How measurement noise limits the accuracy of brain-behaviour predictions.

Major efforts in human neuroimaging strive to understand individual differences and find biomarkers for clinical applications by predicting behavioural phenotypes from brain imaging data. To identify generalisable and replicable brain-behaviour prediction models, sufficient measurement reliability is essential. However, the selection of prediction targets is predominantly guided by scientific interest or data availability rather than psychometric considerations.

Resting fMRI-guided TMS evokes subgenual anterior cingulate response in depression.

Background: Depression alleviation following treatment with repetitive transcranial magnetic stimulation (rTMS) tends to be more effective when TMS is targeted to cortical areas with high (negative) resting state functional connectivity (rsFC) with the subgenual anterior cingulate cortex (sgACC). However, the relationship between sgACC-cortex rsFC and the TMS-evoked response in the sgACC is still being explored and has not yet been established in depressed patients.

Objectives: In this study, we investigated the relationship between sgACC-cortical (site of stimulation) rsFC and induced evoked responses in the sgACC in healthy controls and depressed patients.

Study design features increase replicability in brain-wide association studies.

Brain-wide association studies (BWAS) are a fundamental tool in discovering brain-behaviour associations. Several recent studies have shown that thousands of study participants are required for good replicability of BWAS. Here we performed analyses and meta-analyses of a robust effect size index using 63 longitudinal and cross-sectional MRI studies from the Lifespan Brain Chart Consortium (77,695 total scans) to demonstrate that optimizing study design is critical for increasing standardized effect sizes and replicability in BWAS.

Investigating Mood and Cognition in People with Multiple Sclerosis: A Prospective Study Protocol.

Multiple sclerosis (MS) is an immune-mediated neurological disorder that affects one million people in the United States. Up to 50% of people with MS experience depression, yet the mechanisms of depression in MS remain under-investigated. Studies of medically healthy participants with depression have described associations between white matter variability and depressive symptoms, but frequently exclude participants with medical comorbidities and thus cannot be extrapolated to people with intracranial diseases.

Association of Anxiety with Uncinate Fasciculus Lesion Burden in Multiple Sclerosis.

Importance: Multiple sclerosis (MS) is an immune-mediated neurological disorder that affects 2.4 million people world-wide, and up to 60% experience anxiety.

Objective: We investigated how anxiety in MS is associated with white matter lesion burden in the uncinate fasciculus (UF).

Changes in brain connectivity and neurovascular dynamics during dexmedetomidine-induced loss of consciousness.

Understanding the neurophysiological changes that occur during loss and recovery of consciousness is a fundamental aim in neuroscience and has marked clinical relevance. Here, we utilize multimodal magnetic resonance neuroimaging to investigate changes in regional network connectivity and neurovascular dynamics as the brain transitions from wakefulness to dexmedetomidine-induced unconsciousness, and finally into early-stage recovery of consciousness. We observed widespread decreases in functional connectivity strength across the whole brain, and targeted increases in structure-function coupling (SFC) across select networks-especially the cerebellum-as individuals transitioned from wakefulness to hypnosis.

Impact of insurance status on MRI phenotypes in MS.

Background: Health insurance in the United States varies in coverage of essential diagnostic tests, therapies, and specialists. Health disparities between privately and publicly insured patients with MS have not been comprehensively assessed. The objective of this study is to evaluate the impact of public versus private insurance on longitudinal brain outcomes in MS.

Quantifying brain development in the HEALthy Brain and Child Development (HBCD) Study: The magnetic resonance imaging and spectroscopy protocol.

The HEALthy Brain and Child Development (HBCD) Study, a multi-site prospective longitudinal cohort study, will examine human brain, cognitive, behavioral, social, and emotional development beginning prenatally and planned through early childhood. The acquisition of multimodal magnetic resonance-based brain development data is central to the study's core protocol. However, application of Magnetic Resonance Imaging (MRI) methods in this population is complicated by technical challenges and difficulties of imaging in early life.

Symptom dimensions of resting-state electroencephalographic functional connectivity in autism.

Autism spectrum disorder (ASD) is a common neurodevelopmental disorder characterized by social and communication deficits (SCDs), restricted and repetitive behaviors (RRBs) and fixated interests. Despite its prevalence, development of effective therapy for ASD is hindered by its symptomatic and neurophysiological heterogeneities. To comprehensively explore these heterogeneities, we developed a new analytical framework combining contrastive learning and sparse canonical correlation analysis that identifies symptom-linked resting-state electroencephalographic connectivity dimensions within 392 ASD samples.

Regression and alignment for functional data and network topology.

In the brain, functional connections form a network whose topological organization can be described by graph-theoretic network diagnostics. These include characterizations of the community structure, such as modularity and participation coefficient, which have been shown to change over the course of childhood and adolescence. To investigate if such changes in the functional network are associated with changes in cognitive performance during development, network studies often rely on an arbitrary choice of preprocessing parameters, in particular the proportional threshold of network edges.

Moving beyond processing- and analysis-related variation in resting-state functional brain imaging.

When fields lack consensus standard methods and accessible ground truths, reproducibility can be more of an ideal than a reality. Such has been the case for functional neuroimaging, where there exists a sprawling space of tools and processing pipelines. We provide a critical evaluation of the impact of differences across five independently developed minimal preprocessing pipelines for functional magnetic resonance imaging.

A network control theory pipeline for studying the dynamics of the structural connectome.

Network control theory (NCT) is a simple and powerful tool for studying how network topology informs and constrains the dynamics of a system. Compared to other structure-function coupling approaches, the strength of NCT lies in its capacity to predict the patterns of external control signals that may alter the dynamics of a system in a desired way. An interesting development for NCT in the neuroscience field is its application to study behavior and mental health symptoms.

Brain-age prediction: Systematic evaluation of site effects, and sample age range and size.

Structural neuroimaging data have been used to compute an estimate of the biological age of the brain (brain-age) which has been associated with other biologically and behaviorally meaningful measures of brain development and aging. The ongoing research interest in brain-age has highlighted the need for robust and publicly available brain-age models pre-trained on data from large samples of healthy individuals. To address this need we have previously released a developmental brain-age model.

A sensorimotor-association axis of thalamocortical connection development.

Human cortical development follows a sensorimotor-to-association sequence during childhood and adolescence. The brain's capacity to enact this sequence over decades indicates that it relies on intrinsic mechanisms to regulate inter-regional differences in the timing of cortical maturation, yet regulators of human developmental chronology are not well understood. Given evidence from animal models that thalamic axons modulate windows of cortical plasticity, here we evaluate the overarching hypothesis that structural connections between the thalamus and cortex help to coordinate cortical maturational heterochronicity during youth.

Network enrichment significance testing in brain-phenotype association studies.

Functional networks often guide our interpretation of spatial maps of brain-phenotype associations. However, methods for assessing enrichment of associations within networks of interest have varied in terms of both scientific rigor and underlying assumptions. While some approaches have relied on subjective interpretations, others have made unrealistic assumptions about spatial properties of imaging data, leading to inflated false positive rates.

Deciphering the functional specialization of whole-brain spatiomolecular gradients in the adult brain.

Cortical arealization arises during neurodevelopment from the confluence of molecular gradients representing patterned expression of morphogens and transcription factors. However, whether similar gradients are maintained in the adult brain remains unknown. Here, we uncover three axes of topographic variation in gene expression in the adult human brain that specifically capture previously identified rostral-caudal, dorsal-ventral, and medial-lateral axes of early developmental patterning.

In vivo whole-cortex marker of excitation-inhibition ratio indexes cortical maturation and cognitive ability in youth.

A balanced excitation-inhibition ratio (E/I ratio) is critical for healthy brain function. Normative development of cortex-wide E/I ratio remains unknown. Here, we noninvasively estimate a putative marker of whole-cortex E/I ratio by fitting a large-scale biophysically plausible circuit model to resting-state functional MRI (fMRI) data.

Net: A toolbox for personalized functional networks modeling.

Personalized functional networks (FNs) derived from functional magnetic resonance imaging (fMRI) data are useful for characterizing individual variations in the brain functional topography associated with the brain development, aging, and disorders. To facilitate applications of the personalized FNs with enhanced reliability and reproducibility, we develop an open-source toolbox that is user-friendly, extendable, and includes rigorous quality control (QC), featuring multiple user interfaces (graphics, command line, and a step-by-step guideline) and job-scheduling for high performance computing (HPC) clusters. Particularly, the toolbox, named personalized functional network modeling (pNet), takes fMRI inputs in either volumetric or surface type, ensuring compatibility with multiple fMRI data formats, and computes personalized FNs using two distinct modeling methods: one method optimizes the functional coherence of FNs, while the other enhances their independence.