Structure determines function. However, this universal theme in biology has been surprisingly difficult to observe in human brain neuroimaging data. Here, we link structure to function by hypothesizing that brain signals propagate as a Markovian process on an underlying structure. We focus on a metric called commute time: the average number of steps for a random walker to go from region A to B and then back to A. Commute times based on white matter tracts from diffusion MRI exhibit an average ± standard deviation Spearman correlation of −0.26 ± 0.08 with functional MRI connectivity data across 434 UK Biobank individuals and −0.24 ± 0.06 across 400 HCP Young Adult brain scans. The correlation increases to −0.36 ± 0.14 and to −0.32 ± 0.12 when the principal contributions of both commute time and functional connectivity are compared for both datasets. The correlations are stronger by 33% compared to broadly used communication measures such as search information and communicability. The difference further widens to a factor of 5 when commute times are correlated to the principal mode of functional connectivity from its eigenvalue decomposition. Overall, the study points to the utility of commute time to account for the role of polysynaptic (indirect) connectivity underlying brain function by assuming that signals randomly traverse along the underlying brain structure.

magnetic resonance imaging, structural MRI, functional MRI, fMRI, magnetic resonance spectroscopy, MRS, glucose, atrophy, aging, fasting, metabolic, metabolism, diabetes, brain

model, modeling, glucose, ketones, ketosis, Neuroblox, magnetic resonance spectroscopy, MRS, metabolic, metabolism, flux equations, glycolysis, mitochondria, energy

Discovering governing equations that describe complex chaotic systems remains a fundamental challenge in physics and neuroscience. Here, we introduce the PEM-UDE method, which combines the prediction-error method with universal differential equations to extract interpretable mathematical expressions from chaotic dynamical systems, even with limited or noisy observations. This approach succeeds where traditional techniques fail by smoothing optimization landscapes and removing the chaotic properties during the fitting process without distorting optimal parameters. We demonstrate its efficacy by recovering hidden states in the Rossler system and reconstructing dynamics from noise-corrupted electrical-circuit data, in which the correct functional form of the dynamics is recovered even when one of the observed time series is corrupted by noise 5x the magnitude of the true signal. We demonstrate that this method can recover the correct dynamics, whereas direct symbolic regression methods, such as STLSQ, fail to do so with the available data and noise. Importantly, when applied to neural populations, our method derives novel governing equations that respect biological constraints such as network sparsity — a constraint necessary for cortical information processing yet not captured in next-generation neural mass models — while preserving microscale neuronal parameters. These equations predict an emergent relationship between connection density and both oscillation frequency and synchrony in neural circuits. We validate these predictions using three intracranial electrode recording datasets from the medial entorhinal cortex, prefrontal cortex, and orbitofrontal cortex. Our work provides a pathway to develop mechanistic, multi-scale brain models that generalize across diverse neural architectures, bridging the gap between single-neuron dynamics and macroscale brain activity.

network, fMRI, functional magnetic resonance imaging, fMRI, MRI, genetics, genome, diabetes, male, female, age, aging, dementia, metabolic, metabolism

The men and women who worked in rescue and recovery operations at the 9/11 World Trade Center site are developing cognitive impairment (CI) at mid-life, decades before CI is usually detected. To date, one of the most consistent risk factors for CI in this population is symptoms of post-traumatic stress disorder (PTSD). However, little is known about the mechanistic cascade that drives stress-related neurological changes to accelerate cognitive decline in the human brain. We used machine learning to identify distinct brain signatures from functional magnetic resonance imaging between trauma-exposed healthy controls (TEHC; N = 30; 21 men), PTSD without CI (PTSD-CI; N = 19; 16 men), and PTSD with CI (PTSD + CI; N = 22; 18 men). We compared the spatial gradient of each functional signature to the distribution of mRNA expression in the brain. We applied structural equation modeling (SEM) to infer mechanistic cascades specific to each group. While modest accuracy was achieved for the PTSD–CI versus TEHC signature (0.67), clear differentiation was observed for PTSD + CI versus TEHC (0.73) and PTSD + CI versus PTSD–CI (0.85). Consistent significant correlations were found between PTSD + CI signatures and ZNF48, TOMM40, and GRIN1 expression distributions. The cortisol-induced neurotoxicity pathway was consistently found with the PTSD + CI signature, while the p53 signaling pathway was observed across all PTSD signatures. Our results reinforce peripheral biomarkers from a previous transcriptomic study and suggest functional biomarkers in PTSD and PTSD-related CI. Furthermore, our SEM results suggest that PTSD and PTSD-related CI may diverge at the mechanistic level, with neurotoxicity being specific to CI.

Precision neurotherapeutics represents a transformative paradigm shift from standardized "one-size-fits-all" treatments of neurological, neurodegenerative, and/or psychiatric disorders toward individualized interventions that leverage patient-specific biological, behavioral, and physiological characteristics. Traditional neurotherapeutic approaches achieve modest response rates of 30–60% for first-line treatments, necessitating personalized strategies that account for individual differences in genetics, brain structure and function, and treatment response profiles. This review examines advances across three core domains: pharmaceutical approaches utilizing fragment-based drug discovery, pharmacokinetic modeling, and quantitative systems pharmacology; neuromodulation technologies evolving from open-loop to adaptive closed-loop systems with real-time biomarker feedback; and biomarker development spanning neuroimaging, pharmacogenomics, and digital health applications. Critical challenges include developing robust methodological frameworks for single-subject parameter estimation, addressing signal-to-noise ratio limitations in neuroimaging, and navigating complex regulatory landscapes. The convergence of artificial intelligence, computational modeling, and US Food and Drug Administration policy shifts toward in silico approaches creates unprecedented opportunities for mechanistically informed biomarkers that can guide truly personalized mental health care.

Neuroblox, model, modeling, multiscale, biomimetic computational primitive, BCP, category learning, cognition, neuron, incongruent, macaque

psychiatry, metabolism, metabolic, ketosis, ketogenic

Understanding the key drivers of brain aging is essential for effective prevention and treatment of neurodegenerative diseases. Here, we integrate human brain and physiological data to investigate underlying mechanisms. Functional MRI analyses across four large datasets (totaling 19,300 participants) show that brain networks not only destabilize throughout the lifetime but do so along a nonlinear trajectory, with consistent temporal “landmarks” of brain aging starting in midlife (40s). Comparison of metabolic, vascular, and inflammatory biomarkers implicate dysregulated glucose homeostasis as the driver mechanism for these transitions. Correlation between the brain’s regionally heterogeneous patterns of aging and gene expression further supports these findings, selectively implicating GLUT4 (insulin-dependent glucose transporter) and APOE (lipid transport protein). Notably, MCT2 (a neuronal, but not glial, ketone transporter) emerges as a potential counteracting factor by facilitating neurons’ energy uptake independently of insulin. Consistent with these results, an interventional study of 101 participants shows that ketones exhibit robust effects in restabilizing brain networks, maximized from ages 40 to 60, suggesting a midlife “critical window” for early metabolic intervention.

One of the primary challenges in metabolic psychiatry is that the disrupted brain functions that underlie psychiatric conditions arise from a complex set of downstream and feedback processes that span multiple spatiotemporal scales. Importantly, the same circuit can have multiple points of failure, each of which results in a different type of dysregulation, and thus elicits distinct cascades downstream that produce divergent signs and symptoms. Here, we illustrate this challenge by examining how subtle differences in circuit perturbations can lead to divergent clinical outcomes. We also discuss how computational models can perform the spatially heterogeneous integration and bridge in vitro and in vivo paradigms. By leveraging recent methodological advances and tools, computational models can integrate relevant processes across scales (e.g., tricarboxylic acid cycle, ion channel, neural microassembly, whole-brain macrocircuit) and across physiological systems (e.g., neural, endocrine, immune, vascular), providing a framework that can unite these mechanistic processes in a manner that goes beyond the conceptual and descriptive to the quantitative and generative. These hold the potential to sharpen our intuitions toward circuit-based models for personalized diagnostics and treatment. Allostasis; Circuit; Computational; Feedback; Homeostasis; Metabolic; Neural.

We investigate the impact of noise on parameter fitting for an Ornstein-Uhlenbeck process, focusing on the effects of multiplicative and thermal noise on the accuracy of signal separation. To address these issues, we propose algorithms and methods that can effectively distinguish between thermal and multiplicative noise and improve the precision of parameter estimation for optimal data analysis. Specifically, we explore the impact of both multiplicative and thermal noise on the obfuscation of the actual signal and propose methods to resolve them. First, we present an algorithm that can effectively separate thermal noise with comparable performance to Hamilton Monte Carlo (HMC) methods, but with significantly improved speed. We then analyze multiplicative noise and demonstrate that HMC is insufficient for isolating thermal and multiplicative noise. However, we show that with additional knowledge of the ratio between thermal and multiplicative noise, we can accurately distinguish between the two types of noise when provided with a sufficiently large sampling rate or an amplitude of multiplicative noise that is smaller than the thermal noise. Thus, we demonstrate the mechanism underlying an otherwise counterintuitive phenomenon: when multiplicative noise dominates the noise spectrum, one can successfully estimate the parameters for such systems after adding additional white noise to shift the noise balance.

All fields of science depend on mathematical models. Occam's razor refers to the principle that good models should exclude parameters beyond those minimally required to describe the systems they represent. This is because redundancy can lead to incorrect estimates of model parameters from data, and thus inaccurate or ambiguous conclusions. Here, we show how deep learning can be powerfully leveraged to apply Occam's razor to model parameters. Our method, FixFit, uses a feedforward deep neural network with a bottleneck layer to characterize and predict the behavior of a given model from its input parameters. FixFit has three major benefits. First, it provides a metric to quantify the original model's degree of complexity. Second, it allows for the unique fitting of data. Third, it provides an unbiased way to discriminate between experimental hypotheses that add value versus those that do not. In three use cases, we demonstrate the broad applicability of this method across scientific domains. To validate the method using a known system, we apply FixFit to recover known composite parameters for the Kepler orbit model and a dynamic model of blood glucose regulation. In the latter, we demonstrate the ability to fit the latent parameters to real data. To illustrate how the method can be applied to less well-established fields, we use it to identify parameters for a multi-scale brain model and reduce the search space for viable candidate mechanisms.

The integration-segregation framework is a popular first step to understand brain dynamics because it simplifies brain dynamics into two states based on global versus local signaling patterns. However, there is no consensus for how to best define the two states. Here, we map integration and segregation to order and disorder states from the Ising model in physics to calculate state probabilities, Pint and Pseg, from functional MRI data. We find that integration decreases and segregation increases with age across three databases. Changes are consistent with weakened connection strength among regions rather than topological connectivity based on structural and diffusion MRI data.

The brain primarily relies on glycolysis for mitochondrial respiration but switches to alternative fuels such as ketone bodies (KBs) when less glucose is available. Neuronal KB uptake, which does not rely on glucose transporter 4 (GLUT4) or insulin, has shown promising clinical applicability in alleviating the neurological and cognitive effects of disorders with hypometabolic components. However, the specific mechanisms by which such interventions affect neuronal functions are poorly understood. In this study, we pharmacologically blocked GLUT4 to investigate the effects of exogenous KB D-ꞵ-hydroxybutyrate (D-ꞵHb) on mouse brain metabolism during acute insulin resistance (AIR). We found that both AIR and D-ꞵHb had distinct impacts across neuronal compartments: AIR decreased synaptic activity and long-term potentiation (LTP) and impaired axonal conduction, synchronization, and action potential properties, while D-ꞵHb rescued neuronal functions associated with axonal conduction, synchronization, and LTP.

functional magnetic resonance imaging, fMRI, 7T, ultra high field, phantom, calibration, precision medicine, n=1, BrainDancer, dynamic