Reduced neural sensitivity to social stimuli in infants at risk for autism

In the hope of discovering early markers of autism, attention has recently turned to the study of infants at risk owing to being the younger siblings of children with autism. Because the condition is highly heritable, later-born siblings of diagnosed children are at substantially higher risk for developing autism or the broader autism phenotype than the general population. Currently, there are no strong predictors of autism in early infancy and diagnosis is not reliable until around 3 years of age. Because indicators of brain functioning may be sensitive predictors, and atypical social interactions are characteristic of the syndrome, we examined whether temporal lobe specialization for processing visual and auditory social stimuli during infancy differs in infants at risk. In a functional near-infrared spectroscopy study, infants aged 4–6 months at risk for autism showed less selective neural responses to social stimuli (auditory and visual) than low-risk controls. These group differences could not be attributed to overall levels of attention, developmental stage or chronological age. Our results provide the first demonstration of specific differences in localizable brain function within the first 6 months of life in a group of infants at risk for autism. Further, these differences closely resemble known patterns of neural atypicality in children and adults with autism. Future work will determine whether these differences in infant neural responses to social stimuli predict either later autism or the broader autism phenotype frequently seen in unaffected family members

Imaging plasticity and structure of cortical maps in cat and mouse visual cortex

The study reported in the first part of this thesis utilized optical imaging of intrinsic signals to visualize changes in orientation maps in cat visual cortex induced by pairing a visual stimulus with an intracortical electrical stimulation. We found that the direction of plasticity within orientation maps depends critically on the relative timing between visual and electrical stimulation on a millisecond time scale: a shift in orientation preference towards the paired orientation was observed if the cortex was first visually and then electrically stimulated. In contrast, the cortical response to the paired orientation was diminished if the electrical preceded the visual cortical stimulation. Spike-time-dependent plasticity has been observed in single cell studies; however, our results demonstrate an analogous effect at the systems level in the live animal. Thus, timing-dependent plasticity needs to be incorporated into our conception of cortical map development. While the pairing paradigm induced pronounced shifts in orientation preference, the general setup of the orientation preference map remained unaltered. In order to unravel potential factors contributing to this overall stability, we determined the distribution of plasticity across the cortical surface. We found that pinwheel centers, points were domains of all orientation meet, exhibited less plasticity than other regions of the orientation map. The resistance of pinwheel centers to changes in orientation preference may support maintenance of the general structure of the orientation map. The study that forms the second part employs optical imaging to visualize the retinotopy in mouse visual cortex. We were able to resolve the pattern of retinotopic activity with high precision and reliability in the primary visual cortex (area 17). Functional imaging of the position, size and shape of area 17 corresponded exactly to the location of this area in stained histological sections. The imaged maps were also confirmed with electrophysiological recordings. The retinotopic structure of area 17 showed very low inter-animal variability, thus allowing averaging maps across animals and therefore statistical analysis. These averaged maps greatly facilitated the identification of at least four extrastriate visual areas. In addition, we detected decreases in the intrinsic signal below baseline with a shape and location reminiscent of lateral inhibition. This decrease of the intrinsic signal was shown to be correlated with a decrease in neuronal firing rate below baseline. Both studies were facilitated by the development of a signal analysis technique (part III), which improves the quality of optical imaging data. Intrinsic signal fluctuations originating from blood vessels were minimized based on their correlation with the actual superficial blood vessel pattern. These fluctuation components were then extracted from images obtained during sensory stimulation. This method increases the reproducibility of functional maps from cat, rat, and mouse visual cortex significantly and might also be applied to high resolution imaging using voltage sensitve dyes or functional magnetic resonance

Representational similarity precedes category selectivity in the developing ventral visual pathway

© 2019 Many studies have investigated the development of face-, scene-, and body-selective regions in the ventral visual pathway. This work has primarily focused on comparing the size and univariate selectivity of these neural regions in children versus adults. In contrast, very few studies have investigated the developmental trajectory of more distributed activation patterns within and across neural regions. Here, we scanned both children (ages 5–7) and adults to test the hypothesis that distributed representational patterns arise before category selectivity (for faces, bodies, or scenes) in the ventral pathway. Consistent with this hypothesis, we found mature representational patterns in several ventral pathway regions (e.g., FFA, PPA, etc.), even in children who showed no hint of univariate selectivity. These results suggest that representational patterns emerge first in each region, perhaps forming a scaffold upon which univariate category selectivity can subsequently develop. More generally, our findings demonstrate an important dissociation between category selectivity and distributed response patterns, and raise questions about the relative roles of each in development and adult cognition

Development of visual cortical function in infant macaques: A BOLD fMRI study.

Functional brain development is not well understood. In the visual system, neurophysiological studies in nonhuman primates show quite mature neuronal properties near birth although visual function is itself quite immature and continues to develop over many months or years after birth. Our goal was to assess the relative development of two main visual processing streams, dorsal and ventral, using BOLD fMRI in an attempt to understand the global mechanisms that support the maturation of visual behavior. Seven infant macaque monkeys (Macaca mulatta) were repeatedly scanned, while anesthetized, over an age range of 102 to 1431 days. Large rotating checkerboard stimuli induced BOLD activation in visual cortices at early ages. Additionally we used static and dynamic Glass pattern stimuli to probe BOLD responses in primary visual cortex and two extrastriate areas: V4 and MT-V5. The resulting activations were analyzed with standard GLM and multivoxel pattern analysis (MVPA) approaches. We analyzed three contrasts: Glass pattern present/absent, static/dynamic Glass pattern presentation, and structured/random Glass pattern form. For both GLM and MVPA approaches, robust coherent BOLD activation appeared relatively late in comparison to the maturation of known neuronal properties and the development of behavioral sensitivity to Glass patterns. Robust differential activity to Glass pattern present/absent and dynamic/static stimulus presentation appeared first in V1, followed by V4 and MT-V5 at older ages; there was no reliable distinction between the two extrastriate areas. A similar pattern of results was obtained with the two analysis methods, although MVPA analysis showed reliable differential responses emerging at later ages than GLM. Although BOLD responses to large visual stimuli are detectable, our results with more refined stimuli indicate that global BOLD activity changes as behavioral performance matures. This reflects an hierarchical development of the visual pathways. Since fMRI BOLD reflects neural activity on a population level, our results indicate that, although individual neurons might be adult-like, a longer maturation process takes place on a population level

Evidence in Neuroimaging: Towards a Philosophy of Data Analysis

Neuroimaging technology is the most widely used tool to study human cognition. While originally a promising tool for mapping the content of cognitive theories onto the structures of the brain, recently developed tools for the analysis, handling and sharing of data have changed the theoretical landscape of cognitive neuroscience. Even with these advancements philosophical analyses of evidence in neuroimaging remain skeptical of the promise of neuroimaging technology. These views often treat the analysis techniques used to make sense of data produced in a neuroimaging experiment as one, attributing the inferential limitations of analysis pipelines to the technology as a whole. Situated against the neuroscientists own critical assessment of their methods and the limitations of those methods, this skepticism appears based on a misunderstanding of the role data analysis techniques play in neuroimaging research. My project picks up here, examining how data analysis techniques, such as pattern classification analysis, are used to assess the evidential value of neuroimaging data. The project takes the form of three papers. In the first I identify the use of multiple data analysis techniques as an important aspect of the data interpretation process that is overlooked by critics. In the second I develop an account of inferences in neuroimaging research that is sensitive to this use of data analysis techniques, arguing that interpreting neuroimaging data is a process of isolating and explaining a variety of data patterns. In the third I argue that the development and uptake of new techniques for analyzing data must be accompanied by changes in research practices and standards of evidence if they are to promote knowledge generation. My approach to this work is both traditionally philosophical, insofar as it involves reading and analyzing the work of philosophers and neuroscientists, and embedded insofar as most of the research was conducted while engaged in attending lab meetings and participating in the work of those scientists whose work is the object of my research

Representational Similarity Analysis – Connecting the Branches of Systems Neuroscience

A fundamental challenge for systems neuroscience is to quantitatively relate its three major branches of research: brain-activity measurement, behavioral measurement, and computational modeling. Using measured brain-activity patterns to evaluate computational network models is complicated by the need to define the correspondency between the units of the model and the channels of the brain-activity data, e.g., single-cell recordings or voxels from functional magnetic resonance imaging (fMRI). Similar correspondency problems complicate relating activity patterns between different modalities of brain-activity measurement (e.g., fMRI and invasive or scalp electrophysiology), and between subjects and species. In order to bridge these divides, we suggest abstracting from the activity patterns themselves and computing representational dissimilarity matrices (RDMs), which characterize the information carried by a given representation in a brain or model. Building on a rich psychological and mathematical literature on similarity analysis, we propose a new experimental and data-analytical framework called representational similarity analysis (RSA), in which multi-channel measures of neural activity are quantitatively related to each other and to computational theory and behavior by comparing RDMs. We demonstrate RSA by relating representations of visual objects as measured with fMRI in early visual cortex and the fusiform face area to computational models spanning a wide range of complexities. The RDMs are simultaneously related via second-level application of multidimensional scaling and tested using randomization and bootstrap techniques. We discuss the broad potential of RSA, including novel approaches to experimental design, and argue that these ideas, which have deep roots in psychology and neuroscience, will allow the integrated quantitative analysis of data from all three branches, thus contributing to a more unified systems neuroscience

Estimating neural signal dynamics in the human brain

Although brain imaging methods are highly effective for localizing the effects of neural activation throughout the human brain in terms of the blood oxygenation level dependent (BOLD) response, there is currently no way to estimate the underlying neural signal dynamics in generating the BOLD response in each local activation region (except for processes slower than the BOLD time course). Knowledge of the neural signal is critical if spatial mapping is to progress to the analysis of dynamic information flow through the cortical networks as the brain performs its tasks. We introduce an analytic approach that provides a new level of conceptualization and specificity in the study of brain processing by non-invasive methods. This technique allows us to use brain imaging methods to determine the dynamics of local neural population responses to their native temporal resolution throughout the human brain, with relatively narrow confidence intervals on many response properties. The ability to characterize local neural dynamics in the human brain represents a significant enhancement of brain imaging capabilities, with potential applications ranging from general cognitive studies to assessment of neuropathologies

The interaction between human vision and eye movements in health and disease

Human motor behaviour depends on the successful integration of vision and eye movements. Many studies have investigated neural correlates of visual processing in humans, but typically with the eyes stationary and fixated centrally. Similarly, many studies have sought to characterise which brain areas are responsible for oculomotor control, but generally in the absence of visual stimulation. The few studies to explicitly study the interaction between visual perception and eye movements suggest strong influences of both static and dynamic eye position on visual processing and modulation of oculomotor structures by properties of visual stimuli. However, the neural mechanisms underlying these interactions are poorly understood. This thesis uses a range of fMRI methodologies such as retinotopic mapping, multivariate analsyis techniques, dynamic causal modelling and ultra high resolution imaging to examine the interactions between the oculomotor and visual systems in the normal human brain. The results of the experiments presented in this thesis demonstrate that oculomotor behaviour has complex effects on activity in visual areas, while spatial properites of visual stimuli modify activity in oculomotor areas. Specifically, responses in the lateral geniculate nucleus and early cortical visual areas are modulated by saccadic eye movements (a process potentially mediated by the frontal eye fields) and by changes in static eye position. Additionally, responses in oculomotor structures such as the superior colliculus are biased for visual stimuli presented in the temporal rather than nasal hemifield. These findings reveal that although the visual and oculomotor systems are spatially segregated in the brain, they show a high degree of integration at the neural level. This is consistent with our everyday experience of the visual world where frequent eye movements do not lead to disruption of visual continuity and visual information is seamlessly transformed into motor behaviour

What Makes a Pattern? Matching Decoding Methods to Data in Multivariate Pattern Analysis

Research in neuroscience faces the challenge of integrating information across different spatial scales of brain function. A promising technique for harnessing information at a range of spatial scales is multivariate pattern analysis (MVPA) of functional magnetic resonance imaging (fMRI) data. While the prevalence of MVPA has increased dramatically in recent years, its typical implementations for classification of mental states utilize only a subset of the information encoded in local fMRI signals. We review published studies employing multivariate pattern classification since the technique’s introduction, which reveal an extensive focus on the improved detection power that linear classifiers provide over traditional analysis techniques. We demonstrate using simulations and a searchlight approach, however, that non-linear classifiers are capable of extracting distinct information about interactions within a local region. We conclude that for spatially localized analyses, such as searchlight and region of interest, multiple classification approaches should be compared in order to match fMRI analyses to the properties of local circuits