scholarly journals Generation of Infant-dedicated Fine-grained Functional Parcellation Maps of Cerebral Cortex

2021 ◽  
Author(s):  
Fan Wang ◽  
Han Zhang ◽  
Zhengwang Wu ◽  
Dan Hu ◽  
Zhen Zhou ◽  
...  
Keyword(s):  
Resting State Fmri ◽  
Temporal Variations ◽  
Development Pattern ◽  
Dynamic Changes ◽  
Fine Grained ◽  
Human Brain Development ◽  
Functional Development ◽  
Cortical Parcellation ◽  
Gradient Density ◽  
Infant Brain

Infancy is a dynamic and immensely important period in human brain development. Studies of infant functional development using resting-state fMRI rely on precisely defined cortical parcellation maps. However, available adult-based functional parcellation maps are not applicable for infants due to their substantial differences in functional organizations. Fine-grained infant-dedicated cortical parcellation maps are highly desired but remain scarce, due to difficulties ranging from acquiring to processing of infant brain MRIs. In this study, leveraging 1,064 high-resolution longitudinal rs-fMRIs from 197 infants from birth to 24 months and advanced infant-dedicated processing tools, we create the first set of infant-specific, fine-grained cortical functional parcellation maps. Besides the conventional folding-based cortical registration, we specifically establish the functional correspondences across individuals using functional gradient densities and generate both age-specific and age-common fine-grained parcellation maps. The first set of comprehensive brain functional developmental maps are accordingly derived, and reveals a complex, hitherto unseen multi-peak fluctuation development pattern in temporal variations of gradient density, network sizes, and local efficiency, with more dynamic changes during the first 9 months than other ages. Our proposed method is applicable in generating fine-grained parcellations for the whole lifespan, and our parcellation maps will be available online to advance the neuroimaging field.

scholarly journals Young Infants Process Prediction Errors at the Theta Rhythm

2020 ◽  
Author(s):  
Moritz Köster ◽  
Miriam Langeloh ◽  
Christine Michel ◽  
Stefanie Hoehl
Keyword(s):  
Theta Rhythm ◽  
Time Window ◽  
Predictive Processing ◽  
Prediction Errors ◽  
Unexpected Events ◽  
Young Infants ◽  
Human Brain Development ◽  
Social Events ◽  
Infant Brain ◽  
Process Prediction

AbstractExamining how young infants respond to unexpected events is key to our understanding of their emerging concepts about the world around them. From a predictive processing perspective, it is intriguing to investigate how the infant brain responds to unexpected events (i.e., prediction errors), because they require infants to refine their predictive models about the environment. Here, to better understand prediction error processes in the infant brain, we presented 9-month-olds (N = 36) a variety of physical and social events with unexpected versus expected outcomes, while recording their electroencephalogram. We found a pronounced response in the ongoing 4 – 5 Hz theta rhythm for the processing of unexpected (in contrast to expected) events, for a prolonged time window (2 s) and across all scalp-recorded electrodes. The condition difference in the theta rhythm was not related to the condition difference in infants’ event-related activity on the negative central (Nc) component (.4 – .6 s), which has been described in former studies. These findings constitute critical evidence that the theta rhythm is involved in the processing of prediction errors from very early in human brain development, which may support infants’ refinement of basic concepts about the physical and social environment.

scholarly journals Slow Cortical Waves via Cyclicity

2021 ◽  
Author(s):  
Ivan Abraham ◽  
Bahar Shahsavarani ◽  
Ben Zimmerman ◽  
Fatima Husain ◽  
yuliy baryshnikov
Keyword(s):  
Time Series ◽  
Dynamic Structure ◽  
Resting State Fmri ◽  
Brain Regions ◽  
Fine Grained ◽  
Human Connectome Project ◽  
Fmri Time Series ◽  
The Brain ◽  
Cortical Waves ◽  
Temporal Ordering

Fine-grained information about dynamic structure of cortical networks is crucial in unpacking brain function. Here,we introduced a novel analytical method to characterize the dynamic interaction between distant brain regions,based on cyclicity analysis, and applied it to data from the Human Connectome Project. Resting-state fMRI time series are aperiodic and, hence, lack a base frequency. Cyclicity analysis, which is time-reparametrization invariant, is effective in recovering dynamic temporal ordering of such time series along a circular trajectory without assuming any time scale. Our analysis detected the propagation of slow cortical waves across thebrain with consistent shifts in lead-lag relationships between specific brain regions. We also observed short bursts of strong temporal ordering that dominated overall lead-lag relationships between pairs of regions in the brain, which were modulated by tasks. Our results suggest the possible role played by slow waves of ordered information between brain regions that underlie emergent cognitive function.

scholarly journals Functional development in the infant brain for auditory pitch processing

2011 ◽  
Vol 33 (3) ◽  
pp. 596-608 ◽  
Author(s):  
Fumitaka Homae ◽  
Hama Watanabe ◽  
Tamami Nakano ◽  
Gentaro Taga
Keyword(s):  
Pitch Processing ◽  
Functional Development ◽  
Infant Brain

scholarly journals The Functional Architecture of the Infant Brain as Revealed by Resting-State fMRI

2010 ◽  
Vol 21 (1) ◽  
pp. 145-154 ◽  
Author(s):  
Peter Fransson ◽  
Ulrika Åden ◽  
Mats Blennow ◽  
Hugo Lagercrantz
Keyword(s):  
Resting State ◽  
Resting State Fmri ◽  
Functional Architecture ◽  
Infant Brain

scholarly journals Multimodal Transformer Networks for Pedestrian Trajectory Prediction

2021 ◽  
Author(s):  
Ziyi Yin ◽  
Ruijin Liu ◽  
Zhiliang Xiong ◽  
Zejian Yuan
Keyword(s):  
Optical Flow ◽  
Temporal Variations ◽  
Coarse Grained ◽  
Vehicle Speed ◽  
Temporal Consistency ◽  
Moving Vehicle ◽  
Dynamic Motion ◽  
Fine Grained ◽  
High Dynamics

We consider the problem of forecasting the future locations of pedestrians in an ego-centric view of a moving vehicle. Current CNNs or RNNs are flawed in capturing the high dynamics of motion between pedestrians and the ego-vehicle, and suffer from the massive parameter usages due to the inefficiency of learning long-term temporal dependencies. To address these issues, we propose an efficient multimodal transformer network that aggregates the trajectory and ego-vehicle speed variations at a coarse granularity and interacts with the optical flow in a fine-grained level to fill the vacancy of highly dynamic motion. Specifically, a coarse-grained fusion stage fuses the information between trajectory and ego-vehicle speed modalities to capture the general temporal consistency. Meanwhile, a fine-grained fusion stage merges the optical flow in the center area and pedestrian area, which compensates the highly dynamic motion of ego-vehicle and target pedestrian. Besides, the whole network is only attention-based that can efficiently model long-term sequences for better capturing the temporal variations. Our multimodal transformer is validated on the PIE and JAAD datasets and achieves state-of-the-art performance with the most light-weight model size. The codes are available at https://github.com/ericyinyzy/MTN_trajectory.

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