Age-related differences in finger interdependence during complex hand movements.

The well-known decrease in finger dexterity during healthy aging leads to a significant reduction in quality of life. Still, the exact patterns of altered finger kinematics of older adults in daily life are fairly unexplored. Finger interdependence is the unintentional comovement of fingers that are not intended to move, and it is known to vary across the lifespan. Nevertheless, the magnitude and direction of age-related differences in finger interdependence are ambiguous across studies and tasks and have not been explored in the context of daily life finger movements. We investigated five different free and daily-life-inspired finger movements of the right, dominant hand as well as a sequential finger tapping task of the thumb against the other fingers, in 17 younger (22-37 yr) and 17 older (62-80 yr) adults using an exoskeleton data glove for data recording. Using inferential statistics, we found that the unintentional comovement of fingers generally decreases with age in all performed daily-life-inspired movements. Finger tapping, however, showed a trend towards higher finger interdependence for older compared with younger adults. Using machine learning, we predicted the age group of a person from finger interdependence features of single movement trials significantly better than chance level for the daily-life-inspired movements, but not for finger tapping. Taken together, we show that for specific tasks, decreased finger interdependence (i.e., less comovement) could potentially act as a marker of human aging that specifically characterizes older adults' complex finger movements in daily life.NEW & NOTEWORTHY Kinematic finger movement data were analyzed with regard to age-related differences. Extensive analyses of complex and daily-life-inspired movements reveal that the direction of age effects is not uniform but task-dependent: Although older adults generally show more finger interdependence than younger adults in a simple finger tapping task, this effect is reversed for daily-life-inspired movement tasks. For these tasks, finger interdependence indices offer potential new markers to predict the age group of an individual using machine learning approaches.

Reduced dimension stimulus decoding and column-based modeling reveal architectural differences of primary somatosensory finger maps between younger and older adults.

The primary somatosensory cortex (SI) contains fine-grained tactile representations of the body, arranged in an orderly fashion. The use of ultra-high resolution fMRI data to detect group differences, for example between younger and older adults' SI maps, is challenging, because group alignment often does not preserve the high spatial detail of the data. Here, we use robust-shared response modeling (rSRM) that allows group analyses by mapping individual stimulus-driven responses to a lower dimensional shared feature space, to detect age-related differences in tactile representations between younger and older adults using 7T-fMRI data. Using this method, we show that finger representations are more precise in Brodmann-Area (BA) 3b and BA1 compared to BA2 and motor areas, and that this hierarchical processing is preserved across age groups. By combining rSRM with column-based decoding (C-SRM), we further show that the number of columns that optimally describes finger maps in SI is higher in younger compared to older adults in BA1, indicating a greater columnar size in older adults' SI. Taken together, we conclude that rSRM is suitable for finding fine-grained group differences in ultra-high resolution fMRI data, and we provide first evidence that the columnar architecture in SI changes with increasing age.

Distribution Pattern of Closed and Extended Forms of Glutathione in the Human Brain: MR Spectroscopic Study.

Glutathione (GSH) is a potent antioxidant synthesized de novo in cells and helps to detoxify free radicals in the brain and other organs. In vitro NMR studies from various research groups have reported primarily two sets of chemical shifts (2.80 or 2.96 ppm) of Cys-ßCH2 depending on GSH sample preparation in either inert or oxygenated environments. A multi-center in vivo MRS human study has also validated the presence of two types of GSH conformer in the human brain. Our study is aimed at investigating the distribution patterns of the two GSH conformers from five brain regions, namely, ACC (anterior cingulate cortex), PCC (posterior cingulate cortex), LPC (left parietal cortex), LH (left hippocampus), and CER (cerebellum). GSH was measured using a 3T MRI scanner using MEGA-PRESS pulse sequence in healthy young male and female populations (M/F = 5/9; age 32.8 ± 5.27 years). We conclude that the closed GSH conformer (characteristic NMR shift signature: Cys Hα 4.40-Hß 2.80 ppm) is more abundant than the extended GSH form (characteristic NMR shift signature Cys Hα 4.56-Hß 2.95 ppm). Closed conformer has a non-uniform distribution (ACC < CER < LH < PCC < LPC) in the healthy brain. On the contrary, the extended form of GSH has a uniform distribution in various anatomical regions.

Discriminating Free Hand Movements Using Support Vector Machine and Recurrent Neural Network Algorithms.

Decoding natural hand movements is of interest for human-computer interaction and may constitute a helpful tool in the diagnosis of motor diseases and rehabilitation monitoring. However, the accurate measurement of complex hand movements and the decoding of dynamic movement data remains challenging. Here, we introduce two algorithms, one based on support vector machine (SVM) classification combined with dynamic time warping, and the other based on a long short-term memory (LSTM) neural network, which were designed to discriminate small differences in defined sequences of hand movements. We recorded hand movement data from 17 younger and 17 older adults using an exoskeletal data glove while they were performing six different movement tasks. Accuracy rates in decoding the different movement types were similarly high for SVM and LSTM in across-subject classification, but, for within-subject classification, SVM outperformed LSTM. The SVM-based approach, therefore, appears particularly promising for the development of movement decoding tools, in particular if the goal is to generalize across age groups, for example for detecting specific motor disorders or tracking their progress over time.