The missing pathway in current visuospatial processing models

Spatial configuration learning depends on the parahippocampal place area (PPA), a functionally heterogenous area which current visuo-spatial processing models place downstream from parietal cortex and area V4 of early visual cortex (EVC). Here, we present evidence for the medial occipital longitudinal tract (MOLT), a novel white matter pathway connecting the PPA with EVC earlier than V4. By using multimodal imaging and neuropsychological assessments in the unique case of Patient 1, we demonstrate that an occipital stroke sparing the PPA but disconnecting the MOLT can lead to chronic deficits in configuration learning. Further, through an advanced, data-driven clustering analysis of diffusion MRI structural connectivity in a large control cohort, we demonstrate that the PPA sits at the confluence of the MOLT and the parieto-medial-temporal branch of the dual-stream model. The MOLT may therefore support multi-stage learning of object configuration by allowing direct reciprocal exchange between the PPA and EVC.

Selective Responses to Faces, Scenes, and Bodies in the Ventral Visual Pathway of Infants

Three of the most robust functional landmarks in the human brain are the selective responses to faces in the fusiform face area (FFA), scenes in the parahippocampal place area (PPA), and bodies in the extrastriate body area (EBA). Are the selective responses of these regions present early in development, or do they require many years to develop? Prior evidence leaves this question unresolved. We designed a new 32-channel infant MRI coil, and collected high-quality functional magnetic resonance imaging (fMRI) data from infants (2-9 months of age) while they viewed stimuli from four conditions – faces, bodies, objects, and scenes. We find that infants have face-, scene-, and body-selective responses specifically localized to the FFA, PPA, and EBA, respectively, powerfully constraining accounts of cortical development.

Bilateral vestibulopathy causes selective deficits in recombining novel routes in real space

The differential impact of complete and incomplete bilateral vestibulopathy (BVP) on spatial orientation, visual exploration, and navigation-induced brain network activations is still under debate. In this study, 14 BVP patients (6 complete, 8 incomplete) and 14 age-matched healthy controls performed a navigation task requiring them to retrace familiar routes and recombine novel routes to find five items in real space. [18F]-fluorodeoxyglucose-PET was used to determine navigation-induced brain activations. Participants wore a gaze-controlled, head-fixed camera that recorded their visual exploration behaviour. Patients performed worse, when recombining novel routes (p < 0.001), whereas retracing of familiar routes was normal (p = 0.82). These deficits correlated with the severity of BVP. Patients exhibited higher gait fluctuations, spent less time at crossroads, and used a possible shortcut less often (p < 0.05). The right hippocampus and entorhinal cortex were less active and the bilateral parahippocampal place area more active during navigation in patients. Complete BVP showed reduced activations in the pontine brainstem, anterior thalamus, posterior insular, and retrosplenial cortex compared to incomplete BVP. The navigation-induced brain activation pattern in BVP is compatible with deficits in creating a mental representation of a novel environment. Residual vestibular function allows recruitment of brain areas involved in head direction signalling to support navigation.

Effects of age on goal-dependent modulation of episodic memory retrieval

Retrieval gating refers to the ability to modulate the retrieval of features of a single memory episode according to behavioral goals. Recent findings demonstrate that younger adults engage retrieval gating by attenuating the representation of task-irrelevant features of an episode. Here, we examine whether retrieval gating varies with age. Younger and older adults incidentally encoded words superimposed over scenes or scrambled backgrounds that were displayed in one of three spatial locations. Participants subsequently underwent fMRI as they completed two memory tasks: the background task, which tested memory for the word’s background, and the location task, testing memory for the word’s location. Employing univariate and multivariate approaches, we demonstrated that younger, but not older adults, exhibited attenuated reinstatement of scene information when it was goal-irrelevant (during the location task). Additionally, in younger adults only, the strength of scene reinstatement in the parahippocampal place area during the background task was related to item and source memory performance. Together, these findings point to an age-related decline in the ability to engage retrieval gating.

Unraveling Representations in Scene-selective Brain Regions Using Scene-Parsing Deep Neural Networks

Visual scene perception is mediated by a set of cortical regions that respond preferentially to images of scenes, including the occipital place area (OPA) and parahippocampal place area (PPA). However, the differential contribution of OPA and PPA to scene perception remains an open research question. In this study, we take a deep neural network (DNN)-based computational approach to investigate the differences in OPA and PPA function. In a first step, we search for a computational model that predicts fMRI responses to scenes in OPA and PPA well. We find that DNNs trained to predict scene components (e.g., wall, ceiling, floor) explain higher variance uniquely in OPA and PPA than a DNN trained to predict scene category (e.g., bathroom, kitchen, office). This result is robust across several DNN architectures. On this basis, we then determine whether particular scene components predicted by DNNs differentially account for unique variance in OPA and PPA. We find that variance in OPA responses uniquely explained by the navigation-related floor component is higher compared to the variance explained by the wall and ceiling components. In contrast, PPA responses are better explained by the combination of wall and floor, that is, scene components that together contain the structure and texture of the scene. This differential sensitivity to scene components suggests differential functions of OPA and PPA in scene processing. Moreover, our results further highlight the potential of the proposed computational approach as a general tool in the investigation of the neural basis of human scene perception.

Object representations in the human brain reflect the co-occurrence statistics of vision and language

ABSTRACTA central regularity of visual perception is the co-occurrence of objects in the natural environment. Here we use machine learning and fMRI to test the hypothesis that object co-occurrence statistics are encoded in the human visual system and elicited by the perception of individual objects. We identified low-dimensional representations that capture the latent statistical structure of object co-occurrence in real-world scenes, and we mapped these statistical representations onto voxelwise fMRI responses during object viewing. We found that cortical responses to single objects were predicted by the statistical ensembles in which they typically occur, and that this link between objects and their visual contexts was made most strongly in the anterior portion of the scene-selective parahippocampal place area. In contrast, a language-based statistical model of the co-occurrence of object names in written text predicted responses in neighboring regions of object-selective visual cortex. Together, these findings show that the sensory coding of objects in the human brain reflects the latent statistics of object context in visual and linguistic experience.

Unravelling Representations in Scene-selective Brain Regions Using Scene Parsing Deep Neural Networks

Visual scene perception is mediated by a set of cortical regions that respond preferentially to images of scenes, including the occipital place area (OPA) and parahippocampal place area (PPA). However, the differential contribution of OPA and PPA to scene perception remains an open research question. In this study, we take a deep neural network (DNN)-based computational approach to investigate the differences in OPA and PPA function. In a first step we search for a computational model that predicts fMRI responses to scenes in OPA and PPA well. We find that DNNs trained to predict scene components (e.g., wall, ceiling, floor) explain higher variance uniquely in OPA and PPA than a DNN trained to predict scene category (e.g., bathroom, kitchen, office). This result is robust across several DNN architectures. On this basis, we then determine whether particular scene components predicted by DNNs differentially account for unique variance in OPA and PPA. We find that variance in OPA responses uniquely explained by the navigation-related floor component is higher compared to the variance explained by the wall and ceiling components. In contrast, PPA responses are better explained by the combination of wall and floor, that is scene components that together contain the structure and texture of the scene. This differential sensitivity to scene components suggests differential functions of OPA and PPA in scene processing. Moreover, our results further highlight the potential of the proposed computational approach as a general tool in the investigation of the neural basis of human scene perception.

Neural differentiation is moderated by age in scene- but not face-selective cortical regions

The aging brain is characterized by neural dedifferentiation – an apparent decrease in the functional selectivity of category-selective cortical regions. Age-related reductions in neural differentiation have been proposed to play a causal role in cognitive aging. Recent findings suggest, however, that age-related dedifferentiation is not equally evident for all stimulus categories and, additionally, that the relationship between neural differentiation and cognitive performance is not moderated by age. In light of these findings, in the present experiment younger and older human adults (males and females) underwent fMRI as they studied words paired with images of scenes or faces prior to a subsequent memory task. Neural selectivity was measured in two scene-selective (parahippocampal place area and retrosplenial cortex) and two face-selective (fusiform and occipital face areas) regions of interest using both a univariate differentiation index and multivoxel pattern similarity analysis. Both methods provided highly convergent results which revealed evidence of age-related reductions in neural dedifferentiation in scene-selective but not face-selective cortical regions. Additionally, neural differentiation in the parahippocampal place area demonstrated a positive, age-invariant relationship with subsequent source memory performance (recall of the image category paired with each recognized test word). These findings extend prior findings suggesting that age-related neural dedifferentiation is not a ubiquitous phenomenon, and that the specificity of neural responses to scenes is predictive subsequent memory performance independently of age.Significance StatementIncreasing age is associated with reduced neural specificity in cortical regions that are selectively responsive to a given perceptual stimulus category (age-related neural dedifferentiation), a phenomenon which has been proposed to contribute to cognitive aging. Recent findings reveal that age-related neural dedifferentiation is not present for all types of visual stimulus categories, and the factors which determine when the phenomenon arises remain unclear. Here, we demonstrate that scene- but not face-selective cortical regions exhibit age-related neural dedifferentiation during an attentionally-demanding task. Additionally, we report that higher neural selectivity in the scene-selective ‘parahippocampal place area’ is associated with better memory performance after controlling for variance associated with age group, adding to evidence that neural differentiation impacts cognition across the adult lifespan.

Individual face- and house-related eye movement patterns distinctively activate FFA and PPA

We investigated if the fusiform face area (FFA) and the parahippocampal place area (PPA) contain a representation of fixation sequences that are typically used when looking at faces or houses. Here, we instructed observers to follow a dot presented on a uniform background. The dot’s movements represented gaze paths acquired separately from observers looking at face or house pictures. Even when gaze dispersion differences were controlled, face- and house-associated gaze patterns could be discriminated by fMRI multivariate pattern analysis in FFA and PPA, more so for the current observer’s own gazes than for another observer’s gaze. The discrimination of the observer’s own gaze patterns was not observed in early visual areas (V1 – V4) or superior parietal lobule and frontal eye fields. These findings indicate a link between perception and action—the complex gaze patterns that are used to explore faces and houses—in the FFA and PPA.