Early Developmental Trajectories of Functional Connectivity Along the Visual Pathways in Rhesus Monkeys.

Early social interactions shape the development of social behavior, although the critical periods or the underlying neurodevelopmental processes are not completely understood. Here, we studied the developmental changes in neural pathways underlying visual social engagement in the translational rhesus monkey model. Changes in functional connectivity (FC) along the ventral object and motion pathways and the dorsal attention/visuo-spatial pathways were studied longitudinally using resting-state functional MRI in infant rhesus monkeys, from birth through early weaning (3 months), given the socioemotional changes experienced during this period. Our results revealed that (1) maturation along the visual pathways proceeds in a caudo-rostral progression with primary visual areas (V1-V3) showing strong FC as early as 2 weeks of age, whereas higher-order visual and attentional areas (e.g., MT-AST, LIP-FEF) show weak FC; (2) functional changes were pathway-specific (e.g., robust FC increases detected in the most anterior aspect of the object pathway (TE-AMY), but FC remained weak in the other pathways (e.g., AST-AMY)); (3) FC matures similarly in both right and left hemispheres. Our findings suggest that visual pathways in infant macaques undergo selective remodeling during the first 3 months of life, likely regulated by early social interactions and supporting the transition to independence from the mother.

Subtyping cognitive profiles in Autism Spectrum Disorder using a Functional Random Forest algorithm.

DSM-5 Autism Spectrum Disorder (ASD) comprises a set of neurodevelopmental disorders characterized by deficits in social communication and interaction and repetitive behaviors or restricted interests, and may both affect and be affected by multiple cognitive mechanisms. This study attempts to identify and characterize cognitive subtypes within the ASD population using our Functional Random Forest (FRF) machine learning classification model. This model trained a traditional random forest model on measures from seven tasks that reflect multiple levels of information processing. 47 ASD diagnosed and 58 typically developing (TD) children between the ages of 9 and 13 participated in this study. Our RF model was 72.7% accurate, with 80.7% specificity and 63.1% sensitivity. Using the random forest model, the FRF then measures the proximity of each subject to every other subject, generating a distance matrix between participants. This matrix is then used in a community detection algorithm to identify subgroups within the ASD and TD groups, and revealed 3 ASD and 4 TD putative subgroups with unique behavioral profiles. We then examined differences in functional brain systems between diagnostic groups and putative subgroups using resting-state functional connectivity magnetic resonance imaging (rsfcMRI). Chi-square tests revealed a significantly greater number of between group differences (p < .05) within the cingulo-opercular, visual, and default systems as well as differences in inter-system connections in the somato-motor, dorsal attention, and subcortical systems. Many of these differences were primarily driven by specific subgroups suggesting that our method could potentially parse the variation in brain mechanisms affected by ASD.

Firing rate control of a neuron using a linear proportional-integral controller.

Some electrophysiology experiments require periodically firing neurons. One example is when measuring a neuron's phase response curve (PRC) where a neuron is stimulated with a synaptic input and the perturbation in the neuron's period is measured as a function of when the stimulus is applied. However, even regular spiking cells have considerable variations in their period. These variations can be categorized into two types: jitter, which characterizes the rapid changes in interspike intervals (ISIs) from spike to spike, and drift, which is a slow change in firing rate over seconds. The jitter is removed by averaging the phase advance of a synaptic input applied at a particular phase several times. The drift over long time scales results in a systematic change in the period over the duration of the experiment which cannot be removed by averaging. To compensate for the drift of the neuron over minutes, we designed a linear proportional-integral (PI) controller to slowly adjust the applied current to a neuron to maintain the average firing rate at a desired ISI. The parameters of the controller were calculated based on a first-order discrete model to describe the relationship between ISI and current. The algorithm is demonstrated on pyramidal cells in the hippocampal formation showing ISIs from the neuron in an open loop (constant applied current) and a closed loop (current adjusted by a spike rate controller). The advantages of using the controller can be summarized as: (1) there is a reduction in the transient time to reach a desired ISI, (2) the drift in the ISI is removed allowing for long experiments at a desired spiking rate and (3) the variance is diminished by removing the slow drift. Furthermore, we implemented an auto-tuning algorithm that estimates in real time the coefficients for each clamped neuron. We also show how the controller can improve the PRC estimation. The program runs on Real-Time eXperiment Interface (RTXI), which is Linux-based software for real-time data acquisition and control applications.