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1. Contrasting patterns of extrasynaptic NMDAR-GluN2B expression in macaque subgenual cingulate and dorsolateral prefrontal cortices

   https://doi.org/10.3389/fnana.2025.1553056  

   Joyce, Mary_Kate P; Datta, Dibyadeep; Arellano, Jon I; Duque, Alvaro; Morozov, Yury M; Morrison, John H; Arnsten, Amy_F T (April 2025, Frontiers in Neuroanatomy)

   Expression of the N-methyl-D-aspartate receptor, particularly when containing the GluN2B subunit (NMDAR-GluN2B), varies across the prefrontal cortex (PFC). In humans, the subgenual cingulate cortex (SGC) contains among the highest levels of NMDAR-GluN2B expression, while the dorsolateral prefrontal cortex (dlPFC) exhibits a more moderate level of NMDAR-GluN2B expression. NMDAR-GluN2B are commonly associated with ionotropic synaptic function and plasticity and are essential to the neurotransmission underlying working memory in the macaque dlPFC in the layer III circuits, which in humans are afflicted in schizophrenia. However, NMDAR-GluN2B can also be found at extrasynaptic sites, where they may trigger distinct events, including some linked to neurodegenerative processes. The SGC is an early site of tau pathology in sporadic Alzheimer’s disease (sAD), which mirrors its high NMDAR-GluN2B expression. Additionally, the SGC is hyperactive in depression, which can be treated with NMDAR antagonists. Given the clinical relevance of NMDAR in the SGC and dlPFC, the current study used immunoelectron microscopy (immunoEM) to quantitatively compare the synaptic and extrasynaptic expression patterns of NMDAR-GluN2B across excitatory and inhibitory neuron dendrites in rhesus macaque layer III SGC and dlPFC. We found a larger population of extrasynaptic NMDAR-GluN2B in dendrites of putative pyramidal neurons in SGC as compared to the dlPFC, while the dlPFC had a higher proportion of synaptic NMDAR-GluN2B. In contrast, in putative inhibitory dendrites from both areas, extrasynaptic expression of NMDAR-GluN2B was far more frequently observed over synaptic expression. These findings may provide insight into varying cortical vulnerability to alterations in excitability and neurodegenerative forces. 

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2. DSAM: A deep learning framework for analyzing temporal and spatial dynamics in brain networks

   https://doi.org/10.1016/j.media.2025.103462  

   Thapaliya, Bishal; Miller, Robyn; Chen, Jiayu; Wang, Yu Ping; Akbas, Esra; Sapkota, Ram; Ray, Bhaskar; Suresh, Pranav; Ghimire, Santosh; Calhoun, Vince D; et al (April 2025, Medical Image Analysis)

   Resting-state functional magnetic resonance imaging (rs-fMRI) is a noninvasive technique pivotal for understanding human neural mechanisms of intricate cognitive processes. Most rs-fMRI studies compute a single static functional connectivity matrix across brain regions of interest, or dynamic functional connectivity matrices with a sliding window approach. These approaches are at risk of oversimplifying brain dynamics and lack proper consideration of the goal at hand. While deep learning has gained substantial popularity for modeling complex relational data, its application to uncovering the spatiotemporal dynamics of the brain is still limited. In this study we propose a novel interpretable deep learning framework that learns goal-specific functional connectivity matrix directly from time series and employs a specialized graph neural network for the final classification. Our model, DSAM, leverages temporal causal convolutional networks to capture the temporal dynamics in both low- and high-level feature representations, a temporal attention unit to identify important time points, a self-attention unit to construct the goal-specific connectivity matrix, and a novel variant of graph neural network to capture the spatial dynamics for downstream classification. To validate our approach, we conducted experiments on the Human Connectome Project dataset with 1075 samples to build and interpret the model for the classification of sex group, and the Adolescent Brain Cognitive Development Dataset with 8520 samples for independent testing. Compared our proposed framework with other state-of-art models, results suggested this novel approach goes beyond the assumption of a fixed connectivity matrix, and provides evidence of goal-specific brain connectivity patterns, which opens up potential to gain deeper insights into how the human brain adapts its functional connectivity specific to the task at hand. 

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3. Neuromodulation of prefrontal cortex cognitive function in primates: the powerful roles of monoamines and acetylcholine

   https://doi.org/10.1038/s41386-021-01100-8  

   Cools, Roshan; Arnsten, Amy F. (January 2022, Neuropsychopharmacology)

   Abstract The primate prefrontal cortex (PFC) subserves our highest order cognitive operations, and yet is tremendously dependent on a precise neurochemical environment for proper functioning. Depletion of noradrenaline and dopamine, or of acetylcholine from the dorsolateral PFC (dlPFC), is as devastating as removing the cortex itself, and serotonergic influences are also critical to proper functioning of the orbital and medial PFC. Most neuromodulators have a narrow inverted U dose response, which coordinates arousal state with cognitive state, and contributes to cognitive deficits with fatigue or uncontrollable stress. Studies in monkeys have revealed the molecular signaling mechanisms that govern the generation and modulation of mental representations by the dlPFC, allowing dynamic regulation of network strength, a process that requires tight regulation to prevent toxic actions, e.g., as occurs with advanced age. Brain imaging studies in humans have observed drug and genotype influences on a range of cognitive tasks and on PFC circuit functional connectivity, e.g., showing that catecholamines stabilize representations in a baseline-dependent manner. Research in monkeys has already led to new treatments for cognitive disorders in humans, encouraging future research in this important field. 

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4. Static and Dynamic Cross‐Network Functional Connectivity Shows Elevated Entropy in Schizophrenia Patients

   https://doi.org/10.1002/hbm.70134  

   Maksymchuk, Natalia; Miller, Robyn_L; Bustillo, Juan_R; Ford, Judith_M; Mathalon, Daniel_H; Preda, Adrian; Pearlson, Godfrey_D; Calhoun, Vince_D (February 2025, Human Brain Mapping)

   ABSTRACT Schizophrenia (SZ) patients exhibit abnormal static and dynamic functional connectivity across various brain domains. We present a novel approach based on static and dynamic inter‐network connectivity entropy (ICE), which represents the entropy of a given network's connectivity to all the other brain networks. This novel approach enables the investigation of how connectivity strength is heterogeneously distributed across available targets in both SZ patients and healthy controls. We analyzed fMRI data from 151 SZ patients and 160 demographically matched healthy controls (HC). Our assessment encompassed both static and dynamic ICE, revealing significant differences in the heterogeneity of connectivity levels across available functional brain networks between SZ patients and HC. These networks are associated with subcortical (SC), auditory (AUD), sensorimotor (SM), visual (VIS), cognitive control (CC), default mode network (DMN), and cerebellar (CB) functional brain domains. Elevated ICE observed in individuals with SZ suggests that patients exhibit significantly higher randomness in the distribution of time‐varying connectivity strength across functional regions from each source network, compared to HC. C‐means fuzzy clustering analysis of functional ICE correlation matrices revealed that SZ patients exhibit significantly higher occupancy weights in clusters with weak, low‐scale functional entropy correlation, while the control group shows greater occupancy weights in clusters with strong, large‐scale functional entropy correlation. K‐means clustering analysis on time‐indexed ICE vectors revealed that cluster with highest ICE have higher occupancy rates in SZ patients whereas clusters characterized by lowest ICE have larger occupancy rates for control group. Furthermore, our dynamic ICE approach revealed that in HC, the brain primarily communicates through complex, less structured connectivity patterns, with occasional transitions into more focused patterns. Individuals with SZ are significantly less likely to attain these more focused and structured transient connectivity patterns. The proposed ICE measure presents a novel framework for gaining deeper insight into mechanisms of healthy and diseased brain states and represents a useful step forward in developing advanced methods to help diagnose mental health conditions. 

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5. The dynamics of dynamic time warping in fMRI data: A method to capture inter-network stretching and shrinking via warp elasticity

   https://doi.org/10.1162/imag_a_00187  

   Wiafe, Sir-Lord; Faghiri, Ashkan; Fu, Zening; Miller, Robyn; Preda, Adrian; Calhoun, Vince D (May 2024, Imaging Neuroscience)

   Abstract In neuroimaging research, understanding the intricate dynamics of brain networks over time is paramount for unraveling the complexities of brain function. One approach commonly used to explore the dynamic nature of brain networks is functional connectivity analysis. However, while functional connectivity offers valuable insights, it fails to consider the diverse timescales of coupling between different brain regions. This gap in understanding leaves a significant aspect of brain dynamics unexplored in neuroimaging research. We propose an innovative approach that delves into the dynamic coupling/connectivity timescales of brain regions relative to one another, focusing on how brain region couplings stretch or shrink over time, rather than relying solely on functional connectivity measures. Our method introduces a novel metric called “warping elasticity,” which utilizes dynamic time warping (DTW) to capture the temporal nuances of connectivity. Unlike traditional methods, our approach allows for (potentially nonlinear) dynamic compression and expansion of the time series, offering a more intricate understanding of how coupling between brain regions evolves. Through the adaptive windows employed by the DTW method, we can effectively capture transient couplings within varying connectivity timescales of brain network pairs. In extensive evaluations, our method exhibits high replicability across subjects and diverse datasets, showcasing robustness against noise. More importantly, it uncovers statistically significant distinctions between healthy control (HC) and schizophrenia (SZ) groups through the identification of warp elasticity states. These states are cluster centroids, representing the warp elasticity across subjects and time, offering a novel perspective on the dynamic nature of brain connectivity, distinct from conventional metrics focused solely on functional connectivity. For instance, controls spend more time in a warp elasticity state characterized by timescale stretching of the visual domain relative to other domains, suggesting disruptions in the visual cortex. Conversely, patients show increased time spent in a warp elasticity state with stretching timescales in higher cognitive areas relative to sensory regions, indicative of prolonged cognitive processing of sensory input. Overall, our approach presents a promising avenue for investigating the temporal dynamics of brain network interactions in functional magnetic resonance imaging (fMRI) data. By focusing on the elasticity of connectivity timescales, rather than adhering to functional connectivity metrics, we pave the way for a deeper understanding of neuropsychiatric disorders in neuroscience research. 

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