A dynamic spatiotemporal representation framework for deciphering personal brain function.

Functional magnetic resonance imaging (fMRI) opens a window on observing spontaneous activities of the human brain in vivo. However, the high complexity of fMRI signals makes brain functional representations intractable. Here, we introduce a state decomposition method to reduce this complexity and decipher individual brain functions at multiple levels.

Developmental shifts in testosterone levels are associated with alterations in the neural oscillatory dynamics serving selective attention.

The transition from childhood into adolescence is associated with marked increases in testosterone, a sex hormone that has been linked with significant changes in brain structure and function. However, the majority of the extant literature on sex hormone effects has focused on structural brain development, with far fewer studies examining changes in the neural dynamics serving higher-order cognitive function and behavioral improvements with development. Herein, we investigated whether the neural oscillatory dynamics serving selective attention were sensitive to testosterone levels as a marker of development in a sample of 87 participants aged 6-13 years old.

Neural activity is altered by childhood trauma exposure and varied by sex in typically developing youths during sustained attention-to-response tasks (SART).

It is well recognized that adults with exposure to childhood traumas are at risk of developing psychopathology and executive dysfunction. However, how these executive function deficits emerge following trauma exposure has not been widely examined. We hypothesized that children exposed to a higher number of early childhood traumas would show reduced amplitude and longer latency in cortical response in executive brain regions during tasks requiring sustained attention and inhibition, compared to children with fewer or no such experiences.

TransUNET-DDPM: A transformer-enhanced diffusion model for subject-specific brain network generation and classification.

Generative AI for image synthesis has significantly progressed with the advent of advanced diffusion models. These models have set new benchmarks in creating high-quality and meaningful visual information. In this paper, we introduce TransUNET-DDPM, a novel framework that fuses transformer-based architectures with denoising diffusion probabilistic models (DDPMs) to generate high-quality, 2D and 3D intrinsic connectivity networks (ICNs).

From Symptomatic to Pre-symptomatic: Adaptive Knowledge Distillation for Early Alzheimer's Detection Using Functional MRI.

Alzheimer's disease (AD) progresses from asymptomatic changes to clinical symptoms, underscoring the critical need for early detection to facilitate timely treatment. Functional magnetic resonance imaging (fMRI) offers non-invasive biomarkers for detection, but current methods fail to reliably identify pre-symptomatic individuals due to two key challenges: (1) Subtle, anatomically distinct fMRI patterns in pre-symptomatic cases that resemble healthy controls more than symptomatic patients, and (2) Severe class imbalance in real-world data, where healthy controls vastly outnumber pre-symptomatic subjects. To address this, we reconceptualize AD diagnosis as a multi-stage distillation task, where insights from easier-to-detect symptomatic cases guide pre-symptomatic detection.

Functional correspondences in the human and marmoset visual cortex during movie watching: Insights from correlation, redundancy, and synergy.

The world of beauty is deeply connected to the visual cortex, as perception often begins with vision in both humans and marmosets. Quantifying functional correspondences in the visual cortex across species can help us understand how information is processed in the primate visual cortex, while also providing deeper insights into human visual cortex functions through the study of marmosets. In this study, to investigate their functional correspondences, we used 13 healthy human volunteers (9 males and 4 females, aged 22-56 years) and 8 common marmosets (6 males and 2 females, aged 20-42 months).

Confound Controlled Multimodal Neuroimaging Data Fusion and Its Application to Developmental Disorders.

Multimodal fusion provides multiple benefits over single modality analysis by leveraging both shared and complementary information from different modalities. Notably, supervised fusion enjoys extensive interest for capturing multimodal co-varying patterns associated with clinical measures. A key challenge of brain data analysis is how to handle confounds, which, if unaddressed, can lead to an unrealistic description of the relationship between the brain and clinical measures.

Molecular connectivity studies in neurotransmission: a scoping review.

Positron emission tomography (PET) and single photon emission computed tomography (SPECT) are essential molecular imaging tools for the in vivo investigation of neurotransmission. Traditionally, PET and SPECT images are analysed in a univariate manner, testing for changes in radiotracer binding in regions or voxels of interest independently of each other. Over the past decade, there has been an increasing interest in the so-calledapproach that captures relationships of molecular imaging measures in different brain regions.

zBMI scales with multispectral alterations in the neural oscillatory dynamics serving verbal working memory in youth.

Pediatric obesity is one of the most serious public health issues the world faces today. Deleterious behavioral effects scaling with obesity and body mass have been demonstrated in cognitive tasks in children and adults, yet the neural oscillatory dynamics underlying these effects remain largely unstudied. In this study, 88 youth (6-13 years old) performed a verbal working memory task during high-density magnetoencephalography (MEG).

Effects of an 18-month meditation training on dynamic functional connectivity states in older adults: Secondary analyses from the Age-Well randomized controlled trial.

Meditation training in older adults has been proposed as a non-pharmacological intervention to promote healthy aging and lower the risks of developing Alzheimer's disease (AD). Resting-state dynamic functional network connectivity (dFNC) highlighted two brain states, the "strongly connected" and "default mode network (DMN)-negatively connected" states, associated with protective factors for dementia including AD, and two states, the "weakly connected" and "salience-negatively connected" states, associated with risk factors for dementia. In this study, we aimed at assessing the impact of an 18-month meditation training on dFNC states in older adults.

Association Between Acute Plasma Glial Fibrillary Acidic Protein Levels and White Matter Integrity in Recent Trauma Survivors.

Traumatic brain injuries (TBIs) account for over 2.5 million emergency department (ED) visits each year in the United States. The bulk of TBI research in acute care settings has focused exclusively on individuals who receive computed tomography (CT) scanning.

Functionally Adaptive Structural Basis Sets of the Brain: A Dynamic Fusion Approach.

The precise relationship between brain structure and function has been investigated through a multitude of lenses, but one detail that is held constant across most neuroimaging studies in this space is the identification of a singular structural basis set of the brain, upon which functional activation signals can be reconstructed to examine the linkage between structure and function. Such basis sets can be considered "functionally independent", as they are derived through structural data alone and have no explicit association to functional data. Recent work in multimodal fusion has facilitated a more integrated view of structure-function linkages by enabling the equal contribution of both modalities to the joint decomposition, resulting in components that are independent within modality but co-vary closely across modalities.

Developmental trajectory of neural activity underlying motor control differs by sequence complexity and motor stage.

Primary motor areas in the brain mature relatively early in development, yet the control of complex movements improves through early adulthood. Neural oscillations in higher-order regions are refined in adolescence and contribute to executive processes important for complex motor control, but the neural dynamics among these regions and primary motor cortices remain poorly understood in youth. We recorded magnetoencephalography during a motor sequencing task in 68 healthy youth from ten to 17 years of age.

Predicting Traumatic Brain Injury Post-Trauma Using Temporal Attention on Sleep-Wake Data.

Background: Traumatic Brain Injury (TBI) is a major public health concern, and accurate classification is essential for effective treatment and improved patient outcomes. Sleep/wake behavior has emerged as a potential biomarker for TBI classification, yet the optimal time window in which to identify sleep/wake changes after TBI remains unclear.

Methods: We evaluated daily longitudinal sleep/wake data from a prospective cohort of more than 2,000 emergency department patients with and without blood biomarker-documented TBI (Glial Fibrillary Acidic Protein - GFAP $ > 268 \frac{pg}{ml}$).

A graph transformer-based foundation model for brain functional connectivity network.

Although foundation models have advanced many medical imaging fields, their absence in neuroimage analysis limits progress in neuroscience and clinical practice. Brain functional connectivity (FC) analysis is central to understanding brain function and widely used in neuroscience. We propose a foundation model tailored for brain functional connectivity networks (FCN).

Group Information Guided Smooth Independent Component Analysis Method for Multi-Subject fMRI Data Analysis.

Group independent component analysis (ICA) has been extensively used to extract brain functional networks (FNs) and associated neuroimaging measures from multi-subject functional magnetic resonance imaging (fMRI) data. However, the inherent noise in fMRI data can adversely affect the performance of ICA, often leading to noisy FNs and hindering the identification of network-level biomarkers. To address this challenge, we propose a novel method called group information-guided smooth independent component analysis (GIG-sICA).

Aberrant Cortical-Subcortical-Cerebellar Connectivity in Resting-State fMRI as an Imaging Marker of Schizophrenia and Psychosis: A Systematic Review of Data-Driven Whole-Brain Functional Connectivity Analyses.

Schizophrenia is extremely heterogenous, and the underlying brain mechanisms are not fully understood. Many attempts have been made to substantiate and delineate the relationship between schizophrenia and the brain through unbiased exploratory investigations of resting-state functional magnetic resonance imaging (rs-fMRI). The results of numerous data-driven rs-fMRI studies have converged in support of the disconnection hypothesis framework, reporting aberrant connectivity in cortical-subcortical-cerebellar circuitry.

Toward Granular Brain Intrinsic Connectivity Networks and Insights into Schizophrenia.

Spatial group independent component analysis (sgr-ICA) has become a crucial method to understand brain function in functional magnetic resonance imaging (fMRI) research, especially in resting-state fMRI (rsfMRI) studies. Early studies identified large-scale brain networks using sgr-ICA with lower order (e.g.

A Method for Estimating Dynamic Functional Network Connectivity Gradients (dFNGs) From ICA Captures Smooth Inter-Network Modulation.

Dynamic functional network connectivity (dFNC) analysis is a widely used approach for studying brain function and offering insight into how brain networks evolve over time. Typically, dFNC studies utilize fixed spatial maps and evaluate transient changes in coupling among time courses estimated from independent component analysis (ICA). This manuscript presents a complementary approach that relaxes this assumption by spatially reordering the components dynamically at each time point to optimize for a smooth gradient in the FNC (i.

Distinct white matter alteration patterns in post-infectious and gradual onset chronic fatigue syndrome revealed by diffusion MRI.

While post-infectious (PI-ME/CFS) and gradual onset (GO-ME/CFS) myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) manifest similar symptoms, it has long been suspected that different disease processes underlie them. However, the lack of biological evidence has left this question unanswered. In this study, how white matter microstructural changes in PI-ME/CFS and GO-ME/CFS patients were investigated.

Mutualistic Multi-Network Noisy Label Learning (MMNNLL) Method and Its Application to Transdiagnostic Classification of Bipolar Disorder and Schizophrenia.

The subjective nature of diagnosing mental disorders complicates achieving accurate diagnoses. The complex relationship among disorders further exacerbates this issue, particularly in clinical practice where conditions like bipolar disorder (BP) and schizophrenia (SZ) can present similar clinical symptoms and cognitive impairments. To address these challenges, this paper proposes a mutualistic multi-network noisy label learning (MMNNLL) method, which aims to enhance diagnostic accuracy by leveraging neuroimaging data under the presence of potential clinical diagnosis bias or errors.

Sharing Neuroimaging Data with Squirrel - A Relational Data Format to Store Raw to Analyzed Data and Everything in Between.

Reproducibility of neuroimaging analyses and aggregation of heterogenous datasets are significant challenges in human subjects imaging research. This stems in part from a lack of an easy to use and universal data format that encompasses all steps of neuroimaging. The BIDS format has become widely adopted, however it is increasingly complex to implement as features are added, with the documentation now exceeding 500 pages.

Analysis of Longitudinal Change Patterns in Developing Brain Using Functional and Structural Magnetic Resonance Imaging via Multimodal Fusion.

Functional and structural magnetic resonance imaging (fMRI and sMRI) are complementary approaches that can be used to study longitudinal brain changes in adolescents. Each individual modality offers distinct insights into the brain. However each individual modality may overlook crucial aspects of brain analysis.

Cognitive interference elicits developmental sex differences in inhibitory control.

Inhibitory control is a key component of cognitive control that enables children and adolescents to develop increasingly complex skills throughout development. These processes are subject to insult via endogenous and environmental stressors (e.g.

Groupwise registration of infant brain diffusion tensor images using intermediate subgroup templates.

Registering infant brain images is challenging, as the infant brain undergoes rapid changes in size, shape and tissue contrast in the first months of life. Diffusion tensor images (DTI) have relatively consistent tissue properties over the course of infancy compared to commonly used T1 or T2-weighted images, presenting great potential for infant brain registration. Moreover, groupwise registration using intermediate templates can reduce deformation and bias introduced by predefined atlases, but most methods use scalar (e.