Sato, J. R. | bioRxiv Science

bioRxiv2018

Unique individual factors shape brain hubs organization of the human functional connectome

Recent evidence suggests that the human functional connectome is stable at different time scales and unique. These characteristics posit the functional connectome not only as an individual marker but also as a powerful discriminatory measure characterized by high intersubject variability. Among distinct sources of intersubject variability, the long-term sources include functional patterns that emerge from genetic factors. Here, we sought to investigate the contribution of additive genetic factors to the variability of functional networks by determining the heritability of the connectivity strength in a multivariate fashion. First, we reproduced and extended the connectome fingerprinting analysis to the identification of twin pairs. Then, we estimated the heritability of functional networks by a multivariate ACE modeling approach with bootstrapping. Twin pairs were identified above chance level using connectome fingerprinting, with monozygotic twin identification accuracy equal to 57.2% on average for whole-brain connectome. Additionally, we found that a visual (0.37), the medial frontal (0.31) and the motor (0.30) functional networks were the most influenced by additive genetic factors. Our findings suggest that genetic factors not only partially determine intersubject variability of the functional connectome, such that twins can be identified using connectome fingerprinting, but also differentially influence connectivity strength in large-scale functional networks.

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bioRxiv2017

Network supporting contextual fear learning after hippocampal damage has increased dependence on retrosplenial cortex

Hippocampal damage results in profound retrograde, but no anterograde amnesia in contextual fear conditioning (CFC). Although the content learned in the latter have been discussed, the compensating regions were seldom proposed and never empirically addressed. Here, we employed network analysis of pCREB expression quantified from brain slices of rats with dorsal hippocampal lesion (dHPC) after undergoing CFC session. Using inter-regional correlations of pCREB-positive nuclei between brain regions, we modelled functional networks using different thresholds. The dHPC network showed small-world topology, equivalent to SHAM (control) network. However, diverging hubs were identified in each network. In a direct comparison, hubs in both networks showed consistently higher centrality values compared to the other network. Further, the distribution of correlation coefficients was different between the groups, with most significantly stronger correlation coefficients belonging to the SHAM network. These results suggest that dHPC network engaged in CFC learning is partially different, and engage alternative hubs. We next tested if pre-training lesions of dHPC and one of the new dHPC network hubs (perirhinal, Per; or disgranular retrosplenial, RSC, cortices) would impair CFC. Only dHPC-RSC, but not dHPC-Per, impaired CFC. Interestingly, only RSC showed a consistently higher centrality in the dHPC network, suggesting that the increased centrality reflects an increased functional dependence on RSC. Our results provide evidence that, without hippocampus, the RSC, an anatomically central region in the medial temporal lobe memory system might support CFC learning and memory.\n\nAUTHOR SUMMARYWhen determined cognitive performances are not affected by brain lesions of regions generally involved in that performance, the interpretation is that the remaining regions can compensate the damaged one. In contextual fear conditioning, a memory model largely used in laboratory rodents, hippocampal lesions produce amnesia for events occurred before, but not after the lesion, although the hippocampus is known to be important for new learning. Addressing compensation in animal models has always been challenging as it requires large-scale brain mapping. Here, we quantified 30 brain regions and used mathematical tools to model how a brain network can compensate hippocampal loss and learn contextual fear. We described that the damaged network preserved general interactivity characteristics, although different brain regions were identified as highly important for the network (e.g. highly connected). Further, we empirically validated our network model by performing double lesions of the hippocampus and the alternative hubs observed in the network models. We verified that double lesion of the hippocampus and retrosplenial cortex, one of the hubs, impaired contextual fear learning. We provide evidence that without hippocampus, the remaining network relies on alternative important regions from the memory system to coordinate contextual fear learning.

neuroscience↗

bioRxiv2017

A common representation of time across visual andauditory modalities

Humans and non-human animals ability to process time on the scale of milliseconds and seconds is essential for adaptive behaviour. A central question of how brains keep track of time is how specific temporal information across different sensory modalities is. In the present study, we show that encoding of temporal intervals in auditory and visual modalities are qualitatively similar. Human participants were instructed to reproduce intervals in the range from 750 ms to 1500 ms marked by auditory or visual stimuli. Our behavioural results suggest that, although participants were more accurate in reproducing intervals marked by auditory stimuli, there was a strong correlation in performance between modalities. Using multivariate pattern analysis in scalp EEG, we show that activity during late periods of the intervals was similar within and between modalities. Critically, we show that a multivariate pattern classifier was able to accurately predict the elapsed interval, even when trained on an interval marked by a stimulus of a different sensory modality. Taken together, our results suggest that, while there are differences in the processing of intervals marked by auditory and visual stimuli, they also share a common neural representation.

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