Age-related disintegration in functional connectivity: Evidence from Reference Ability Neural Network (RANN) cohort

2021 ◽  
Vol 156 ◽  
pp. 107856
Author(s):  
Georgette Argiris ◽  
Yaakov Stern ◽  
Christian Habeck
Keyword(s):  
Neural Network ◽  
Functional Connectivity ◽  
Age Related

scholarly journals Explainable Convolutional Neural Network to Investigate the Age-Related Changes in Multi-Order Functional Connectivity

2021 ◽  
Author(s):  
Sunghee Dong ◽  
Yan Jin ◽  
SuJin Bak ◽  
Bumchul Yoon ◽  
and Jichai Jeong
Keyword(s):  
Neural Network ◽  
Functional Connectivity ◽  
Convolutional Neural Network ◽  
Classification Accuracy ◽  
The Elderly ◽  
Higher Order ◽  
Potential Candidate ◽  
Younger Adults ◽  
Age Related ◽  
Age Related Changes

Functional connectivity (FC) is a potential candidate that can increase the performance of brain-computer interfaces (BCIs) in the elderly because of its compensatory role in neural circuits. However, it is difficult to decode FC by current machine learning techniques because of a lack of its physiological understanding. To investigate the suitability of FC in BCI for the elderly, we propose the decoding of lower- and higher-order FCs using a convolutional neural network (CNN) in six cognitive-motor tasks. The layer-wise relevance propagation (LRP) method describes how age-related changes in FCs impact BCI applications for the elderly compared to younger adults. Seventeen younger (24.5±2.7 years) and twelve older (72.5±3.2 years) adults were recruited to perform tasks related to hand-force control with or without mental calculation. CNN yielded a six-class classification accuracy of 75.3% in the elderly, exceeding the 70.7% accuracy for the younger adults. In the elderly, the proposed method increases the classification accuracy by 88.3% compared to the filter-bank common spatial pattern (FBCSP). LRP results revealed that both lower- and higher-order FCs were dominantly overactivated in the prefrontal lobe depending on task type. These findings suggest a promising application of multi-order FC with deep learning on BCI systems for the elderly.

scholarly journals Explainable Convolutional Neural Network to Investigate Age-Related Changes in Multi-Order Functional Connectivity

Electronics ◽  
2021 ◽  
Vol 10 (23) ◽  
pp. 3020
Author(s):  
Sunghee Dong ◽  
Yan Jin ◽  
SuJin Bak ◽  
Bumchul Yoon ◽  
Jichai Jeong
Keyword(s):  
Neural Network ◽  
Functional Connectivity ◽  
Convolutional Neural Network ◽  
Classification Accuracy ◽  
The Elderly ◽  
Higher Order ◽  
Potential Candidate ◽  
Younger Adults ◽  
Age Related ◽  
Age Related Changes

Functional connectivity (FC) is a potential candidate that can increase the performance of brain-computer interfaces (BCIs) in the elderly because of its compensatory role in neural circuits. However, it is difficult to decode FC by the current machine learning techniques because of a lack of physiological understanding. To investigate the suitability of FC in BCIs for the elderly, we propose the decoding of lower- and higher-order FC using a convolutional neural network (CNN) in six cognitive-motor tasks. The layer-wise relevance propagation (LRP) method describes how age-related changes in FCs impact BCI applications for the elderly compared to younger adults. A total of 17 young adults 24.5±2.7 years and 12 older 72.5±3.2 years adults were recruited to perform tasks related to hand-force control with or without mental calculation. The CNN yielded a six-class classification accuracy of 75.3% in the elderly, exceeding the 70.7% accuracy for the younger adults. In the elderly, the proposed method increased the classification accuracy by 88.3% compared to the filter-bank common spatial pattern. The LRP results revealed that both lower- and higher-order FCs were dominantly overactivated in the prefrontal lobe, depending on the task type. These findings suggest a promising application of multi-order FC with deep learning on BCI systems for the elderly.

Age differences in ventromedial prefrontal cortex functional connectivity during socioemotional content processing

2020 ◽  
Vol 48 (7) ◽  
pp. 1-19
Author(s):  
Ryan T. Daley ◽  
Holly J. Bowen ◽  
Eric C. Fields ◽  
Angela Gutchess ◽  
Elizabeth A. Kensinger
Keyword(s):  
Prefrontal Cortex ◽  
Functional Connectivity ◽  
Inferior Frontal Gyrus ◽  
Age Groups ◽  
Emotional Content ◽  
Ventromedial Prefrontal Cortex ◽  
Anterior Cingulate ◽  
Supramarginal Gyrus ◽  
Age Related ◽  
Content Processing

Self-relevance effects are often confounded by the presence of emotional content, rendering it difficult to determine how brain networks functionally connected to the ventromedial prefrontal cortex (vmPFC) are affected by the independent contributions of self-relevance and emotion. This difficulty is complicated by age-related changes in functional connectivity between the vmPFC and other default mode network regions, and regions typically associated with externally oriented networks. We asked groups of younger and older adults to imagine placing emotional and neutral objects in their home or a stranger's home. An age-invariant vmPFC cluster showed increased activation for self-relevant and emotional content processing. Functional connectivity analyses revealed age × self-relevance interactions in vmPFC connectivity with the anterior cingulate cortex. There were also age × emotion interactions in vmPFC functional connectivity with the anterior insula, orbitofrontal gyrus, inferior frontal gyrus, and supramarginal gyrus. Interactions occurred in regions with the greatest differences between the age groups, as revealed by conjunction analyses. Implications of the findings are discussed.

scholarly journals Age-related differences in resting state functional connectivity in pediatric migraine

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Tiffany Bell ◽  
Akashroop Khaira ◽  
Mehak Stokoe ◽  
Megan Webb ◽  
Melanie Noel ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Power Spectra ◽  
Interaction Effects ◽  
Resting State Functional Connectivity ◽  
Default Mode ◽  
Pediatric Migraine ◽  
Age Related ◽  
Status Interaction ◽  
Underlying Mechanisms

Abstract Background Migraine affects roughly 10% of youth aged 5–15 years, however the underlying mechanisms of migraine in youth are poorly understood. Multiple structural and functional alterations have been shown in the brains of adult migraine sufferers. This study aims to investigate the effects of migraine on resting-state functional connectivity during the period of transition from childhood to adolescence, a critical period of brain development and the time when rates of pediatric chronic pain spikes. Methods Using independent component analysis, we compared resting state network spatial maps and power spectra between youth with migraine aged 7–15 and age-matched controls. Statistical comparisons were conducted using a MANCOVA analysis. Results We show (1) group by age interaction effects on connectivity in the visual and salience networks, group by sex interaction effects on connectivity in the default mode network and group by pubertal status interaction effects on connectivity in visual and frontal parietal networks, and (2) relationships between connectivity in the visual networks and the migraine cycle, and age by cycle interaction effects on connectivity in the visual, default mode and sensorimotor networks. Conclusions We demonstrate that brain alterations begin early in youth with migraine and are modulated by development. This highlights the need for further study into the neural mechanisms of migraine in youth specifically, to aid in the development of more effective treatments.

scholarly journals Reduced resting state functional connectivity with increasing age-related hearing loss and McGurk susceptibility

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Alina Schulte ◽  
Christiane M. Thiel ◽  
Anja Gieseler ◽  
Maike Tahden ◽  
Hans Colonius ◽  
...  
Keyword(s):  
Hearing Loss ◽  
Functional Connectivity ◽  
Resting State ◽  
Mcgurk Effect ◽  
Functional Coupling ◽  
Resting State Functional Connectivity ◽  
Visual Integration ◽  
Age Related ◽  
Hearing Thresholds ◽  
Age Related Hearing Loss

Abstract Age-related hearing loss has been related to a compensatory increase in audio-visual integration and neural reorganization including alterations in functional resting state connectivity. How these two changes are linked in elderly listeners is unclear. The current study explored modulatory effects of hearing thresholds and audio-visual integration on resting state functional connectivity. We analysed a large set of resting state data of 65 elderly participants with a widely varying degree of untreated hearing loss. Audio-visual integration, as gauged with the McGurk effect, increased with progressing hearing thresholds. On the neural level, McGurk illusions were negatively related to functional coupling between motor and auditory regions. Similarly, connectivity of the dorsal attention network to sensorimotor and primary motor cortices was reduced with increasing hearing loss. The same effect was obtained for connectivity between the salience network and visual cortex. Our findings suggest that with progressing untreated age-related hearing loss, functional coupling at rest declines, affecting connectivity of brain networks and areas associated with attentional, visual, sensorimotor and motor processes. Especially connectivity reductions between auditory and motor areas were related to stronger audio-visual integration found with increasing hearing loss.

scholarly journals Cerebral functional networks during sleep in young and older individuals

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Véronique Daneault ◽  
Pierre Orban ◽  
Nicolas Martin ◽  
Christian Dansereau ◽  
Jonathan Godbout ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Brain Plasticity ◽  
Network Connectivity ◽  
Sleep Stages ◽  
Functional Networks ◽  
Older Individuals ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
Age Related ◽  
Light Sleep

AbstractEven though sleep modification is a hallmark of the aging process, age-related changes in functional connectivity using functional Magnetic Resonance Imaging (fMRI) during sleep, remain unknown. Here, we combined electroencephalography and fMRI to examine functional connectivity differences between wakefulness and light sleep stages (N1 and N2 stages) in 16 young (23.1 ± 3.3y; 7 women), and 14 older individuals (59.6 ± 5.7y; 8 women). Results revealed extended, distributed (inter-between) and local (intra-within) decreases in network connectivity during sleep both in young and older individuals. However, compared to the young participants, older individuals showed lower decreases in connectivity or even increases in connectivity between thalamus/basal ganglia and several cerebral regions as well as between frontal regions of various networks. These findings reflect a reduced ability of the older brain to disconnect during sleep that may impede optimal disengagement for loss of responsiveness, enhanced lighter and fragmented sleep, and contribute to age effects on sleep-dependent brain plasticity.

Feed-forward artificial neural network provides data-driven inference of functional connectivity

2019 ◽  
Vol 29 (9) ◽  
pp. 091101 ◽  
Author(s):  
Nikita Frolov ◽  
Vladimir Maksimenko ◽  
Annika Lüttjohann ◽  
Alexey Koronovskii ◽  
Alexander Hramov
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Functional Connectivity ◽  
Data Driven ◽  
Feed Forward ◽  
Artificial Neural
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