Functional connectivity of the human face network exhibits right hemispheric lateralization from infancy to adulthood.

Adults typically exhibit right hemispheric dominance in the processing of faces. In this cross-sectional study, we investigated age-dependent changes in face processing lateralization from infancy to adulthood (1-48 years old; N = 194). We co-registered anatomical and resting state functional Magnetic Resonance Imaging (fMRI) scans of toddlers, children, adolescents, and adults into a common space and examined functional connectivity across the face, as well as place, and object-selective regions identified in adults. As expected, functional connectivity between core face-selective regions was stronger in the right compared to the left hemisphere in adults. Most importantly, the same lateralization was evident in all other age groups (infants, children, adolescents) and appeared only in face-selective regions, and not in place or object-selective regions. These findings suggest that the physiological development of face-selective brain areas may differ from that of object and place-selective areas. Specifically, the functional connectivity of the core-face selective regions exhibits rightward lateralization from infancy, years before these areas develop mature face-selective responses.

From a deep learning model back to the brain-Identifying regional predictors and their relation to aging.

We present a Deep Learning framework for the prediction of chronological age from structural magnetic resonance imaging scans. Previous findings associate increased brain age with neurodegenerative diseases and higher mortality rates. However, the importance of brain age prediction goes beyond serving as biomarkers for neurological disorders. Specifically, utilizing convolutional neural network (CNN) analysis to identify brain regions contributing to the prediction can shed light on the complex multivariate process of brain aging. Previous work examined methods to attribute pixel/voxel-wise contributions to the prediction in a single image, resulting in "explanation maps" that were found noisy and unreliable. To address this problem, we developed an inference scheme for combining these maps across subjects, thus creating a population-based, rather than a subject-specific map. We applied this method to a CNN ensemble trained on predicting subjects' age from raw T1 brain images in a lifespan sample of 10,176 subjects. Evaluating the model on an untouched test set resulted in mean absolute error of 3.07 years and a correlation between chronological and predicted age of r = 0.98. Using the inference method, we revealed that cavities containing cerebrospinal fluid, previously found as general atrophy markers, had the highest contribution for age prediction. Comparing maps derived from different models within the ensemble allowed to assess differences and similarities in brain regions utilized by the model. We showed that this method substantially increased the replicability of explanation maps, converged with results from voxel-based morphometry age studies and highlighted brain regions whose volumetric variability correlated the most with the prediction error.

Mapping higher-order relations between brain structure and function with embedded vector representations of connectomes.

Connectomics generates comprehensive maps of brain networks, represented as nodes and their pairwise connections. The functional roles of nodes are defined by their direct and indirect connectivity with the rest of the network. However, the network context is not directly accessible at the level of individual nodes. Similar problems in language processing have been addressed with algorithms such as word2vec that create embeddings of words and their relations in a meaningful low-dimensional vector space. Here we apply this approach to create embedded vector representations of brain networks or connectome embeddings (CE). CE can characterize correspondence relations among brain regions, and can be used to infer links that are lacking from the original structural diffusion imaging, e.g., inter-hemispheric homotopic connections. Moreover, we construct predictive deep models of functional and structural connectivity, and simulate network-wide lesion effects using the face processing system as our application domain. We suggest that CE offers a novel approach to revealing relations between connectome structure and function.

Holistic face representation is highly orientation-specific.

It has long been argued that face processing requires disproportionate reliance on holistic processing (HP), relative to that required for nonface object recognition. Nevertheless, whether the holistic nature of face perception is achieved via a unique internal representation or by the employment of an automated attention mechanism is still debated. Previous studies had used the face inversion effect (FIE), a unique face-processing marker, or the face composite task, a gold standard paradigm measuring holistic processing, to examine the validity of these two different hypotheses, with some studies combining the two paradigms. However, the results of such studies remain inconclusive, particularly pertaining to the issue of the two proposed HP mechanisms-an internal representation as opposed to an automated attention mechanism. Here, using the complete composite paradigm design, we aimed to examine whether face rotation yields a nonlinear or a linear drop in HP, thus supporting an account that face processing is based either on an orientation-dependent internal representation or on automated attention. Our results reveal that even a relatively small perturbation in face orientation (30 deg away from upright) already causes a sharp decline in HP. These findings support the face internal representation hypothesis and the notion that the holistic processing of faces is highly orientation-specific.

Adolescent Tuning of Association Cortex in Human Structural Brain Networks.

Motivated by prior data on local cortical shrinkage and intracortical myelination, we predicted age-related changes in topological organization of cortical structural networks during adolescence. We estimated structural correlation from magnetic resonance imaging measures of cortical thickness at 308 regions in a sample of N = 297 healthy participants, aged 14-24 years. We used a novel sliding-window analysis to measure age-related changes in network attributes globally, locally and in the context of several community partitions of the network. We found that the strength of structural correlation generally decreased as a function of age. Association cortical regions demonstrated a sharp decrease in nodal degree (hubness) from 14 years, reaching a minimum at approximately 19 years, and then levelling off or even slightly increasing until 24 years. Greater and more prolonged age-related changes in degree of cortical regions within the brain network were associated with faster rates of adolescent cortical myelination and shrinkage. The brain regions that demonstrated the greatest age-related changes were concentrated within prefrontal modules. We conclude that human adolescence is associated with biologically plausible changes in structural imaging markers of brain network organization, consistent with the concept of tuning or consolidating anatomical connectivity between frontal cortex and the rest of the connectome.

Altered topology of neural circuits in congenital prosopagnosia.

Using a novel, fMRI-based inter-subject functional correlation (ISFC) approach, which isolates stimulus-locked inter-regional correlation patterns, we compared the cortical topology of the neural circuit for face processing in participants with an impairment in face recognition, congenital prosopagnosia (CP), and matched controls. Whereas the anterior temporal lobe served as the major network hub for face processing in controls, this was not the case for the CPs. Instead, this group evinced hyper-connectivity in posterior regions of the visual cortex, mostly associated with the lateral occipital and the inferior temporal cortices. Moreover, the extent of this hyper-connectivity was correlated with the face recognition deficit. These results offer new insights into the perturbed cortical topology in CP, which may serve as the underlying neural basis of the behavioral deficits typical of this disorder. The approach adopted here has the potential to uncover altered topologies in other neurodevelopmental disorders, as well.

Stimulus Dependent Dynamic Reorganization of the Human Face Processing Network.

Using the "face inversion effect", a hallmark of face perception, we examined network mechanisms supporting face representation by tracking functional magnetic resonance imaging (fMRI) stimulus-dependent dynamic functional connectivity within and between brain networks associated with the processing of upright and inverted faces. We developed a novel approach adapting the general linear model (GLM) framework classically used for univariate fMRI analysis to capture stimulus-dependent fMRI dynamic connectivity of the face network. We show that under the face inversion manipulation, the face and non-face networks have complementary roles that are evident in their stimulus-dependent dynamic connectivity patterns as assessed by network decomposition into components or communities. Moreover, we show that connectivity patterns are associated with the behavioral face inversion effect. Thus, we establish "a network-level signature" of the face inversion effect and demonstrate how a simple physical transformation of the face stimulus induces a dramatic functional reorganization across related brain networks. Finally, we suggest that the dynamic GLM network analysis approach, developed here for the face network, provides a general framework for modeling the dynamics of blocked stimulus-dependent connectivity experimental designs and hence can be applied to a host of neuroimaging studies.

Sensitivity to object impossibility in the human visual cortex: evidence from functional connectivity.

Processing spatial configuration is a fundamental requirement for object recognition. Using fMRI, the neural basis underlying this ability was examined while human participants viewed possible and visually similar, but spatially impossible, objects presented for either long or short exposure duration. Response profiles in object-selective cortical regions exhibited sensitivity to object possibility, but only for the long exposure duration. Contrary, functional connectivity, indexed by the pairwise correlations between activation profiles across ROIs, revealed sensitivity to possibility, evident in enhanced correlations for impossible compared with possible objects. Such sensitivity was found even following a brief exposure duration, which allowed only minimal awareness of possibility. Importantly, this sensitivity was correlated with participants' general spatial ability as assessed by an independent neuropsychological test. These results suggest that the visual system is highly susceptible to objects' 3-D structural information even with minimal perceptual awareness. Such sensitivity is captured at the level of functional connectivity between object-selective regions, rather than the absolute level of within-region activity, implicating the role of interregional synchronization in the representation of objects' 3-D structure.

Branch retinal vein occlusion in Churg-Strauss syndrome.

The case is reported of a 64-year-old man who was diagnosed as having Churg-Strauss syndrome associated with branch retinal vein occlusion, without accompanying retinal vasculitis. It was assumed that the blood thrombocytosis caused a hypercoagulable state and thromboembolism leading to the branch retinal vein occlusion.

High postdialysis urea rebound can predict intradialytic increase in intraocular pressure in dialysis patients with lowered intradialytic hemoconcentration.

BACKGROUND: Intradialytic (ID) decrease in intraocular pressure (IOP) parallel to ultrafiltration-induced hemoconcentration has been recently reported. However, exacerbation of glaucoma in hemodialysis (HD) patients during HD sessions is occasionally observed. Postdialysis urea rebound (PDUR) is induced by the lag in urea removal from the cells to urea removal from the extracellular fluid, which when increased can result in ID drag of water to intracellular compartment. It is our hypothesis that similar lag in urea removal from ocular compartments may also be reflected by PDUR, and may induce drag of water into ocular compartments counteracting the effect of hemoconcentration. Our assumption was, therefore, that PDUR might predict ID increase in IOP. METHODS: IOP, serum urea and hematocrit levels were measured at the start, end and 1 h postdialysis, in 19 chronic HD patients with normal IOP. RESULTS: PDUR was positively correlated with mean (both eyes) ID changes in IOP (MIDIOP) (r = 0.5, p = 0.03) and % MIDIOP (r = 0.55, p = 0.02). ID increase in IOP was observed only in the 7 patients with relatively higher PDUR (> or = 9 mg%), who had also a relatively lower % ID change in Hct (<8%). MIDIOP was negatively correlated with % ID changes in Hct (r = -0.65, p = 0.03) in the 12 patients with PDUR > or = 9 mg, and positively correlated with PDUR (r = 0.57, p = 0.03) in the 14 patients with % ID change in Hct <8%. CONCLUSION: High PDUR may predict susceptibility to ID increase in IOP in patients with lowered ID hemoconcentration.