Individual characteristics outperform resting-state fMRI for the prediction of behavioral phenotypes.

In this study, we aimed to compare imaging-based features of brain function, measured by resting-state fMRI (rsfMRI), with individual characteristics such as age, gender, and total intracranial volume to predict behavioral measures. We developed a machine learning framework based on rsfMRI features in a dataset of 20,000 healthy individuals from the UK Biobank, focusing on temporal complexity and functional connectivity measures. Our analysis across four behavioral phenotypes revealed that both temporal complexity and functional connectivity measures provide comparable predictive performance. However, individual characteristics consistently outperformed rsfMRI features in predictive accuracy, particularly in analyses involving smaller sample sizes. Integrating rsfMRI features with demographic data sometimes enhanced predictive outcomes. The efficacy of different predictive modeling techniques and the choice of brain parcellation atlas were also examined, showing no significant influence on the results. To summarize, while individual characteristics are superior to rsfMRI in predicting behavioral phenotypes, rsfMRI still conveys additional predictive value in the context of machine learning, such as investigating the role of specific brain regions in behavioral phenotypes.

Resting-State Changes in Aging and Parkinson's Disease Are Shaped by Underlying Neurotransmission: A Normative Modeling Study.

BACKGROUND: Human healthy and pathological aging is linked to a steady decline in brain resting-state activity and connectivity measures. The neurophysiological mechanisms that underlie these changes remain poorly understood. METHODS: Making use of recent developments in normative modeling and availability of in vivo maps for various neurochemical systems, we tested in the UK Biobank cohort (n = 25,917) whether and how age- and Parkinson's disease-related resting-state changes in commonly applied local and global activity and connectivity measures colocalize with underlying neurotransmitter systems. RESULTS: We found that the distributions of several major neurotransmitter systems including serotonergic, dopaminergic, noradrenergic, and glutamatergic neurotransmission correlated with age-related changes across functional activity and connectivity measures. Colocalization patterns in Parkinson's disease deviated from normative aging trajectories for these, as well as for cholinergic and GABAergic (gamma-aminobutyric acidergic) neurotransmission. The deviation from normal colocalization of brain function and GABAA correlated with disease duration. CONCLUSIONS: These findings provide new insights into molecular mechanisms underlying age- and Parkinson's-related brain functional changes by extending the existing evidence elucidating the vulnerability of specific neurochemical attributes to normal aging and Parkinson's disease. The results particularly indicate that alongside dopamine and serotonin, increased vulnerability of glutamatergic, cholinergic, and GABAergic systems may also contribute to Parkinson's disease-related functional alterations. Combining normative modeling and neurotransmitter mapping may aid future research and drug development through deeper understanding of neurophysiological mechanisms that underlie specific clinical conditions.

Lifetime Exposure to Depression and Neuroimaging Measures of Brain Structure and Function.

Importance: Despite decades of neuroimaging studies reporting brain structural and functional alterations in depression, discrepancies in findings across studies and limited convergence across meta-analyses have raised questions about the consistency and robustness of the observed brain phenotypes. Objective: To investigate the associations between 6 operational criteria of lifetime exposure to depression and functional and structural neuroimaging measures. Design, Setting, and Participants: This cross-sectional study analyzed data from a UK Biobank cohort of individuals aged 45 to 80 years who were enrolled between January 1, 2014, and December 31, 2018. Participants included individuals with a lifetime exposure to depression and matched healthy controls without indications of psychosis, mental illness, behavior disorder, and disease of the nervous system. Six operational criteria of lifetime exposure to depression were evaluated: help seeking for depression; self-reported depression; antidepressant use; depression definition by Smith et al; hospital International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10) diagnosis codes F32 and F33; and Composite International Diagnostic Interview Short Form score. Six increasingly restrictive depression definitions and groups were defined based on the 6 depression criteria, ranging from meeting only 1 criterion to meeting all 6 criteria. Data were analyzed between January and October 2022. Main Outcomes and Measures: Functional measures were calculated using voxel-wise fractional amplitude of low-frequency fluctuation (fALFF), global correlation (GCOR), and local correlation (LCOR). Structural measures were calculated using gray matter volume (GMV). Results: The study included 20 484 individuals with lifetime depression (12 645 females [61.7%]; mean [SD] age, 63.91 [7.60] years) and 25 462 healthy controls (14 078 males [55.3%]; mean [SD] age, 65.05 [7.8] years). Across all depression criteria, individuals with lifetime depression displayed regionally consistent decreases in fALFF, LCOR, and GCOR (Cohen d range, -0.53 [95% CI, -0.88 to -0.15] to -0.04 [95% CI, -0.07 to -0.01]) but not in GMV (Cohen d range, -0.47 [95 % CI, -0.75 to -0.12] to 0.26 [95% CI, 0.15-0.37]). Hospital ICD-10 diagnosis codes F32 and F33 (median [IQR] difference in effect sizes, -0.14 [-0.17 to -0.11]) and antidepressant use (median [IQR] difference in effect sizes, -0.12 [-0.16 to -0.10]) were criteria associated with the most pronounced alterations. Conclusions and Relevance: Results of this cross-sectional study indicate that lifetime exposure to depression was associated with robust functional changes, with a more restrictive depression definition revealing more pronounced alterations. Different inclusion criteria for depression may be associated with the substantial variation in imaging findings reported in the literature.

Local synchronicity in dopamine-rich caudate nucleus influences Huntington's disease motor phenotype.

Structural grey and white matter changes precede the manifestation of clinical signs of Huntington's disease by many years. Conversion to clinically manifest disease therefore likely reflects not merely atrophy but a more widespread breakdown of brain function. Here, we investigated the structure-function relationship close to and after clinical onset, in important regional brain hubs, particularly caudate nucleus and putamen, which are central to maintaining normal motor behaviour. In two independent cohorts of patients with premanifest Huntington's disease close to onset and very early manifest Huntington's disease (total n = 84; n = 88 matched controls), we used structural and resting state functional MRI. We show that measures of functional activity and local synchronicity within cortical and subcortical regions remain normal in the premanifest Huntington's disease phase despite clear evidence of brain atrophy. In manifest Huntington's disease, homeostasis of synchronicity was disrupted in subcortical hub regions such as caudate nucleus and putamen, but also in cortical hub regions, for instance the parietal lobe. Cross-modal spatial correlations of functional MRI data with receptor/neurotransmitter distribution maps showed that Huntington's disease-specific alterations co-localize with dopamine receptors D1 and D2, as well as dopamine and serotonin transporters. Caudate nucleus synchronicity significantly improved models predicting the severity of the motor phenotype or predicting the classification into premanifest Huntington's disease or motor manifest Huntington's disease. Our data suggest that the functional integrity of the dopamine receptor-rich caudate nucleus is key to maintaining network function. The loss of caudate nucleus functional integrity affects network function to a degree that causes a clinical phenotype. These insights into what happens in Huntington's disease could serve as a model for what might be a more general relationship between brain structure and function in neurodegenerative diseases in which other brain regions are vulnerable.

The asteroid and comet impact hazard: risk assessment and mitigation options.

The impact of extraterrestrial matter onto Earth is a continuous process. On average, some 50,000 tons of dust are delivered to our planet every year. While objects smaller than about 30 m mainly disintegrate in the Earth's atmosphere, larger ones can penetrate through it and cause damage on the ground. When an object of hundreds of meters in diameter impacts an ocean, a tsunami is created that can devastate coastal cities. Further, if a km-sized object hit the Earth it would cause a global catastrophe due to the transport of enormous amounts of dust and vapour into the atmosphere resulting in a change in the Earth's climate. This article gives an overview of the near-Earth asteroid and comet (near-Earth object-NEO) impact hazard and the NEO search programmes which are gathering important data on these objects. It also points out options for impact hazard mitigation by using deflection systems. It further discusses the critical constraints for NEO deflection strategies and systems as well as mitigation and evacuation costs and benefits. Recommendations are given for future activities to solve the NEO impact hazard problem.