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Aim: An observational longitudinal study to evaluate the feasibility of assessing cognitive, neuropsychological and emotional-behavioural functioning in children with myotonic dystrophy type 1 (DM1), and to estimate prospectively changes in functioning over time.
Method: Ten DM1 patients, aged 1.5-16 years (mean 9.1), 5 with congenital DM1, and 5 with childhood DM1, were assessed with standardized measures of intellectual, neuropsychological, and emotional-behavioural functioning. For 6 patients, assessments were repeated 2 years later.
Results: At baseline, intellectual disability was found both in the congenital and the childhood group. A clear-cut reduction of the mean and individual developmental/intelligence quotient after 2 years was demonstrated in re-tested patients. As regards to the neuropsychological aspects, the baseline evaluation identified impairments in visuospatial skills and attentional functions, with no clear trend observed after two years. In executive functions, no significant profile was identified even though impairments were detected in a few patients. At the emotional-behavioural assessment, scores in clinical range were found, but they remained heterogeneous and no trends could be recognized.
Conclusion: Several aspects of CNS functions in DM1 children deserve better definition and a longitudinal assessment. A comprehensive protocol should include cognitive, neuropsychological, emotional and behavioural assessment but larger longitudinal studies are needed to better evaluate the trajectories over time and inform practice.
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http://dx.doi.org/10.1016/j.ejpn.2022.05.008 | DOI Listing |
Photobiomodul Photomed Laser Surg
September 2025
Taleghani Hospital Clinical Research Development Unit, Department of Psychiatry, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
There is strong evidence supporting the effectiveness of photobiomodulation therapy (PBMT) in improving neuronal damage and enhancing neuropsychological activities. However, there is limited research on the effects of this method on cognitive function and mood disorders. This project aimed to evaluate the potential benefits of PBMT in improving cognitive status and mood disorders in patients with dementia.
View Article and Find Full Text PDFBrain Behav Immun
September 2025
Department of Public Health Science, Graduate School and Transdisciplinary Major in Learning Health Systems, Graduate School, Korea University, Seoul, South Korea. Electronic address:
Background: Immune dysregulation and metabolic disturbances contribute to cognitive decline in aging populations. The neutrophil-to-HDL cholesterol ratio (NHR), an emerging immunometabolic biomarker, reflects systemic inflammation and vascular dysfunction. However, its role in predicting cognitive impairment in older adults remains unclear.
View Article and Find Full Text PDFNeurology
October 2025
Department of Radiology, Mayo Clinic, Rochester, MN.
Background And Objectives: The relationship between insomnia and cognitive decline is poorly understood. We investigated associations between chronic insomnia, longitudinal cognitive outcomes, and brain health in older adults.
Methods: From the population-based Mayo Clinic Study of Aging, we identified cognitively unimpaired older adults with or without a diagnosis of chronic insomnia who underwent annual neuropsychological assessments (z-scored global cognitive scores and cognitive status) and had quantified serial imaging outcomes (amyloid-PET burden [centiloid] and white matter hyperintensities from MRI [WMH, % of intracranial volume]).
Gerontologist
September 2025
Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, 60612United States.
Background And Objectives: Cognition may be influenced by health-related factors such as blood pressure (BP). However, variations in BP may differentially affect cognition across race. This study investigates BP and cognitive decline in older Black and White adults.
View Article and Find Full Text PDFAnn Acad Med Singap
August 2025
Dementia Research Centre (Singapore), Lee Kong Chian School of Medicine, Nanyang Technology University, Singapore.
Introduction: Interpretation and analysis of magnetic resonance imaging (MRI) scans in clinical settings comprise time-consuming visual ratings and complex neuroimage processing that require trained professionals. To combat these challenges, artificial intelligence (AI) techniques can aid clinicians in interpreting brain MRI for accurate diagnosis of neurodegenerative diseases but they require extensive validation. Thus, the aim of this study was to validate the use of AI-based AQUA (Neurophet Inc.
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