Friday 9th of May 15.00 – 16.40
Aula Scarpa
Chair of the session – Rocco Palumbo
| 15.00 – 15.20 | Zoya Moraj | A functional future for the cognitive neuroscience of human aging. |
| 15.20 – 15.40 | Christiane Jockwitz | Prediction of individual cognitive test scores from brain and non-brain data across the adults lifespan. |
| 15.40 – 16.00 | Camilla Mendl-Heinisch | Prediction of individual cognitive test performance based on imaging and non-imaging data in older adults. |
| 16.00 – 16.20 | Natalia Zhukova | Reduced dynamic functional connectivity in higher ages: are older brains less adaptable? |
Abstracts
A functional future for the cognitive neuroscience of human aging.
by Zoya Mooraj1 | Alireza Salami 2, 3, 4 | Karen L. Campbell5 | Martin J. Dahl1, 6, 7 | Julian Q. Kosciessa8 | Matthew R. Nassar9, 10 | Markus Werkle-Bergner1 | Fergus I. M. Craik11 | Ulman Lindenberger1, 6 | Ulrich Mayr12 | M. Natasha Rajah13, 14 | Naftali Raz15 | Lars Nyberg2, 16, 17 | Douglas D. Garrett1, 6
1 Center for Lifespan Psychology, Max Planck Institute for Human Development
2 Aging Research Center, Karolinska Institutet & Stockholm University
3 Umeå Center for Functional Brain Imaging (UFBI), Umeå University
4 Wallenberg Center for Molecular Medicine, Umeå University
5 Department of Psychology, Brock University
6 Max Planck UCL Centre for Computational Psychiatry and Ageing Research
7 Leonard Davis School of Gerontology, University of Southern California
8 Radboud University, Donders Institute for Brain, Cognition and Behaviour
9 Robert J. & Nancy D. Carney Institute for Brain Science, Brown University
10 Department of Neuroscience, Brown University
11 Rotman Research Institute at Baycrest, Toronto
12 Department of Psychology, University of Oregon
13 Department of Psychiatry, McGill University
14 Department of Psychology, Toronto Metropolitan University
15 Department of Psychology, Stony Brook University
16 Department of Medical and Translational Biology, Umeå University
17 Department of Diagnostics and Intervention, Umeå University
A primary goal in the cognitive neuroscience of aging is to delineate precisely which brain changes underpin aging-related changes in cognition. However, the field has evolved such that ~90% of recent published research focuses on either brain structure or resting-state function (Mooraj et al., under review), neither of which involve imaging the aging brain during experimentally manipulated cognitive operations. Such approaches are therefore unlikely to provide as comprehensive an understanding of the neural bases of cognitive aging as functional neuroimaging accounts (ranging from EEG and fMRI, to dynamic PET and functional MRS), which allow for a sensitive and flexible interrogation of the brainin action by providing an online window into cognitive functioning. We thus emphasise the necessity and value of a functionally interrogated, multi-modally imaged, behaviour-first perspective on the cognitive neuroscience of aging.
Prediction of individual cognitive test scores from brain and non-brain data across the adults lifespan.
by Christiane Jockwitz1,2 | Camilla Mendl-Heinisch1,2 | Tatiana Miller1,2 | Paulo Dellani1,2 | Svenja Caspers1,2
1 Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany
2 Institute for Anatomy I, Medical Faculty & University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
Predicting cognitive decline in aging remains a challenging but important topic. Existing results are heterogeneous, potentially due to the non-linear nature of both, cognitive decline and the factors that influence it. We here aimed to systematically examine the predictability of cognitive abilities based on brain and non-brain data across five decades of the adult lifespan in the large German National Cohort (NAKO; N = 23,863; 25 to 75 years). Brain summary statistics (e.g. total grey matter), health (e.g. body-mass-index) and demographic (i.e. age, sex, education) data were used to predict four cognitive scores using a machine learning (ML; repeated nested cross-validation; four regression algorithms) approach.
Current results emphasize that demographics tend to outperform brain and health factors in predicting cognitive abilities in a large sample spanning the whole adulthood, with better predictability for episodic memory and interference compared to verbal fluency and working memory. Contrary to the hypothesis of a worse prediction at older ages, prediction appeared to be similarly low in each decade. Hence, sample size seems to matter even more than sample homogeneity. Including a wide age range for reaching large sample sizes, though, could come at the cost of predicting a hidden age effect.
Prediction of individual cognitive test performance based on imaging and non-imaging data in older adults.
by Camilla Mendl-Heinisch1,2 | Nora Bittner1,2 | Tatiana Miller1,2 | Paulo Dellani1,2 | Svenja Caspers1,2 | Christiane Jockwitz1,2
1 Institute for Anatomy I, Medical Faculty & University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
2 Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany
Early detection of cognitive decline gains relevance in normal aging given its impact on the quality of life of older adults. While using brain imaging data alone can be challenging, there is an opportunity to use health-related and demographic data as biomarker as these are easily accessible and have already been shown to be associated with cognitive dysfunction.
Thus, using machine learning (ML) we examined the practicality of 1) imaging, 2) healthrelated and 3) demographic data, in the prediction of cognitive functioning (16 cognitive test scores) in 494 older adults (67 +/- 7 years) from 1000BRAINS. Prediction performance was obtained for each modality and its combinations using cross-validation and four algorithms.
Predictability differences emerged across modalities and cognitive functions. In terms of individual tests, vocabulary, executive and episodic memory functions were moderately predicted from demographic and partially from brain data; working memory showed low predictability across modalities.
Overall, health-related data showed limited predictability across cognitive functions despite known associations between cardiovascular health and cognitive decline. Strikingly, demographic variables outperformed health and imaging data highlighting their impact on predictions of cognition. Finally, we observed higher predictability of executive and episodic memory functions, which are important for the prognosis of neurodegenerative diseases.
Reduced dynamic functional connectivity in higher ages: are older brains less adaptable?
by Natalia Zhukova1,2 | Camilla Mendl-Heinisch1,2 | Chrictiane Jockwitz1,2 | Svenja Caspers1,2 |
1 Institute for Anatomy I, Medical Faculty & University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
2 Institute of Neuroscience and Medicine (INM-1), Research Center Jülich, Jülich, Germany
Static functional connectivity (FC) approaches, assuming constant interactions between brain regions, revealed important insights into the aging process. Nevertheless, recent research has indicated that a more sophisticated understanding of the time-varying nature of brain function may prove particularly useful in identifying biomarkers for healthy aging.
The current study therefore investigated dynamic FC (dFC) in a large group of older adults (1000BRAINS; n=817; 373 females; 55-85 years; MAge=67±7). Time-varying correlation matrices and dFC states were extracted from resting-state fMRI using sliding windows and clustering. We examined both, temporal features of dFC states, e.g. duration and transitions, together with age-related differences in network architecture (17 networks Schaefer parcellation). We found four distinct dFC states, of which two showed agesensitive patterns. The first was distinguished by highly connected networks and became less prevalent with age. In contrast, the second state was characterized by reduced network connectivity, becoming more prevalent with age. Together with a decline in transition between states, the results underscore an age-related reduction in overall network communication and a reduced capacity for functional adaptation. The findings challenge the conventional understanding of brain network interactions by emphasizing the dynamic adaptability of the brain in explaining variations in cognitive functioning.