The Transition From Homogeneous to Heterogeneous Machine Learning in Neuropsychiatric Research.

Despite the advantage of neuroimaging-based machine learning (ML) models as pivotal tools for investigating brain-behavior relationships in neuropsychiatric studies, these data-driven predictive approaches have yet to yield substantial, clinically actionable insights for mental health care. A notable impediment lies in the inadequate accommodation of most ML research to the natural heterogeneity within large samples. Although commonly thought of as individual-level analyses, many ML algorithms are unimodal and homogeneous and thus incapable of capturing the potentially heterogeneous relationships between biology and psychopathology.

Predicting Task Activation Maps from Resting-State Functional Connectivity using Deep Learning.

Recent work has shown that deep learning is a powerful tool for predicting brain activation patterns evoked through various tasks using resting state features. We replicate and improve upon this recent work to introduce two models, BrainSERF and BrainSurfGCN, that perform at least as well as the state-of-the-art while greatly reducing memory and computational footprints. Our performance analysis observed that low predictability was associated with a possible lack of task engagement derived from behavioral performance.

Sex-specific differences in brain activity dynamics of youth with a family history of substance use disorder.

An individual's risk of substance use disorder (SUD) is shaped by a complex interplay of potent biosocial factors. Current neurodevelopmental models posit vulnerability to SUD in youth is due to an overreactive reward system and reduced inhibitory control. Having a family history of SUD is a particularly strong risk factor, yet few studies have explored its impact on brain function and structure prior to substance exposure.

Contributions of network structure, chemoarchitecture and diagnostic categories to transitions between cognitive topographies.

The mechanisms linking the brain's network structure to cognitively relevant activation patterns remain largely unknown. Here, by leveraging principles of network control, we show how the architecture of the human connectome shapes transitions between 123 experimentally defined cognitive activation maps (cognitive topographies) from the NeuroSynth meta-analytic database. Specifically, we systematically integrated large-scale multimodal neuroimaging data from functional magnetic resonance imaging, diffusion tractography, cortical morphometry and positron emission tomography to simulate how anatomically guided transitions between cognitive states can be reshaped by neurotransmitter engagement or by changes in cortical thickness.

Altered Structural Connectivity and Functional Brain Dynamics in Individuals With Heavy Alcohol Use Elucidated via Network Control Theory.

Background: Heavy alcohol use and its associated conditions, such as alcohol use disorder, impact millions of individuals worldwide. While our understanding of the neurobiological correlates of alcohol use has evolved substantially, we still lack models that incorporate whole-brain neuroanatomical, functional, and pharmacological information under one framework.

Methods: Here, we utilized diffusion and functional magnetic resonance imaging to investigate alterations to brain dynamics in 130 individuals with a high amount of current alcohol use.

Release the Krakencoder: A unified brain connectome translation and fusion tool.

Brain connectivity can be estimated in many ways, depending on modality and processing strategy. Here we present the Krakencoder, a joint connectome mapping tool that simultaneously, bidirectionally translates between structural (SC) and functional connectivity (FC), and across different atlases and processing choices via a common latent representation. These mappings demonstrate unprecedented accuracy and individual-level identifiability; the mapping between SC and FC has identifiability 42-54% higher than existing models.

Predicting Language Function Post-Stroke: A Model-Based Structural Connectivity Approach.

Background: The prediction of post-stroke language function is essential for the development of individualized treatment plans based on the personal recovery potential of aphasic stroke patients.

Objective: To establish a framework for integrating information on connectivity disruption of the language network based on routinely collected clinical magnetic resonance (MR) images into Random Forest modeling to predict post-stroke language function.

Methods: Language function was assessed in 76 stroke patients from the trial, using the Token Test (TT), Boston Naming Test (BNT), and Semantic Verbal Fluency (sVF) Test as primary outcome measures.

Controlling the human connectome with spatially diffuse input signals.

The human brain is never at "rest"; its activity is constantly fluctuating over time, transitioning from one brain state-a whole-brain pattern of activity-to another. Network control theory offers a framework for understanding the effort - energy - associated with these transitions. One branch of control theory that is especially useful in this context is "optimal control", in which input signals are used to selectively drive the brain into a target state.

Diffusion Tensor Imaging Along Perivascular Spaces (DTI-ALPS) to Assess Effects of Age, Sex, and Head Size on Interstitial Fluid Dynamics in Healthy Subjects.

Diffusion tensor imaging along perivascular spaces (DTI-ALPS) is a novel MRI method for assessing brain interstitial fluid dynamics, potentially indexing glymphatic function. Failed glymphatic clearance is implicated in Alzheimer's disease (AD) pathophysiology. We assessed the contribution of age and female sex (strong AD risk factors) to DTI-ALPS index in healthy subjects.

Infarct-related structural disconnection and delirium in surgical aortic valve replacement patients.

Objective: Although acute brain infarcts are common after surgical aortic valve replacement (SAVR), they are often unassociated with clinical stroke symptoms. The relationship between clinically "silent" infarcts and in-hospital delirium remains uncertain; obscured, in part, by how infarcts have been traditionally summarized as global metrics, independent of location or structural consequence. We sought to determine if infarct location and related structural connectivity changes were associated with postoperative delirium after SAVR.

The sequence of regional structural disconnectivity due to multiple sclerosis lesions.

Prediction of disease progression is challenging in multiple sclerosis as the sequence of lesion development and retention of inflammation within a subset of chronic lesions is heterogeneous among patients. We investigated the sequence of lesion-related regional structural disconnectivity across the spectrum of disability and cognitive impairment in multiple sclerosis. In a full cohort of 482 multiple sclerosis patients (age: 41.

Altered structural connectivity and functional brain dynamics in individuals with heavy alcohol use.

Heavy alcohol use and its associated conditions, such as alcohol use disorder (AUD), impact millions of individuals worldwide. While our understanding of the neurobiological correlates of AUD has evolved substantially, we still lack models incorporating whole-brain neuroanatomical, functional, and pharmacological information under one framework. Here, we utilize diffusion and functional magnetic resonance imaging to investigate alterations to brain dynamics in = 130 individuals with a high amount of current alcohol use.

Human brain responses are modulated when exposed to optimized natural images or synthetically generated images.

Understanding how human brains interpret and process information is important. Here, we investigated the selectivity and inter-individual differences in human brain responses to images via functional MRI. In our first experiment, we found that images predicted to achieve maximal activations using a group level encoding model evoke higher responses than images predicted to achieve average activations, and the activation gain is positively associated with the encoding model accuracy.

Association Between the Attention Network Test, Neuropsychological Measures, and Disability in Post-Acute Traumatic Brain Injury.

Cognitive impairment after traumatic brain injury (TBI) is persistent and disabling. Assessing cognitive function in a reliable and valid manner, using measures that are sensitive to the integrity of underlying neural substrates, is crucial in clinical research. The Attention Network Test (ANT) is one such assessment measure that has demonstrated associations with neural regions involved in attention; however, clinical utility of the ANT is limited because its relationship with neuropsychological measures of cognitive function (i.

Data-driven biomarkers outperform theory-based biomarkers in predicting stroke motor outcomes.

Chronic motor impairments are a leading cause of disability after stroke. Previous studies have predicted motor outcomes based on the degree of damage to predefined structures in the motor system, such as the corticospinal tract. However, such theory-based approaches may not take full advantage of the information contained in clinical imaging data.

Larger lesion volume in people with multiple sclerosis is associated with increased transition energies between brain states and decreased entropy of brain activity.

Quantifying the relationship between the brain's functional activity patterns and its structural backbone is crucial when relating the severity of brain pathology to disability in multiple sclerosis (MS). Network control theory (NCT) characterizes the brain's energetic landscape using the structural connectome and patterns of brain activity over time. We applied NCT to investigate brain-state dynamics and energy landscapes in controls and people with MS (pwMS).

Association of brain tissue cerebrospinal fluid fraction with age in healthy cognitively normal adults.

Background And Purpose: Our objective was to apply multi-compartment T2 relaxometry in cognitively normal individuals aged 20-80 years to study the effect of aging on the parenchymal CSF fraction (CSFF), a potential measure of the subvoxel CSF space.

Materials And Methods: A total of 60 volunteers (age range, 22-80 years) were enrolled. Voxel-wise maps of short-T2 myelin water fraction (MWF), intermediate-T2 intra/extra-cellular water fraction (IEWF), and long-T2 CSFF were obtained using fast acquisition with spiral trajectory and adiabatic T2prep (FAST-T2) sequence and three-pool non-linear least squares fitting.

Time-resolved network control analysis links reduced control energy under DMT with the serotonin 2a receptor, signal diversity, and subjective experience.

Psychedelics offer a profound window into the functioning of the human brain and mind through their robust acute effects on perception, subjective experience, and brain activity patterns. In recent work using a receptor-informed network control theory framework, we demonstrated that the serotonergic psychedelics lysergic acid diethylamide (LSD) and psilocybin flatten the brain's control energy landscape in a manner that covaries with more dynamic and entropic brain activity. Contrary to LSD and psilocybin, whose effects last for hours, the serotonergic psychedelic N,N-dimethyltryptamine (DMT) rapidly induces a profoundly immersive altered state of consciousness lasting less than 20 minutes, allowing for the entirety of the drug experience to be captured during a single resting-state fMRI scan.

Computational modelling in disorders of consciousness: Closing the gap towards personalised models for restoring consciousness.

Disorders of consciousness are complex conditions characterised by persistent loss of responsiveness due to brain injury. They present diagnostic challenges and limited options for treatment, and highlight the urgent need for a more thorough understanding of how human consciousness arises from coordinated neural activity. The increasing availability of multimodal neuroimaging data has given rise to a wide range of clinically- and scientifically-motivated modelling efforts, seeking to improve data-driven stratification of patients, to identify causal mechanisms for patient pathophysiology and loss of consciousness more broadly, and to develop simulations as a means of testing in silico potential treatment avenues to restore consciousness.

Comparisons of electrophysiological markers of impaired executive attention after traumatic brain injury and in healthy aging.

Executive attention impairments are a persistent and debilitating consequence of traumatic brain injury (TBI). To make headway towards treating and predicting outcomes following heterogeneous TBI, cognitive impairment specific pathophysiology first needs to be characterized. In a prospective observational study, we measured EEG during the attention network test aimed at detecting alerting, orienting, executive attention and processing speed.

Glymphatic clearance estimated using diffusion tensor imaging along perivascular spaces is reduced after traumatic brain injury and correlates with plasma neurofilament light, a biomarker of injury severity.

The glymphatic system is a perivascular fluid clearance system, most active during sleep, considered important for clearing the brain of waste products and toxins. Glymphatic failure is hypothesized to underlie brain protein deposition in neurodegenerative disorders like Alzheimer's disease. Preclinical evidence suggests that a functioning glymphatic system is also essential for recovery from traumatic brain injury, which involves release of debris and toxic proteins that need to be cleared from the brain.