Most recent paper

Eur J Neurosci. 2025 Oct;62(7):e70271. doi: 10.1111/ejn.70271.

ABSTRACT

Depressive symptoms are commonly observed in stress-related psychiatric disorders, such as major depressive disorder (MDD) and posttraumatic stress disorder (PTSD). To date, emerging evidence from behavior and psychology suggests the possibility of underlying neurobiological mechanisms in transdiagnostic depression. This study aims to identify predictive signatures of depression severity across MDD and PTSD using a whole-brain connectivity machine learning analysis based on resting-state functional magnetic resonance imaging (rs-fMRI). Patients with MDD (n = 84) and PTSD (n = 65), all medication-free at the time of enrollment, underwent rs-fMRI scans along with a battery of clinical assessments. Using a multivariate machine learning approach, we applied sparse connectome predictive modeling to identify a functional connectivity signature that predicts individual depression severity, as assessed by Hamilton Depression Rating Scale-17 items. The cross-validated model explained 42% of the variance in depression severity across MDD and PTSD. The identified connectome signature predominantly involved regions in the fronto-limbic circuit (e.g., middle frontal gyrus and temporal pole), subcortical areas (e.g., hippocampal, caudate, and brainstem), and the cerebellum. Our findings highlight diffuse whole-brain dysfunction patterns associated with depressive symptom severity, emphasizing the importance of transdiagnostic research in understanding the neurobiological mechanisms underlying key clinical features across disorders.

PMID:41045096 | DOI:10.1111/ejn.70271

J Neurochem. 2025 Oct;169(10):e70217. doi: 10.1111/jnc.70217.

ABSTRACT

Brain's high energy demands require abundant production of ATP from glucose oxidation, mandating coupling between neural activity and nutrient supply. Understanding how neural activity augments blood flow (CBF) to support metabolism of glucose (CMRglc) and oxygen (CMRO2) can help unravel mysteries of neurovascular and neurometabolic couplings underlying functional MRI (fMRI) with blood oxygenation level-dependent (BOLD) contrast. Key to this enigma is oxygen extraction fraction (OEF). Fundamentally, OEF is defined by flow-metabolism (i.e., CBF-CMRO2) coupling generating mitochondrial ATP to signify limits of hypoxia and ischemia. However, to fully account for observed CBF-CMRO2 coupling, the OEF must include a term for oxygen diffusivity (DO2) that is regulated by rheological properties of blood. BOLD contrast depends on intravoxel spin dephasing of tissue water protons due to paramagnetic fields generated by deoxyhemoglobin. During augmented neural activity, if CBF increases more than CMRO2, then deoxyhemoglobin (paramagnetic) is replaced by perfusing oxyhemoglobin (diamagnetic) to increase BOLD signal. Calibrated fMRI converts BOLD contrast into OEF according to the deoxyhemoglobin dilution model. Agreement across these OEF models (i.e., OEF trifecta) authenticates calibrated fMRI, both gas-based and gas-free methods. CMRO2 by gas-free calibrated fMRI easily and reproducibly tracks neural activity, while combining it with CMRglc can also reveal aerobic glycolysis. In summary, there is translational potential of gas-free calibrated fMRI for metabolic imaging in the resting and stimulated brain, from neurodegeneration to neurological disorders.

PMID:41044817 | DOI:10.1111/jnc.70217

bioRxiv [Preprint]. 2025 Sep 22:2025.09.22.677908. doi: 10.1101/2025.09.22.677908.

ABSTRACT

Introductory university physics courses often face the dual challenge of introducing students to new physics concepts while also addressing their preconceived notions that conflict with scientific principles. Active learning pedagogical approaches, which employ constructivist principles and emphasize active participation in the learning process, have been shown to be effective in teaching complex physics concepts. However, while the behavioral effects of constructivist methodologies are largely understood, the neurobiological underpinnings that facilitate this process remain unclear. Using functional magnetic resonance imaging (fMRI), we assessed students enrolled in either an active learning or lecture-based physics course before and after a 15-week semester of learning and examined changes in hippocampal whole-brain connectivity. We focused on the hippocampus given its critical role in learning and memory. Our findings revealed that hippocampal connectivity with brain regions in the frontal and parietal lobes decreased over time, regardless of instructional approach. Results also indicated that active learning students exhibited increased hippocampal connectivity with parietal, cerebellar, and frontal regions, reflecting experiential learning based on constructivist principles, whereas lecture-based students exhibited increased hippocampal connectivity with the fusiform gyrus, suggesting learning through passive observation. Our findings demonstrate that while some aspects of hippocampal functional connectivity may decrease over time, active vs. passive learning may preferentially enhance hippocampal connectivity during physics learning.

PMID:41040351 | PMC:PMC12486134 | DOI:10.1101/2025.09.22.677908

Nat Commun. 2025 Oct 3;16(1):8817. doi: 10.1038/s41467-025-63633-6.

ABSTRACT

Changes in task-evoked activity in the entorhinal cortex (EC) and hippocampus have been shown to track changes in temporal context at short and long timescales. However, whether spontaneous changes in EC and hippocampal neural signals-in the absence of task demands-likewise reflect the passage of time remains unknown. Here, we leveraged a dense-sampling study in which two individuals underwent daily resting-state fMRI for 30 days. Similarity in EC- and anterior hippocampal-whole-brain resting connectivity patterns was negatively correlated with the time interval between sessions, suggesting a spontaneous, slow-drifting neural signature of time. These changes could not be explained by other time-varying factors (including session-wise changes in mood, hormones, or motion). Hippocampal connectivity temporal drifts followed an anterior-to-posterior gradient, and anterolateral EC showed stronger temporal drift than posteromedial EC. Finally, posterior networks (including visual and default mode) primarily drove drifts in EC- and hippocampal-whole-brain connectivity over time. Collectively, these findings reveal a resting-state connectivity signature that reflects the passage of time in the absence of task demands and follows a functional gradient along the longitudinal axis of the hippocampus.

PMID:41044064 | PMC:PMC12494713 | DOI:10.1038/s41467-025-63633-6

Neuroimage. 2025 Oct 1;321:121502. doi: 10.1016/j.neuroimage.2025.121502. Online ahead of print.

ABSTRACT

Resting-state functional MRI (fMRI) signals capture physiological processes, including systemic low-frequency oscillations (LFOs), respiration, and cardiac pulsations. These physiological oscillations-often treated as noise in functional connectivity analysis-reflect fundamental aspects of brain physiology and have recently been recognized as key drivers of brain waste clearance. However, these critical physiological signals are obscured in fMRI data due to slow sampling rates (typical repetition time (TR) > 0.8 s), which cause cardiac signal to alias into lower frequencies. To resolve physiological signals in fMRI datasets, we leveraged fast cross-slice sampling within each TR to hypersample the fMRI signal. A key novelty of this study is the development of a region-specific hypersampling approach, called HyPER (Hypersampling for Physiological signal Extraction in a Region-specific manner). HyPER enhances temporal resolution within coherently pulsating vascular and tissue compartments, including the major cerebral arteries, the superior sagittal sinus (SSS), gray matter (GM), and white matter (WM). This study is structured in three parts: (1) We developed and validated the HyPER approach using fast fMRI from a local dataset in four regions of interest: the major cerebral arteries, SSS, GM, and WM. (2) We applied this approach to the publicly available Human Connectome Project-Aging (HCP-A) dataset (ages 36-90 years), increasing the resolvable frequency by ninefold-from 0.625 Hz to 5.625 Hz-enabling clear separation of cardiac, respiration, and LFO oscillations. (3) We investigated how brain physiological pulsations change with age. Our findings revealed an age-related increase in cardiac and respiratory pulsations across all brain regions, likely reflecting an increased vessel stiffness and reduced dampening of high-frequency pulsations along the vascular network. In contrast, LFO pulsations generally declined with age, suggesting reduced vasomotion in the older brain. In summary, we demonstrated the feasibility and reliability of a region-specific hypersampling technique to resolve physiological pulsations in fMRI. This method can be broadly applied to existing fMRI datasets to uncover hidden physiological pulsations and advance our understanding of brain physiology and disease-related alterations.

PMID:41043798 | DOI:10.1016/j.neuroimage.2025.121502

Brain Res Bull. 2025 Oct 1:111563. doi: 10.1016/j.brainresbull.2025.111563. Online ahead of print.

ABSTRACT

This study explored the effects of 36-hour acute sleep deprivation on the functional connectivity of auditory-related brain regions in healthy young males, examining its associations with alertness and emotional states. Sixty participants were assessed before and after sleep deprivation using psychomotor vigilance tasks, sleepiness scales, mood scales, and resting-state fMRI. The findings indicated significant changes in the functional connectivity of auditory-related brain regions, involving multiple cognitive, emotional, and motor areas. Further correlation analysis revealed a complex relationship between auditory-related brain regions and alertness, sleepiness, and mood. This study provides new evidence on how sleep deprivation influences auditory-related brain function.

PMID:41043695 | DOI:10.1016/j.brainresbull.2025.111563

Biol Psychiatry. 2025 Oct 1:S0006-3223(25)01487-8. doi: 10.1016/j.biopsych.2025.09.012. Online ahead of print.

ABSTRACT

Functional magnetic resonance imaging (fMRI) data are often used to inform individual differences in cognitive, behavioral, and psychiatric phenotypes. These so-called "brain-behavior" association studies come in many flavors and are increasingly the focus of investigations utilizing large population neuroscience datasets. Still, many open questions surrounding the utility of task and resting state fMRI for modelling brain-behavior relationships remain, including the feasibility of conducting these investigations in developmental cohorts. With the growing availability of large neurodevelopmental datasets such as that provided by the Adolescent Brain Cognitive Development (ABCD) Study, we are now able to conduct well-powered analyses using large samples of longitudinal neuroimaging data collected from diverse populations of youth. Here we provide a high-level review of current controversies and challenges in this growing subfield of neuroscience, highlighting examples where task fMRI data and resting state fMRI data - either in isolation or combined - have yielded significant insights into brain-behavior associations. Challenges include issues related to measurement noise, appropriate estimation of effect sizes, and limits to generalizability due to insufficient diversity of samples. Innovative solutions involving advanced MRI data acquisition protocols, application of multivariate analysis methods, and more robust consideration of phenotypic complexity are reviewed. We propose that additional future directions for developmental cognitive neuroscience should include more reliable behavioral measures, multimodal neuroimaging brain-behavior studies, and greater consideration of environmental and other contextual influences on brain-behavior associations.

PMID:41043534 | DOI:10.1016/j.biopsych.2025.09.012

medRxiv [Preprint]. 2025 Sep 25:2025.09.23.25336175. doi: 10.1101/2025.09.23.25336175.

ABSTRACT

Identifying robust neuroimaging biomarkers for Alzheimer's disease (AD) and mild cognitive impairment (MCI) is essential for early diagnosis and intervention. In this study, we introduce a novel, fully automated, guided dynamic functional connectivity (dFNC) framework to extract multiple dynamic measures for distinguishing MCI/AD from cognitively normal (CN) individuals. Resting-state fMRI data were used to extract subject-specific brain networks via spatially constrained independent component analysis (scICA), using a multi-objective optimization framework to ensure alignment with known functional networks while preserving individual variability. Using these components, dFNC was computed through a sliding-window approach. ICA was then applied to the concatenated dFNC matrices from the UK Biobank (UKBB) dataset to identify five canonical brain states, each representing a replicable, independent pattern of connectivity. These states served as biologically informed priors in a state-constrained ICA (St-cICA), which was applied to each subject in the combined OASIS-3 and ADNI datasets to guide individual-level decomposition and ensure interpretable connectivity states guided by state priors derived from the normative UKBB sample. St-cICA extracted subject-specific dFNC features and associated weighted timecourses. To characterize dFNC patterns, we computed metrics from the most strongly expressed (primary) state and introduce estimation of the second-most expressed (secondary) state at each timepoint, including dwell time, occupancy rate, and transition probabilities. Group comparisons using two-sample t-tests revealed widespread and significant alterations in AD/MCI compared to CN individuals. AD/MCI participants exhibited higher dwell times and increased self-transitions, indicating reduced neural flexibility and a tendency to remain in specific connectivity states. In contrast, CN individuals showed more diverse and recurrent transitions, reflecting greater adaptability. Secondary transitions revealed widespread selective switching in CN, whereas AD/MCI showed reduced cross-state engagement. A classification model trained on 6,960 dynamic features achieved strong performance in distinguishing AD/MCI from CN (mean AUC ≈ 0.85). These findings highlight the potential of guided dFNC as a biomarker framework for early-stage AD detection using resting-state fMRI.

PMID:41040719 | PMC:PMC12485992 | DOI:10.1101/2025.09.23.25336175

bioRxiv [Preprint]. 2025 Sep 25:2025.09.23.678044. doi: 10.1101/2025.09.23.678044.

ABSTRACT

Mouse models are an essential tool for understanding behavior and disease states in neuroscience research. While genetic and sex-specific effects have been reported in many neurodegenerative and psychiatric illnesses, these factors may also alter baseline neuroanatomical features of mice. This raises the question of whether the observed changes are related to the disease being studied (i.e., pathological differences) or if there are baseline strain or sex differences that may potentially predispose animals to different responses. Over the past decade, tremendous effort has been made in mapping neural architecture at various scales; however, the complex relationships including identifying genetic and sex-specific differences in brain structure and function remain understudied. To bridge this gap, we used C57BL/6J and DBA/2J mice, two of the most widely used inbred mouse strains in neuroscience research, to investigate strain and sex-specific features of the brain connectome in awake animals using magnetic resonance imaging (MRI). By combining resting-state fMRI and diffusion MRI, we found that the motor, sensory, limbic, and salience networks exhibit significant differences in both functional and structural domains between C57BL/6J and DBA/2J mice. Further, functional and structural properties of the brain were significantly correlated in both strains. Our results underscore the importance of considering these baseline differences when interpreting the brain-behavior interactions in mouse models of human disorders.

PMID:41040314 | PMC:PMC12485708 | DOI:10.1101/2025.09.23.678044

bioRxiv [Preprint]. 2025 Sep 25:2025.09.24.678309. doi: 10.1101/2025.09.24.678309.

ABSTRACT

Dynamic coordination of metabolic demand across brain networks supports emerging cognitive abilities and may drive overall cognitive development, yet how these dynamics vary by sex and relate to cognition in late childhood remains unclear. Using resting-state fMRI from 2,000 healthy 9- to 11-year-olds in the ABCD study, we applied time-resolved dynamic time warping to quantify amplitude mismatches, a proxy of relative energy demand across brain intrinsic networks. Clustering revealed three recurring states: convergent (globally balanced), divergent (imbalanced), and mixed (intermediate). Females spent engaged more with the flexible mixed state, whereas males lingered longer in convergent and divergent states. Across the cohort, better performance on cognitive flexibility, processing speed, and long-term memory tasks correlated with greater overall time in the mixed state and with higher transition rates, but with shorter dwell in any single state. These findings indicate that neural flexibility, rather than prolonged stability, supports cognition during late childhood and that sex differences in dynamic energy coordination emerge well before adolescence.

PMID:41040180 | PMC:PMC12485794 | DOI:10.1101/2025.09.24.678309