Towards a Theory of the Laminar Architecture of Cerebral Cortex: Computational Clues from the Visual System

One of the most exciting and open research frontiers in neuroscience is that of seeking to understand the functional roles of the layers of cerebral cortex. New experimental techniques for probing the laminar circuitry of cortex have recently been developed, opening up novel opportunities for investigating ho1v its six-layered architecture contributes to perception and cognition. The task of trying to interpret this complex structure can be facilitated by theoretical analyses of the types of computations that cortex is carrying out, and of how these might be implemented in specific cortical circuits. We have recently developed a detailed neural model of how the parvocellular stream of the visual cortex utilizes its feedforward, feedback, and horizontal interactions for purposes of visual filtering, attention, and perceptual grouping. This model, called LAMINART, shows how these perceptual processes relate to the mechanisms which ensure stable development of cortical circuits in the infant, and to the continued stability of learning in the adult. The present article reviews this laminar theory of visual cortex, considers how it may be generalized towards a more comprehensive theory that encompasses other cortical areas and cognitive processes, and shows how its laminar framework generates a variety of testable predictions.Defense Advanced Research Projects Agency and the Office of Naval Research (N00014-95-0409); National Science Foundation (IRI 94-01659); Office of Naval Research (N00014-92-1-1309, N00014-95-1-0657

Consciousness and Complexity or Conciousness and Resonance?

Defense Advanced Research Projects Agency and the Office of Naval Research (N00014-95-1-0409); National Science Foundation (IRI 97-20333); Office of Naval Research (N00014-95-1-0657, N00014-92-J-1309

Smoothness without Smoothing: Why Gaussian Naive Bayes is Not Naive for Multi-Subject Searchlight Studies

Spatial smoothness is helpful when averaging fMRI signals across multiple subjects, as it allows different subjects\u27 corresponding brain areas to be pooled together even if they are slightly misaligned. However, smoothing is usually not applied when performing multivoxel pattern-based analyses (MVPA), as it runs the risk of blurring away the information that fine-grained spatial patterns contain. It would therefore be desirable, if possible, to carry out pattern-based analyses which take unsmoothed data as their input but which produce smooth images as output. We show here that the Gaussian Naive Bayes (GNB) classifier does precisely this, when it is used in “searchlight” pattern-based analyses. We explain why this occurs, and illustrate the effect in real fMRI data. Moreover, we show that analyses using GNBs produce results at the multi-subject level which are statistically robust, neurally plausible, and which replicate across two independent data sets. By contrast, SVM classifiers applied to the same data do not generate a replication, even if the SVM-derived searchlight maps have smoothing applied to them. An additional advantage of GNB classifiers for searchlight analyses is that they are orders of magnitude faster to compute than more complex alternatives such as SVMs. Collectively, these results suggest that Gaussian Naive Bayes classifiers may be a highly non-naive choice for multi-subject pattern-based fMRI studies

Effects of Socioeconomic Status on Brain Development, and How Cognitive Neuroscience May Contribute to Levelling the Playing Field

The study of socioeconomic status (SES) and the brain finds itself in a circumstance unusual for Cognitive Neuroscience: large numbers of questions with both practical and scientific importance exist, but they are currently under-researched and ripe for investigation. This review aims to highlight these questions, to outline their potential significance, and to suggest routes by which they might be approached. Although remarkably few neural studies have been carried out so far, there exists a large literature of previous behavioural work. This behavioural research provides an invaluable guide for future neuroimaging work, but also poses an important challenge for it: how can we ensure that the neural data contributes predictive or diagnostic power over and above what can be derived from behaviour alone? We discuss some of the open mechanistic questions which Cognitive Neuroscience may have the power to illuminate, spanning areas including language, numerical cognition, stress, memory, and social influences on learning. These questions have obvious practical and societal significance, but they also bear directly on a set of longstanding questions in basic science: what are the environmental and neural factors which affect the acquisition and retention of declarative and nondeclarative skills? Perhaps the best opportunity for practical and theoretical interests to converge is in the study of interventions. Many interventions aimed at improving the cognitive development of low SES children are currently underway, but almost all are operating without either input from, or study by, the Cognitive Neuroscience community. Given that longitudinal intervention studies are very hard to set up, but can, with proper designs, be ideal tests of causal mechanisms, this area promises exciting opportunities for future research

Linking brain-wide multivoxel activation patterns to behaviour: Examples from language and math

A key goal of cognitive neuroscience is to find simple and direct connections between brain and behaviour. However, fMRI analysis typically involves choices between many possible options, with each choice potentially biasing any brain-behaviour correlations that emerge. Standard methods of fMRI analysis assess each voxel individually, but then face the problem of selection bias when combining those voxels into a region-of-interest, or ROI. Multivariate pattern-based fMRI analysis methods use classifiers to analyse multiple voxels together, but can also introduce selection bias via data-reduction steps as feature selection of voxels, pre-selecting activated regions, or principal components analysis. We show here that strong brain-behaviour links can be revealed without any voxel selection or data reduction, using just plain linear regression as a classifier applied to the whole brain at once, i.e. treating each entire brain volume as a single multi-voxel pattern. The brain-behaviour correlations emerged despite the fact that the classifier was not provided with any information at all about subjects\u27 behaviour, but instead was given only the neural data and its condition-labels. Surprisingly, more powerful classifiers such as a linear SVM and regularised logistic regression produce very similar results. We discuss some possible reasons why the very simple brain-wide linear regression model is able to find correlations with behaviour that are as strong as those obtained on the one hand from a specific ROI and on the other hand from more complex classifiers. In a manner which is unencumbered by arbitrary choices, our approach offers a method for investigating connections between brain and behaviour which is simple, rigorous and direct. © 2010 Elsevier Inc

Application of pattern analysis in understanding brain aging-associated symptoms and Alzheimer's disease using functional MRI

Thesis (Ph. D.)--University of Rochester. Department of Biomedical Engineering, 2018.Brain aging is accompanied with multiple symptoms including memory deficits, executive dysfunctions, depression, stress dysregulation, etc. These symptoms may result in serial adverse consequences, including cognitive decline, poor quality of life, reduced participation in intellectually beneficial activities, or mobility restriction. Moreover, abnormal aging that accompanies neuropathological changes can lead to dementia, such as Alzheimer’s disease (AD). Investigating how these brain-aging associated symptoms precede incident dementia, such as AD, is the first step in the early detection and prevention of AD for the aging population. The serial studies herein focused on the restingstate functional magnetic resonance imaging data as a way to understand the aging brain. Multivariate pattern analysis models were developed to characterize the profile of brain function that are related to aging-associated cognitive, behavioral, and psychological symptoms. The relationships between the brain function profile and AD pathological measurements were also examined across different clinical phenotypes. First, a set of brain regions whose amplitudes of low-frequency activation patterns were identified, distinguishing Supernormals (older adults with longitudinally excellent cognitive performance) from normal agers. Second, a shared neural circuit whose functional connectivity patterns was revealed, predicting multiple behavioral disturbances in patients with AD and amnestic mild cognitive impairment (aMCI), a group at high risk for AD. Third, the inter-network patterns whose correlations between functional connectomes before and after an acute stress task were compared. The correspondence between the two sets of inter-network patterns in stress regulation was disrupted in aMCI group when compared with cognitively normal controls. Taken together, the in-depth characterization of brain aging functional profile, based on the relevant symptoms, across the cognitive aging spectrum (from normal cognitive aging to dementia, or to excellent cognitive aging) help lay the foundation for developing computational model-based biomarkers for early detection of AD

Decoding semantic representations from functional near-infrared spectroscopy signals

This study uses representational similarity-based neural decoding to test whether semantic information elicited by words and pictures is encoded in functional near-infrared spectroscopy (fNIRS) data. In experiment 1, subjects passively viewed eight audiovisual word and picture stimuli for 15 min. Blood oxygen levels were measured using the Hitachi ETG-4000 fNIRS system with a posterior array over the occipital lobe and a left lateral array over the temporal lobe. Each participant’s response patterns were abstracted to representational similarity space and compared to the group average (excluding that subject, i.e., leave-one-out cross-validation) and to a distributional model of semantic representation. Mean accuracy for both decoding tasks significantly exceeded chance. In experiment 2, we compared three group-level models by averaging the similarity structures from sets of eight participants in each group. In these models, the posterior array was accurately decoded by the semantic model, while the lateral array was accurately decoded in the between-groups comparison. Our findings indicate that semantic representations are encoded in the fNIRS data, preserved across subjects, and decodable by an extrinsic representational model. These results are the first attempt to link the functional response pattern measured by fNIRS to higher-level representations of how words are related to each other