PII S0361-9230(00)00435-4 Interpreting PET and fMRI measures of functional neural activity: The effects of synaptic inhibition on cortical activation in human imaging studies

ABSTRACT: Human brain imaging methods such as postiron emission tomography and functional magnetic resonance imaging have recently achieved widespread use in the study of both normal cognitive processes and neurological disorders. While many of these studies have begun to yield important insights into human brain function, the relationship between these measurements and the underlying neuronal activity is still not well understood. One open question is how neuronal inhibition is reflected in these imaging results. In this paper, we describe how large-scale modeling can be used to address this question. Specifically, we identify three factors that may play a role in how inhibition affects imaging results: (1) local connectivity; (2) context; and (3) type of inhibitory connection. Simulation results are presented that show how the interaction among these three factors can explain seemingly contradictory experimental results. The modeling suggests that neuronal inhibition can raise brain imaging measures if there is either low local excitatory recurrence or if the region is not otherwise being driven by excitation. Conversely, with high recurrence or actively driven excitation, inhibition can lower observed values

A Behavior-to-Brain Map

Development and study of large-scale computational models of the human brain, and their use to simulate cognitive functions, is becoming increasingly feasible. However, construction of integrated models that span multiple cognitive systems (language, memory, reasoning, learning, sensorimotor control, executive functions, etc.) is currently inhibited by the absence of any systematic catalog of experimentally documented associations between specific behavioral functions and specific brain regions. In this report we provide a prototype for such a mapping in the form of a semantic network. While preliminary and not comprehensive, the results presented here support the idea that an online mapping between cognitive function and cortical/subcortical structures can be developed as a useful reference source

Working Memory in Attention Deficit/Hyperactivity Disorder is Characterized by a Lack of Specialization of Brain Function

Working memory impairments are frequent in Attention Deficit/Hyperactivity Disorder (ADHD) and create problems along numerous functional dimensions. The present study utilized the Visual Serial Addition Task (VSAT) and functional magnetic resonance imaging (fMRI) to explore working memory processes in thirteen typically developing (TD) control and thirteen children with ADHD, Combined type. Analysis of Variance (ANOVA) was used to examine both main effects and interactions. Working memory-specific activity was found in TD children in the bilateral prefrontal cortex. In contrast the within-group map in ADHD did not reveal any working-memory specific regions. Main effects of condition suggested that the right middle frontal gyrus (BA6) and the right precuneus were engaged by both groups during working memory processing. Group differences were driven by significantly greater, non-working memory-specific, activation in the ADHD relative to TD group in the bilateral insula extending into basal ganglia and the medial prefrontal cortex. A region of interest analysis revealed a region in left middle frontal gyrus that was more active during working memory in TD controls. Thus, only the TD group appeared to display working memory-modulated brain activation. In conclusion, children with ADHD demonstrated reduced working memory task specific brain activation in comparison to their peers. These data suggest inefficiency in functional recruitment by individuals with ADHD represented by a poor match between task demands and appropriate levels of brain activity

Development of a Large-Scale Integrated Neurocognitive Architecture Part 1: Conceptual Framework

The idea of creating a general purpose machine intelligence that captures many of the features of human cognition goes back at least to the earliest days of artificial intelligence and neural computation. In spite of more than a half-century of research on this issue, there is currently no existing approach to machine intelligence that comes close to providing a powerful, general-purpose human-level intelligence. However, substantial progress made during recent years in neural computation, high performance computing, neuroscience and cognitive science suggests that a renewed effort to produce a general purpose and adaptive machine intelligence is timely, likely to yield qualitatively more powerful approaches to machine intelligence than those currently existing, and certain to lead to substantial progress in cognitive science, AI and neural computation. In this report, we outline a conceptual framework for the long-term development of a large-scale machine intelligence that is based on the modular organization, dynamics and plasticity of the human brain. Some basic design principles are presented along with a review of some of the relevant existing knowledge about the neurobiological basis of cognition. Three intermediate-scale prototypes for parts of a larger system are successfully implemented, providing support for the effectiveness of several of the principles in our framework. We conclude that a human-competitive neuromorphic system for machine intelligence is a viable long- term goal, but that for the short term, substantial integration with more standard symbolic methods as well as substantial research will be needed to make this goal achievable

Development of a Large-Scale Integrated Neurocognitive Architecture - Part 2: Design and Architecture

In Part 1 of this report, we outlined a framework for creating an intelligent agent based upon modeling the large-scale functionality of the human brain. Building on those results, we begin Part 2 by specifying the behavioral requirements of a large-scale neurocognitive architecture. The core of our long-term approach remains focused on creating a network of neuromorphic regions that provide the mechanisms needed to meet these requirements. However, for the short term of the next few years, it is likely that optimal results will be obtained by using a hybrid design that also includes symbolic methods from AI/cognitive science and control processes from the field of artificial life. We accordingly propose a three-tiered architecture that integrates these different methods, and describe an ongoing computational study of a prototype 'mini-Roboscout' based on this architecture. We also examine the implications of some non-standard computational methods for developing a neurocognitive agent. This examination included computational experiments assessing the effectiveness of genetic programming as a design tool for recurrent neural networks for sequence processing, and experiments measuring the speed-up obtained for adaptive neural networks when they are executed on a graphical processing unit (GPU) rather than a conventional CPU. We conclude that the implementation of a large-scale neurocognitive architecture is feasible, and outline a roadmap for achieving this goal

An examination of the language construct in NIMH's research domain criteria:Time for reconceptualization!

The National Institute of Mental Health’s Research Domain Criteria (RDoC) Initiative “calls for the development of new ways of classifying psychopathology based on dimensions of observable behavior.” As aresult of this ambitious initiative, language has been identifi d as an independent construct in the RDoC matrix. In this article, we frame language within an evolutionary and neuro- psychological context and discuss some of the limitations to the current measurements of language. Findings from genomics and the neuroimaging of performance during language tasks are dis- cussed in relation to serious mental illness and within the context of caveats regarding measuring language. Indeed, the data collec- tion and analysis methods employed to assay language have been both aided and constrained by the available technologies, methodologies, and conceptual defi Consequently, differ- ent fields of language research show inconsistent defi s of language that have become increasingly broad over time. Individ- ually, they have also shown significant improvements in conceptual resolution, aswell as inexperimental and analytic techniques. More recently, language research has embraced collaborations across disciplines, notably neuroscience, cognitive science, and computa- tional linguistics and has ultimately re-defi classical ideas of language. As we move forward, the new models of language with their remarkably multifaceted constructs force a re-examination of the NIMH RDoC conceptualization of language and thus the neuroscience and genetics underlying this concept

A neuroanatomically grounded Hebbian-learning model of attention–language interactions in the human brain

Meaningful familiar stimuli and senseless unknown materials lead to different patterns of brain activation. A late major neurophysiological response indexing ‘sense’ is the negative component of event-related potential peaking at around 400 ms (N400), an event-related potential that emerges in attention-demanding tasks and is larger for senseless materials (e.g. meaningless pseudowords) than for matched meaningful stimuli (words). However, the mismatch negativity (latency 100–250 ms), an early automatic brain response elicited under distraction, is larger to words than to pseudowords, thus exhibiting the opposite pattern to that seen for the N400. So far, no theoretical account has been able to reconcile and explain these findings by means of a single, mechanistic neural model. We implemented a neuroanatomically grounded neural network model of the left perisylvian language cortex and simulated: (i) brain processes of early language acquisition and (ii) cortical responses to familiar word and senseless pseudoword stimuli. We found that variation of the area-specific inhibition (the model correlate of attention) modulated the simulated brain response to words and pseudowords, producing either an N400- or a mismatch negativity-like response depending on the amount of inhibition (i.e. available attentional resources). Our model: (i) provides a unifying explanatory account, at cortical level, of experimental observations that, so far, had not been given a coherent interpretation within a single framework; (ii) demonstrates the viability of purely Hebbian, associative learning in a multilayered neural network architecture; and (iii) makes clear predictions on the effects of attention on latency and magnitude of event-related potentials to lexical items. Such predictions have been confirmed by recent experimental evidence