Biology subjects

Osorio, R. S.

Publications and source records attributed to Osorio, R. S..

6 recordsLinked to original sources

bioRxiv2026

Greater hypoxic burden predicts weaker gray matter-CSF coordination independent of non-hypoxic arousals: Implications for glymphatic activity

Study ObjectivesObstructive sleep apnea (OSA) is a risk factor for neurodegeneration, and glymphatic impairment may be one mechanistic pathway. Anti-phase coordination between brain pulsations and CSF flow, reflecting compensatory CSF displacement during each vascular pulsation cycle, supports glymphatic activity. This study examined whether hypoxic burden and sleep fragmentation, two distinct OSA pathologies, are differentially associated with brain pulsation-CSF flow dynamics. MethodsThis cross-sectional study included 28 individuals with newly identified OSA and 8 without OSA. Participants completed in-lab polysomnography or WatchPAT testing, providing measures of hypoxic burden, quantified as time below 90% oxygen saturation (T90), and non-hypoxic sleep fragmentation, quantified as respiratory effort-related arousals (RERAs). Participants also completed 7T resting-state functional MRI to estimate global BOLD-CSF, defined as anti-phase cross-correlation between blood-oxygen-level-dependent (BOLD) signal and CSF inflow. Associations were examined using correlation and hierarchical regression. Exploratory analyses examined region-specific BOLD-CSF and brain pulsation strength, quantified as BOLD amplitude. ResultsGreater T90 was associated with weaker global BOLD-CSF, independent of RERAs and covariates ({beta}=0.08,p=0.03). Greater T90 was also associated with higher BOLD amplitude across temporal, frontal, and parietal regions, but this elevation in amplitude was not accompanied by stronger region-specific BOLD-CSF coupling ({beta}=0.001,p>0.05). In contrast, among regions where BOLD amplitude was not associated with T90, greater BOLD amplitude predicted stronger region-specific BOLD-CSF ({beta}=-0.004,p<0.001). RERAs were not associated with global BOLD-CSF or BOLD amplitude. ConclusionsIn OSA, hypoxic burden may be the primary feature associated with impaired brain pulsation and CSF dynamics that support glymphatic activity. These alterations may be pronounced in the temporal lobe, where elevated pulsations were uncoupled from compensatory CSF displacement.

neuroscience↗

bioRxiv2025

Functional Connectome of Superagers Reveals Early Markers of Resilience and Vulnerability to Alzheimers Disease

As populations age, identifying the neurobiological basis of cognitive resilience is critical for delaying or preventing Alzheimers disease (AD). While most older adults experience memory decline, a subset known as superagers (SA) maintains youthful memory into late life, offering a unique window into protective mechanisms against neurodegeneration. Here, we identified a functional connectivity (FC) signature, termed Alzheimers-resilient connectome (ARC), that robustly differentiates SA from age-matched patients with AD. Using resting-state fMRI in a discovery cohort (N = 290), we identified ARC derived from machine learning classifiers that distinguished SA from AD with high accuracy (AUC = 0.85), and validated the replicability of the ARC in an independent replication cohort (N = 143). ARC involved prefrontal, temporal and insular networks and was strongly associated with brain age. When applied to cognitively unimpaired (CU) adults (discovery cohort: N = 818 and replication cohort: N = 497), ARC-based subtyping revealed SA-like and AD-like subgroups with similar baseline cognitive performance but markedly divergent longitudinal trajectories. SA-like CU individuals showed slower cognitive decline, reduced amyloid-{beta} accumulation, and lower risk of conversion to mild cognitive impairment and AD, reinforcing the ARC signature as a potential early indicator of resilience. Genome-wide association analysis identified CLYBL and FRMD6 as novel genetic modulators associated with these divergent aging phenotypes. Together, our findings position ARC as a sensitive and generalizable biomarker of resilience, enabling early risk stratification and precision prevention for AD.

neuroscience↗

bioRxiv2024

Elucidating the neuropathological and molecular heterogeneity of amyloid-beta and tau in Alzheimer's disease through machine learning and transcriptomic integration

Discerning functional brain network variations related to neuropathological aggregates in Alzheimers disease (AD), including amyloid-{beta} (A{beta}) and phosphorylated tau (p-tau), is crucial for understanding their link to cognitive decline and underlying molecular mechanisms. However, these variations are often confounded by normal aging-related changes, complicating interpretation. To address this challenge, we first defined Alzheimers continuum cases (A{beta} positive (A+), n = 129) and normal elderly (A{beta} negative (A-), n = 160) using cerebral spinal fluid amyloid levels, and then applied a novel deep learning approach to resting-state connectivity using functional magnetic resonance imaging (fMRI) of the 289 subjects to disentangle A+-specific dimensions in brain network alterations from those shared with A- individuals. The identified A+-specific dimensions were further refined to predict individual A{beta} and p-tau levels separately. We observed that resulting brain signatures, defined from A+-specific dimensions for predicting these two CSF biomarkers, were both attributed to the right superior temporal and anterior cingulate cortices and associated with attention and memory domains. When linking the brain signatures to gene expression data from a public transcriptomic atlas, we found that the brain signatures were associated with molecular pathways involving synaptic dysfunction and disruptions in pathways containing activity of excitatory neurons, astrocytes, and microglia. For A--shared dimensions, the A{beta}-linked brain signature involved the left fusiform and right middle cingulate cortices, correlating with the language cognitive measurement and language-related molecular pathways. The p-tau-linked signature predominantly involved the right insula and inferior temporal cortices, correlating with the aging-related molecular pathways. Collectively, our findings provided new insights in understanding of Alzheimers continuum pathological biomarkers.

neuroscience↗

bioRxiv2023

Defining Dementia Subtypes Through Neuropsychiatric Symptom-Linked Brain Connectivity Patterns

BACKGROUNDDementia is highly heterogeneous, with pronounced individual differences in neuropsychiatric symptoms (NPS) and neuroimaging findings. Understanding the heterogeneity of NPS and associated brain abnormalities is essential for effective management and treatment of dementia. METHODSUsing large-scale neuroimaging data from the Open Access Series of Imaging Studies (OASIS-3), we conducted a multivariate sparse canonical correlation analysis to identify functional connectivity-informed symptom dimensions. Subsequently, we performed a clustering analysis on the obtained latent connectivity profiles to reveal neurophysiological subtypes and examined differences in abnormal connectivity and phenotypic profiles between subtypes. RESULTSWe identified two reliable neuropsychiatric subsyndromes - behavioral and anxiety in the connectivity-NPS linked latent space. The behavioral subsyndrome was characterized by the connections predominantly involving the default mode and somatomotor networks and neuropsychiatric symptoms involving nighttime behavior disturbance, agitation, and apathy. The anxiety subsyndrome was mainly contributed by connections involving the visual network and the anxiety neuropsychiatric symptom. By clustering individuals along these two subsyndromes-linked connectivity latent features, we uncovered three subtypes encompassing both dementia patients and healthy controls. Dementia in one subtype exhibited similar brain connectivity and cognitive-behavior patterns to healthy individuals. However, dementia in the other two subtypes showed different dysfunctional connectivity profiles involving the default mode, frontoparietal control, somatomotor, and ventral attention networks, compared to healthy individuals. These dysfunctional connectivity patterns were associated with differences in baseline dementia severity and longitudinal progression of cognitive impairment and behavioral dysfunction. CONCLUSIONSOur findings shed valuable insights into disentangling the neuropsychiatric and brain functional heterogeneity of dementia, offering a promising avenue to improve clinical management and facilitate the development of timely and targeted interventions for dementia patients.

neuroscience↗

bioRxiv2019

Gray matter volume and estimated brain age gap are not associated with sleep-disordered breathing in subjects from the ADNI cohort

Alzheimers disease (AD) and sleep-disordered breathing (SDB) are prevalent conditions with rising burden. It is suggested that SDB may contribute to cognitive decline and advanced aging. Here, we assessed the link between self-reported SDB and gray matter volume in patients with AD, mild cognitive impairment (MCI) and healthy controls (HC). We further investigated whether SDB was associated with advanced brain aging. We included a total of 330 participants, divided based on self-reported history of SDB, and matched across diagnoses for age, sex and presence of the ApoE4 allele, from the Alzheimers Disease Neuroimaging Initiative. Gray-matter volume was measured using voxel-wise morphometry and differences reflecting SDB, cognitive status, and their interaction were evaluated. Further, using an age-prediction model fitted on gray-matter data of external datasets, we predicted study participants age from their structural scans. Cognitive decline (MCI/AD diagnosis) and advanced age were associated with lower gray matter volume in various regions, particularly in the bilateral temporal lobes. BrainAGE was well predicted from the morphological data in HC and, as expected, elevated in MCI and particularly in AD. However, there was neither a significant difference between regional gray matter volume in any diagnostic group related to the SDB status nor an SDB-by-cognitive status interaction. Also, we found neither a significant difference in BrainAGE gap (estimated - chronological age) related to SDB nor an SDB-by-cognitive status interaction. In summary, contrary to our expectations, we were not able to find a general nor a diagnostic specific effect on either gray-matter volumetric or brain aging.\n\nStatement of significanceDementia syndromes including Alzheimers disease (AD), are a major global concern, and unraveling modifiable predisposing risk factors is indispensable. Sleep-disordered breathing (SDB) and its most prevalent form, obstructive sleep apnea, are suggested as modifiable risk factors of AD, but their contribution to AD hallmarks, like brain atrophy and advanced brain aging, is not clear to this day. While self-reported SDB is suggested to propagate aging process and cognitive decline to AD in clinical studies, here, we demonstrated that, SDB might not necessarily associate to brain atrophy and the advanced brain aging assessed by morphological data, in AD progession. However, multimodal longitudinal studies with polysomnographic assessment of SDB are needed to confirm such fundings.

neuroscience↗

bioRxiv2017

Multichannel Sleep Spindle Detection Using Sparse Low-Rank Optimization

BackgroundWe propose a multichannel spindle detection method that detects global and local spindle activity across all channels of scalp EEG in a single run\n\nNew MethodUsing a non-linear signal model, which assumes the multichannel EEG to be a sum of a transient component and an oscillatory component, we propose a multichannel transient separation algorithm. Consecutive overlapping blocks of the multichannel oscillatory component are assumed to be low-rank whereas the transient component is assumed to be piecewise constant with a zero baseline. The estimated multichannel oscillatory component is used in conjunction with a bandpass filter and the Teager operator for detecting sleep spindles\n\nResults and comparison with other methodsSeveral examples are shown to illustrate the utility of the proposed method in detecting global and local spindle activity. The proposed method is applied to two publicly available databases and compared with 7 existing single-channel automated detectors. F1 scores for the proposed spindle detection method averaged 0.66 (0.02) and 0.62 (0.06) for the two databases, respectively. For an overnight 6 channel EEG signal, the proposed algorithm takes about 4 minutes to detect sleep spindles simultaneously across all channels with a single setting of corresponding algorithmic parameters\n\nConclusionsThe proposed method aims to mimic and utilize, for better spindle detection, a particular human expert behavior where the decision to mark a spindle event may be subconsciously influenced by the presence of a spindle in EEG channels other than the central channel visible on a digital screen

neuroscience↗