Face-Selective Responses Present in Multiple Regions of the Human Infant Brain

Faces are a rich source of social information. How does the infant brain develop the ability to recognize faces and identify potential social partners? We collected functional magnetic neuroimaging (fMRI) data from 49 awake human infants (aged 2.5-9.7 months) while they watched movies of faces, bodies, objects, and scenes. Face-selective responses were observed not only in ventral temporal cortex (VTC) but also in superior temporal sulcus (STS), and medial prefrontal cortex (MPFC). Face responses were also observed (but not fully selective) in the amygdala and thalamus. We find no evidence that face-selective responses develop in visual perception regions (VTC) prior to higher order social perception (STS) or social evaluation (MPFC) regions. We suggest that face-selective responses may develop in parallel across multiple cortical regions. Infants’ brains could thus simultaneously process faces both as a privileged category of visual images, and as potential social partners.

A brain extraction algorithm for infant T2 weighted magnetic resonance images based on fuzzy c-means thresholding

It is challenging to extract the brain region from T2-weighted magnetic resonance infant brain images because conventional brain segmentation algorithms are generally optimized for adult brain images, which have different spatial resolution, dynamic changes of imaging intensity, brain size and shape from infant brain images. In this study, we propose a brain extraction algorithm for infant T2-weighted images. The proposed method utilizes histogram partitioning to separate brain regions from the background image. Then, fuzzy c-means thresholding is performed to obtain a rough brain mask for each image slice, followed by refinement steps. For slices that contain eye regions, an additional eye removal algorithm is proposed to eliminate eyes from the brain mask. By using the proposed method, accurate masks for infant T2-weighted brain images can be generated. For validation, we applied the proposed algorithm and conventional methods to T2 infant images (0–24 months of age) acquired with 2D and 3D sequences at 3T MRI. The Dice coefficients and Precision scores, which were calculated as quantitative measures, showed the highest values for the proposed method as follows: For images acquired with a 2D imaging sequence, the average Dice coefficients were 0.9650 ± 0.006 for the proposed method, 0.9262 ± 0.006 for iBEAT, and 0.9490 ± 0.006 for BET. For the data acquired with a 3D imaging sequence, the average Dice coefficient was 0.9746 ± 0.008 for the proposed method, 0.9448 ± 0.004 for iBEAT, and 0.9622 ± 0.01 for BET. The average Precision was 0.9638 ± 0.009 and 0.9565 ± 0.016 for the proposed method, 0.8981 ± 0.01 and 0.8968 ± 0.008 for iBEAT, and 0.9346 ± 0.014 and 0.9282 ± 0.019 for BET for images acquired with 2D and 3D imaging sequences, respectively, demonstrating that the proposed method could be efficiently used for brain extraction in T2-weighted infant images.

Decoding speech information from EEG data with 4, 7 and 11 month-old infants: Contrasting convolutional neural network, mutual information-based and backward linear models

Computational models that successfully translate neural activity into speech are multiplying in the adult literature, with non-linear convolutional neural network (CNN) approaches joining the more frequently-employed linear and mutual information (MI) models. Despite the promise of these methods for uncovering the neural basis of language acquisition by the human brain, similar studies with infants are rare. Existing infant studies rely on simpler cross-correlation and other linear techniques and aim only to establish neural tracking of the broadband speech envelope. Here, three novel computational models were applied to measure whether low-frequency speech envelope information was encoded in infant neural activity. Backward linear and CNN models were applied to estimate speech information from neural activity using linear versus nonlinear approaches, and a MI model measured how well the acoustic stimuli were encoded in infant neural responses. Fifty infants provided EEG recordings when aged 4, 7, and 11 months, while listening passively to natural speech (sung nursery rhymes) presented by video with a female singer. Each model computed speech information for these nursery rhymes in two different frequency bands, delta (1 – 4 Hz) and theta (4 – 8 Hz), thought to provide different types of linguistic information. All three models demonstrated significant levels of performance for delta-band and theta-band neural activity from 4 months of age. All models also demonstrated higher accuracy for the delta-band neural response in the infant brain. However, only the linear and MI models showed developmental (age-related) effects, and these developmental effects differed by model. Accordingly, the choice of algorithm used to decode speech envelope information from neural activity in the infant brain may determine the developmental conclusions that can be drawn. Better understanding of the strengths and weaknesses of each modelling approach will be fundamental to improving our understanding of how the human brain builds a language system.

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

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.

Real-time motion monitoring improves functional MRI data quality in infants

Imaging the infant brain with MRI has improved our understanding of early stages of neurodevelopment. However, head motion during MRI acquisition is detrimental to both functional and structural MRI scan quality. Though infants are commonly scanned while asleep, they commonly exhibit motion during scanning, causing data loss. Our group has shown that providing MRI technicians with real-time motion estimates via Framewise Integrated Real-Time MRI Monitoring (FIRMM) software helps obtain high-quality, low motion fMRI data. By estimating head motion in real time and displaying motion metrics to the MR technician during an fMRI scan, FIRMM can improve scanning efficiency. Hence, we compared average framewise displacement (FD), a proxy for head motion, and the amount of usable fMRI data (FD ≤ 0.2mm) in infants scanned with (n = 407) and without FIRMM (n = 295). Using a mixed-effects model, we found that the addition of FIRMM to current state-of-the-art infant scanning protocols significantly increased the amount of usable fMRI data acquired per infant, demonstrating its value for research and clinical infant neuroimaging.

Sequence learning attenuates cortical responses in both frontal and perceptual cortices in early infancy

Prior work using a variety of imaging modalities has found that the frontal lobe is involved in higher-order sequential and statistical learning in young infants. Separate lines of work have found evidence of modulation of posterior sensory cortices during and after learning tasks. How do these processes relate together? Here, we build from a well-regarded EEG task that found evidence that the frontal lobe of young infants tracked higher-order sequential information (Basirat et al., 2014) and ask whether posterior perceptual cortices respond differentially to predictable vs. unpredictable sequences as well. First, replicating and extending past work, we found evidence of frontal lobe involvement in this task. Second, consistent with our hypotheses, we found that there is a corresponding attenuation of neural responses in the posterior perceptual cortices (temporal and occipital) to predictable compared to unpredictable audiovisual sequences. This study provides convergent evidence that the frontal lobe is crucial for higher-level learning in young infants but that it likely works as part of a large, distributed network of regions to modulate infant neural responses as a result of learning. Overall, this work challenges the view that the infant brain is not dynamic and disconnected, lacking in long-range neural connections. Instead, this paper reveals patterns of a highly dynamic and interconnected infant brain that change rapidly as a result of new, learnable experiences.

Visual category representations in the infant brain

Visual categorization is a human core cognitive capacity that depends on the development of visual category representations in the infant brain. The nature of infant visual category representations and their relationship to the corresponding adult form however remain unknown. Our results clarify the nature of visual category representations in 6- to 8-month-old infants and their developmental trajectory towards adult maturity in the key characteristics temporal dynamics, representational format, and spectral properties. Temporal dynamics change from slowly emerging, developing representations in infants to quickly emerging, complex representations in adults. Despite those differences infants and adults already partly share visual category representations. The format of infants' representations are visual features of low to intermediate complexity, whereas adults' representations also encoded high complexity features. Theta band neural oscillations form the basis of visual category representations in infants, and these representations are shifted to the alpha/beta band in adults.

TMOD-23. MODELING PEDIATRIC BRAIN CANCER WITH HUMAN ORGANOIDS

Among children/infant brain tumors, Medulloblastoma (MB) is the most common and stands as a cause for a high percentage of morbidity and mortality among patients During the past few years, studies on human MB have uncovered the existence of four major MB groups: WNT, SHH, Group3 and Group4. Patients with Group3 MB currently have the worst outcome among the four groups, and nearly 50% are metastatic at the time of diagnosis. In the last 3 years in our laboratory, we have developed a novel pediatric Medulloblastoma organoid model. We generated human iPSC-derived cancer organoids upon c-MYC/OTX2 and C-MYC/Gfi1 overexpression, mimicking human MB genetic alterations. Furthermore, the use of DNA methylation signature in combination with MB-specific markers analysis indicates that our organoid-based cancer model recapitulates several features of human MB. Now, we are taking advantage of this technology to produce novel brain cancer organoids that we are using to address cancer biology questions.