The relationship between electric field strength induced by electroconvulsive therapy and cognitive and antidepressant outcomes
Related Articles
Mapping computational cognitive profiles of aging to dissociable brain and sociodemographic factors
Aging is associated with declines in cognition and brain structural integrity. However, there is equivocality over (1) the specificity of affected domains in different people, (2) the location of associated patterns of brain structural deterioration, and (3) the sociodemographic factors contributing to ‘unhealthy’ cognition. We aimed to identify cognitive profiles displayed by older adults and determine brain and sociodemographic features potentially shaping these profiles. A sample of Southeast-Asian older adults (N = 386) participated in a multi-session study comprising cognitive testing, neuroimaging, and a structured interview. We used computational models to extract latent mechanisms underlying cognitive flexibility and response inhibition. Data-driven methods were used to construct cognitive profiles based on standard performance measures and model parameters. We also investigated grey matter volume and machine-learning derived ‘brain-ages’. A profile associated with poor set-shifting and rigid focusing was associated with widespread grey matter reduction in cognitive control regions. A slow responding profile was associated with advanced brain-age. Both profiles were correlated with poor socioeconomic standing and cognitive reserve. We found that the impact of sociodemographic factors on cognitive profiles was partially mediated by total grey and white matter, and dorsolateral prefrontal and cerebellar volumes. This study furthers understanding of how distinct aging profiles of cognitive impairment uniquely correspond to specific vs. global brain deterioration and the significance of socioeconomic factors in informing cognitive performance in older age.
Boredom signals deviation from a cognitive homeostatic set point
Boredom is the feeling of wanting but failing to engage the mind and can be conceived as one among many signals of suboptimal utilization of cognitive and neural resources. Using homeostasis as an analogy, this perspective argues that boredom represents a signal indicating deviation from optimal engagement—that is, deviation from a cognitive homeostatic set point. Within this model, allostasis accounts for chronic boredom (i.e., trait boredom proneness), according to which faulty internal models are responsible for why the highly boredom prone may set unrealistic expectations for engagement. In other words, the model characterizes boredom as a dynamic response to both internal and external exigencies, leading to testable hypotheses for both the nature of the state and the trait disposition. Furthermore, this perspective presents the broader notion that humans strive to optimally engage with their environs to maintain a kind of cognitive homeostatic set-point.
Smartwatch- and smartphone-based remote assessment of brain health and detection of mild cognitive impairment
Consumer-grade mobile devices are used by billions worldwide. Their ubiquity provides opportunities to robustly capture everyday cognition. ‘Intuition’ was a remote observational study that enrolled 23,004 US adults, collecting 24 months of longitudinal multimodal data via their iPhones and Apple Watches using a custom research application that captured routine device use, self-reported health information and cognitive assessments. The study objectives were to classify mild cognitive impairment (MCI), characterize cognitive trajectories and develop tools to detect and track cognitive health at scale. The study addresses sources of bias in current cognitive health research, including limited representativeness (for example, racial/ethnic, geographic) and accuracy of cognitive measurement tools. We describe study design and provide baseline cohort characteristics. Next, we present foundational proof-of-concept MCI classification modeling results using interactive cognitive assessment data. Initial findings support the reliability and validity of remote MCI detection and the usefulness of such data in describing at-risk cognitive health trajectories in demographically diverse aging populations. ClinicalTrials.gov identifier: NCT05058950.
Cognitive reserve is associated with education, social determinants, and cognitive outcomes among older American Indians in the Strong Heart Study
Cognitive reserve, a component of resilience, may be conceptualized as the ability to overcome accumulating neuropathology and maintain healthy aging and function. However, research measuring and evaluating it in American Indians is needed. We recruited American Indians from 3 regional centers for longitudinal examinations (2010-13, n = 818; 2017-19, n = 403) including MRI, cognitive, clinical, and questionnaire data. We defined cognitive reserve by measuring the residual from individual regressions of cognitive tests over imaged brain volumes, adjusted for age and sex. Analyses examined three different metrics of cognitive reserve against sociodemographic, clinical, and longitudinal cognitive data in causal mediation models. Better cognitive reserve was significantly associated with more education, higher income, lower prevalence of depression, lower prevalence of diabetes, and lower prevalence of kidney disease, but we found no statistically significant evidence for an association with plasma biomarkers for Alzheimer’s disease and related dementias, APOE e4 carrier status, alcohol use, body mass, or hypertension. Better cognitive reserve was associated with better cognitive function over mean 6.7 years follow-up (range 4-9 years); and the association for education with cognition over time was mediated in part (15-24%) by cognitive reserve. Cognitive reserve, although challenging to measure, appears important for understanding the range of cognitive aging in American Indians.
Visible-to-THz near-field nanoscopy
Optical microscopy has a key role in research, development and quality control across a wide range of scientific, technological and medical fields. However, diffraction limits the spatial resolution of conventional optical instruments to about half the illumination wavelength. A technique that surpasses the diffraction limit in the wide spectral range between visible and terahertz frequencies is scattering-type scanning near-field optical microscopy (s-SNOM). The basis of s-SNOM is an atomic force microscope in which the tip is illuminated with light from the visible to the terahertz spectral range. By recording the elastically tip-scattered light while scanning the sample below the tip, s-SNOM yields near-field optical images with a remarkable resolution of 10 nm, simultaneously with the standard atomic force microscopic topography image. This resolution is independent of the illumination wavelength, rendering s-SNOM a versatile nanoimaging and nanospectroscopy technique for fundamental and applied studies of materials, structures and phenomena. This Review presents an overview of the fundamental principles governing the measurement and interpretation of near-field contrasts and discusses key applications of s-SNOM. We also showcase emerging developments that enable s-SNOM to operate under various environmental conditions, including cryogenic temperatures, electric and magnetic fields, electrical currents, strain and liquid environments. All these recent developments broaden the applicability of s-SNOMs for exploring fundamental solid-state and quantum phenomena, biological matter, catalytic reactions and more.
Responses