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The study performed on a large, heterogeneous and multisite sample of youth with anxiety disorders reveals that only modest classification performances can be realistically achieved with machine learning using neuroanatomical data.
The authors interrogate patterns of symptom-linked resting-state functional connectivity measured with electroencephalography with machine learning techniques to identify two dimensions associated with specific regions, the left angular gyrus and the right middle temporal gyrus and social and communication deficits, and the right inferior parietal lobe with restricted and repetitive disorders, which may serve as biomarkers in autism.
This longitudinal study of young people with parents with bipolar disorder assessed genetic and symptomatic risks associated with brain surface features, identifying reduced baseline frontotemporal cortical thickness in individuals who developed mood disorders at follow-up.
The authors conducted a systematic review and individual participant data meta-analysis of 14 studies to evaluate psychological interventions for depression relapse prevention.
Using a multivariate approach in a sample from the UK Biobank, the authors examine the genetic relationships between measures of functional connectivity and psychiatric disorders.
Analyzing data on households without broadband internet access, level of urbanization, and mental healthcare services, the authors demonstrate that those counties with reduced broadband access are more likely to be under-resourced for mental health practitioners and facilities.
Modeling neurodevelopmental trajectories in a pediatric cohort reveals accelerated decrease in correlation between brain structural and functional connectivity in children exposed to high levels of prenatal adversity.
Using data from the UK Biobank, the authors studied the effect of body mass index, change in body mass index, and metabolic status in people with obesity on the prevalence of neurological and neuropsychiatric disorders.
Investigators present findings from a double-blind randomized controlled trial of personalized stimulation of the left dorsolateral prefrontal cortex using transcranial magnetic stimulation to increase hypnotizability in a sample of patients with fibromyalgia syndrome.
Instructions about nicotine strength influence smokers’ perception of the nicotine content in electronic cigarettes and are associated with neural responses in the thalamocortical system.
The study reports the application of data-driven techniques to structural neuroimaging data of people with recent-onset psychosis or recent-onset depression and matched controls for the prediction of transdiagnostic clinical symptom severity and clinical outcomes.
Fittipaldi et al. examined how heterogeneity affects social cognition in populations over 50 years old by using advanced neuroimaging techniques and social cognition assessments in a diverse sample of over 1,000 adults across nine countries.
Chiba & Ide et al. used the data from a Japanese longitudinal cohort study to investigate the suicide risk in the general population before and after the COVID-19 pandemic.
A neuroimaging study using repeated transcranial magnetic stimulation on unmedicated patients with depression reveals a link between response in the subgenual anterior cingulate cortex and clinical improvement.
Using two series of online experiments involving a public goods game and manipulation of wealth visibility, the authors show that invisible wealth achieved higher subjective well-being, particularly among poorer subjects leading to a reduction in the economic gradient in well-being.
In this randomized, crossover study, the authors applied high-frequency repetitive transcranial magnetic stimulation to the dorsolateral prefrontal cortex or to the visual cortex (V5) in patients with tobacco use disorder to elucidate the underlying cognitive and affective mechanisms of repetitive transcranial magnetic stimulation for smoking cessation.
Voetterl and colleagues examined the application of ‘Brainmarker-I’, an age- and sex-normalized electroencephalogram measure of individual alpha peak frequency, on predicting remission to therapeutic noninvasive neuromodulation protocols (repetitive transcranial magnetic stimulation and electroconvulsive therapy).
Hansen et al. investigate the applicability of machine learning models in identifying diagnosis-specific speech or text-based markers of neuropsychiatric disorders.
Analysis of brain functional connectivity cross-scan stability in a large-scale Adolescent Brain Cognitive Development dataset reveals that functional network connectivity can identify a given child from a large group with high accuracy and that variations across scans are associated with a range of health measures.
By developing the large-scale cortical spatial–rhythmic network analysis, authors identify putative electrophysiological biomarkers for clinical screening of individuals at high familial risk of bipolar disorder onset.