Biology subjects

Bernstein, M. A.

Publications and source records attributed to Bernstein, M. A..

5 recordsLinked to original sources

bioRxiv2026

Network- and Measure-Specific Mid-Term Reliability of Multi-Echo Resting-State Functional Magnetic Resonance Imaging on a Compact 3 Tesla Scanner

1.IntroductionUnderstanding mid-term test-retest reliability and within-subject variability is important for interpreting changes observed in longitudinal and intervention studies. The reliability of resting-state functional magnetic resonance imaging (rs-fMRI) is known to vary across measures and brain regions. However, how reliability differs across functional networks and connectivity-and amplitude-based measures, and whether multi-echo acquisition and processing modify these patterns, remain incompletely characterized. MethodsTwenty-two healthy volunteers underwent two rs-fMRI sessions 15.7 {+/-} 4.0 days apart on a Compact 3T scanner. Multi-echo, middle-echo, and independently acquired single-echo datasets were compared, with multi-echo independent component analysis additionally evaluated as a denoising approach. Functional connectivity (FC) and three amplitude-based measures were evaluated using the Schaefer 400 parcellation. Reliability was systematically assessed using intraclass correlation coefficient (ICC), within-subject standard deviation (wSD), and systematic bias at edge or regional, and network levels. ResultsAcquisition-dependent differences in reliability were generally modest. Multi-echo acquisition and processing increased functional connectivity strength and the magnitude of amplitude-based measures and improved inferior cortical coverage, but these enhancements did not consistently translate into substantially higher ICC or lower wSD. In contrast, reliability showed clear network-dependent differences. FC reliability varied markedly across network pairs and was not explained by connectivity strength alone; pairs involving the default mode and control networks generally showed more favorable profiles than several somatomotor and visual network pairs. Fractional amplitude of low-frequency fluctuations (fALFF) also showed network-dependent reliability, with the most favorable regional reproducibility observed in the default mode and control networks and lower reproducibility in the somatomotor and visual networks. ConclusionThese findings provide practical mid-term reliability benchmarks for rs-fMRI on a Compact 3T scanner and show that measurement stability varies more clearly across measures and functional networks than across acquisition approaches. Key pointsO_LIMid-term test-retest reliability varied more clearly across resting-state measures and functional networks than across acquisition and processing approaches. C_LIO_LIMulti-echo acquisition and processing enhanced functional connectivity strength, amplitude-based signal magnitude, and inferior cortical coverage but did not consistently improve reliability. C_LIO_LIFunctional connectivity strength and fractional amplitude of low-frequency fluctuations showed distinct network-specific reliability profiles, with more favorable reproducibility in default mode and control networks than in several somatomotor and visual networks. C_LI

neuroscience↗

bioRxiv2026

Cerebral Oxygen Budgeting: Network-Level BOLD Dynamics During Acute Hypoxia

Hypoxia constrains cerebral oxygen availability and challenges brain function. Previous work showed that functional connectivity reorganizes early during acute hypoxia, preceding cognitive deterioration, but the functional changes accompanying more severe hypoxic stress remain incompletely understood. We examined dynamic amplitude of low-frequency fluctuations (dALFF) in blood-oxygenation-level dependent (BOLD) fMRI during normoxia, sustained mild hypoxia, and transient severe hypoxia in healthy adults performing a continuous Go/No-go task with concurrent physiological monitoring. We characterized dALFF at whole-brain and network levels using a causal sliding-window approach, principal component analysis, and Schaefer's 17-network parcellation. Severe hypoxia elicited a non-monotonic, phase-dependent dALFF response that was not observed during normoxia or sustained mild hypoxia. Relative preservation during early hypoxia was followed by late-hypoxia suppression, which we operationally defined as a decompensation phase, and by a pronounced rebound after reoxygenation. Within this global response, dALFF became increasingly differentiated across intrinsic brain networks: DefaultA showed marked suppression, whereas SomMotB exhibited relative preservation or enhancement during decompensation. These changes were neither spatially uniform nor tightly synchronized with systemic oxygenation, while broadly overlapping temporally with cognitive deterioration. Together, these findings indicate that dALFF captures a complementary aspect of the brain's response to acute hypoxic stress, characterized by reversible, phase- and network-dependent reorganization of ongoing low-frequency BOLD dynamics under constrained oxygen availability.

neuroscience↗

bioRxiv2025

Brain functional connectivity initiates structured reorganization at a critical oxygen threshold during hypoxia

The human brain dynamically adapts to hypoxia, a reduction in oxygen essential for metabolism. The brains adaptive response to hypoxia, however, remains unclear. We investigated dynamic functional connectivity (FC) in healthy adults under acute hypoxia (FiO2 = 7.7%, 11.8%) using BOLD fMRI, physiological monitoring (PetO2, PetCO2, SpO2), and a Go/No-Go task. Principal component analysis identified a hypoxia-responsive FC component involving 400 cerebral parcels. This component emerged with a critical drop in PetO2 ([~]53 mmHg), preceding changes in SpO2, BOLD signals, and behavior. These FC changes were network-specific and centered on the default mode network (DMN), which selectively synchronized with other high-level cognitive networks. In contrast, visual networks remained stable and segregated from the DMN. These results suggest that the brain proactively reorganizes its functional architecture in anticipation of oxygen decline, rather than in response to it. FC-based markers may offer early indicators of vulnerability in neurological or neurodegenerative conditions.

neuroscience↗

bioRxiv2022

Signatures of electrical stimulation driven network interactions in the human limbic system

Stimulation-evoked signals are starting to be used as biomarkers to indicate the state and health of brain networks. The human limbic network, often targeted for brain stimulation therapy, is involved in emotion and memory processing. Previous anatomical, neurophysiological and functional studies suggest distinct subsystems within the limbic network (Rolls, 2015). Previous studies using intracranial electrical stimulation, however, have emphasized the similarities of the evoked waveforms across the limbic network. We test whether these subsystems have distinct stimulation-driven signatures. In seven patients with drug-resistant epilepsy we stimulated the limbic system with single pulse electrical stimulation (SPES). Reliable cortico-cortical evoked potentials (CCEPs) were measured between hippocampus and the posterior cingulate cortex (PCC) and between the amygdala and the anterior cingulate cortex (ACC). However, the CCEP waveform in the PCC after hippocampal stimulation showed a unique and reliable morphology, which we term the limbic H-wave. This limbic H-wave was visually distinct and separately decoded from the amygdala to ACC waveform. Diffusion MRI data show that the measured endpoints in the PCC overlap with the endpoints of the parolfactory cingulum bundle rather than the parahippocampal cingulum, suggesting that the limbic H-wave may travel through fornix, mammillary bodies and the anterior nucleus of the thalamus (ANT). This was further confirmed by stimulating the ANT, which evoked the same limbic H-wave but with a shorter latency. Limbic subsystems have unique stimulation evoked signatures that may be used in the future to help develop stimulation therapies. Significance StatementThe limbic system is often compromised in diverse clinical conditions, such as epilepsy or Alzheimers disease, and it is important to characterize its typical circuit responses. Stimulation evoked waveforms have been used in the motor system to diagnose circuit pathology. We translate this framework to limbic subsystems using human intracranial stereo EEG (sEEG) recordings that measure deeper brain areas. Our sEEG recordings describe a stimulation evoked waveform characteristic to the memory and spatial subsystem of the limbic network that we term the limbic H-wave. The limbic H-wave follows anatomical white matter pathways from hippocampus to thalamus to the posterior cingulum and shows promise as a distinct biomarker of signaling in the human brain memory and spatial limbic network.

neuroscience↗

bioRxiv2018

Diffusion MRI Indices and their Relation to Cognitive Impairment in Brain Aging: The updated multi-protocol approach in ADNI3

Brain imaging with diffusion-weighted MRI (dMRI) is sensitive to microstructural white matter changes associated with brain aging and neurodegeneration. In its third phase, the Alzheimers Disease Neuroimaging Initiative (ADNI3) is collecting data across multiple sites and scanners using different dMRI acquisition protocols, to better understand disease effects. It is vital to understand when data can be pooled across scanners, and how the choice of dMRI protocol affects the sensitivity of extracted measures to differences in clinical impairment. Here, we analyzed ADNI3 data from 317 participants (mean age: 75.4{+/-}7.9 years; 143 men/174 women), who were each scanned at one of 47 sites with one of six dMRI protocols using scanners from three different manufacturers. We computed four standard diffusion tensor imaging (DTI) indices including fractional anisotropy (FADTI) and mean, radial, and axial diffusivity, and one FA index based on the tensor distribution function (FATDF), in 24 bilaterally averaged white matter regions of interest. We found that protocol differences significantly affected dMRI indices, in particular FADTI. We ranked the diffusion indices for their strength of association with four clinical assessments. In addition to diagnosis, we evaluated cognitive impairment as indexed by three commonly used screening tools for detecting dementia and Alzheimers disease: the Alzheimers Disease Assessment Scale (ADAS-cog), the Mini-Mental State Examination (MMSE), and the Clinical Dementia Rating scale sum-of-boxes (CDR-sob). Using a nested random-effects model to account for protocol and site, we found that across all dMRI indices and clinical measures, the hippocampal-cingulum and fornix (crus) / stria terminalis regions most consistently showed strong associations with clinical impairment. Overall, the greatest effect sizes were detected in the hippocampal-cingulum and uncinate fasciculus for associations between axial or mean diffusivity and CDR-sob. FATDF detected robust widespread associations with clinical measures, while FADTI was the weakest of the five indices for detecting associations. Ultimately, we were able to successfully pool dMRI data from multiple acquisition protocols from ADNI3 and detect consistent and robust associations with clinical impairment and age.

neuroscience↗