects with pathology, and is closely comparable to contemporary pipelines. In sum, MICAFlows combination of machine learning and efficient workflows produces research-grade data quality with clinical-grade speed. This work demonstrates that advanced MRI preprocessing can be done fast and robustly, helping close the gap between research neuroimaging and broad clinical application of quantitative MRI techniques. The source code for MICAFlow is available here: https://github.com/MICA-MNI/micaflow, and for LAMAReg here: https://github.com/MICA-MNI/LAMAReg.

bioinformatics↗

bioRxiv2025

State dependent shifts in large scale functional topographies

Although functional networks can be consistently identified across cognitive states, they also undergo dynamic reconfigurations across different contexts. For example, naturalistic movie watching paradigms amplify activity in sensory systems compared to resting conditions. However, it remains unclear how these different states affect large-scale brain organization. The current study leveraged high-resolution in vivo 7T fMRI data from the Human Connectome Project (HCP) and the Precision NeuroImaging (PNI) datasets to examine large scale functional connectivity changes between resting and movie-watching conditions. To understand these changes within topographic and geometric principles of brain organization, connectivity shifts were stratified relative to macroscale cortical hierarchy and geodesic distance. Our results revealed that primary sensory areas showed increased local connectivity and reduced long-range interactions during movie watching relative to resting conditions, whereas the default mode network (DMN) exhibited an opposing pattern characterized by reduced within-network long-range connectivity and enhanced connectivity with distant regions outside the DMN. Together, these findings demonstrate that different cognitive states involve geometry-and hierarchy-informed reorganization of large-scale functional networks.

neuroscience↗

bioRxiv2025

In-vivo evidence for increased tau deposition in temporal lobe epilepsy

Temporal lobe epilepsy (TLE), the most common pharmaco-resistant epilepsy in adults, has been linked to structural brain changes extending beyond the mesiotemporal areas. While not traditionally viewed as a neurodegenerative disorder, recent ex-vivo studies have shown elevated levels of misfolded tau protein in TLE. This study investigated tau deposition in TLE patients using the in-vivo PET tracer [18F]MK-6240. We studied 28 TLE patients and 28 healthy controls to assess tau uptake and its relationship with brain connectivity, clinical variables, and cognitive function alongside post-surgical tissue from a subset of patients. Compared to controls, TLE patients exhibited markedly increased [18F]MK-6240 uptake in bilateral superior and medial temporal regions and the parietal cortex, with tau accumulation following regional functional and structural connectivity and cognitive impairment. Immunohistochemistry analysis confirmed variable phosphorylated tau staining in 5/6 operated cases with available specimens. These findings suggest that tau accumulation contributes to cognitive decline observed in TLE, supporting a potential role of tau in epilepsy-related neurodegeneration.

neuroscience↗

bioRxiv2024

Multimodal Precision Neuroimaging of the Individual Human Brain at Ultra-high fields

Multimodal neuroimaging, in particular magnetic resonance imaging (MRI), allows for non-invasive examination of human brain structure and function across multiple scales. Precision neuroimaging builds upon this foundation, enabling the mapping of brain structure, function, and connectivity patterns with high fidelity in single individuals. Highfield MRI, operating at magnetic field strengths of 7 Tesla (T) or higher, increases signal-to-noise ratio and opens up possibilities for gains spatial resolution. Here, we share a multimodal Precision Neuroimaging and Connectomics (PNI) 7T MRI dataset. Ten healthy individuals underwent a comprehensive MRI protocol, including T1 relaxometry, magnetization transfer imaging, T2*-weighted imaging, diffusion MRI, and multi-state functional MRI paradigms, aggregated across three imaging sessions. Alongside anonymized raw MRI data, we release cortex-wide connectomes from different modalities across multiple parcellation scales, and supply "gradients" that compactly characterize spatial patterning of cortical organization. Our precision MRI dataset will advance our understanding of structure-function relationships in the individual human brain and is publicly available via the Open Science Framework.

neuroscience↗

bioRxiv2020

Macroscale connectome manifold expansion in adolescence

AO_SCPLOWBSTRACTC_SCPLOWAdolescence is a critical time for the continued maturation of brain networks. Here, we assessed structural connectome development in a large longitudinal sample ranging from childhood to young adulthood. By projecting high-dimensional connectomes into compact manifold spaces, we identified a marked expansion of structural connectomes with the strongest effects in transmodal regions during adolescence. Findings reflected increased within-module connectivity together with increased segregation, indicating increasing differentiation of higher-order association networks from the rest of the brain. Projection of subcortico-cortical connectivity patterns into these manifolds showed parallel alterations in pathways centered on the caudate and thalamus. Connectome findings were contextualized via spatial transcriptome association analysis, highlighting genes enriched in cortex, thalamus, and striatum. Statistical learning of cortical and subcortical manifold features at baseline and their maturational change predicted measures of intelligence at follow-up. Our findings demonstrate that connectome manifold learning can bridge the conceptual and empirical gaps between macroscale network reconfigurations, microscale processes, and cognitive outcomes in adolescent development. IO_SCPLOWMPACTC_SCPLOWManifold learning of longitudinal brain network data provides novel insights into adolescent structural connectome maturation, and how multiple scales of cortical and subcortical organization interact in typical neurodevelopment.

neuroscience↗