Wednesday, October 9th, 2024

Time	Event
10:33a	Computational neurodevelopment: infant decision-making in changing environments In recognition of the fact that most psychiatric conditions have neurodevelopmental origins, there is an increasing interest in applying the methodological and conceptual approaches from computational psychiatry to developmental cohorts. However, the challenge of acquiring and modelling behavioural responses in very young infants has thus far proven difficult to overcome. To address this we developed a novel gaze-contingent, cued-reversal paradigm that allowed 6-10 month old infants to make overt behavioural responses to assess learning of expectations and updating of behaviour in response to change. We then fit computational models to infant behaviour and, for the first time, were able to validate the winning model to the same standards as would be expected of adults (e.g. good parameter recoverability, model identifiability and simulated behavioural responses). Similar to prior findings in adults, model-based prediction error measures correlated with post-switch increases in pupil size; consistent with noradrenalines hypothesised role in learning about change. Data-driven clustering based on model parameters revealed two infant behavioural subtypes hidden within the data; one with a perseverating profile and the other with a more exploratory decision-making pattern. This approach sheds new light on the classic finding that all infants under 12 months tend to perseverate. Crucially, there were no significant differences in age between the clusters, but differences in terms of adaptive skills and temperament measured via gold-standard developmental assessments. These results prime the field for infant computational psychiatry, demonstrating that we can reliably fit models to infant data and that the parameters from such models can identify subgroups with distinct cognitive profiles that are superior to those derived from the behavioural data alone. (Comment on this)
11:45a	Electrophysiological decoding captures the temporal trajectory of face categorization in infants The adult human brain rapidly distinguishes between faces at around 170 milliseconds after stimulus onset. In the developing brain, however, the time course of face discrimination is poorly understood. To shed light on this issue, we presented human and nonhuman primate faces to five to thirteen-month-old infants in an event-related electroencephalography experiment. Using time-resolved decoding based on logistic regression we detected above-chance discrimination of human faces from nonhuman faces in a time window starting at around 200 milliseconds, originating from occipito-temporal electrodes. There was no evidence, however, for above-chance discrimination of individual human or individual nonhuman faces. Moreover, using neural network-based decoding, we delivered the proof of principle that face categorization but not individuation can be detected even at the level of single participants. These results indicate that rapid face categorization emerges already in preverbal infants. (Comment on this)
11:45a	Perinatal Reduction of Genetically Aberrant Neurons from Human Cerebral Cortex Since human neurons are postmitotic and long-lived, the regulation of their genomic content is crucial. Normal neuronal function is uniquely dependent on gene dosage, with diverse genome copy number alterations (CNA) associated with neurodevelopmental and neuropsychiatric conditions1-3. In this study, we evaluated the landscape of CNA arising in normal human brains, focusing on prenatal and perinatal ages. We surveyed [~]5,897 CNA in >1,200 single neurons from human postmortem brain of individuals without a neurological diagnosis, ranging in age from gestational week (GW) 14 to 90 years old. Using Tn5-based single-cell whole-genome amplification (scWGA) and informatic advances to validate CNAs in single neurons, we determined that a striking proportion of neurons (up to 45%) in human prenatal cortex showed aberrant genomes, characterized by large-scale CNAs in multiple chromosomes, which reduces significantly during the perinatal period (p<0.1). Furthermore, we identified micronuclei in the developing cortex, reflecting genetic instability reminiscent of that described in early embryonic development4-6. The scale of CNA appeared to alter the trajectory of neuronal elimination, as subchromosomal CNAs were more slowly eliminated, over the course of a lifetime. CNAs were depleted for dosage-sensitive genes and genes involved in neurodevelopmental disorders (p<.05), and thus represent genomic quality control mechanisms that eliminate selectively those neurons with CNA involving critical genes. Perinatal elimination of defective neuronal genomes may in turn reflect a developmental landmark essential for normal cognitive function. (Comment on this)
11:45a	Loss of Flower/FLWR-1 induces an increase in neuronal excitability and causes defective recycling of synaptic vesicles The Flower protein is proposed to couple synaptic vesicle fusion to recycling in different model organisms. It is supposed to trigger activity-dependent bulk endocytosis by conducting Ca2+ at endocytic sites. However, this mode of action is debated. Here, we investigate the role of the nematode homolog (FLWR-1) in neurotransmission in Caenorhabditis elegans. Our results confirm that FLWR-1 facilitates the recycling of synaptic vesicles at the neuromuscular junction. Ultrastructural analysis of synaptic boutons after hyperstimulation surprisingly reveals an accumulation of endosomal structures in flwr-1 mutants. These findings do not support a role of FLWR-1 in the formation of bulk endosomes but rather a function in their breakdown following cleavage from the plasma membrane. Unexpectedly, loss of FLWR-1 conveys increased neuronal excitability which causes an excitation-inhibition imbalance. Finally, we obtained evidence that this increased transmission at the neuromuscular junction might be caused by deregulation of MCA-3, the nematode homolog of the plasma membrane Ca2+ ATPase (PMCA). (Comment on this)

<< Previous Day

2024/10/09 [Calendar]

Next Day >>