Functional and connectivity changes during working memory in Huntington’s disease: 18 month longitudinal data from the IMAGE-HD study

Background

This study aimed to characterize, for the first time, 18 month longitudinal changes in both functional activation and functional connectivity during working memory in premanifest Huntington’s disease (pre-HD) and symptomatic HD (symp-HD).

Methods

Functional magnetic resonance imaging (fMRI) was used to investigate longitudinal changes in neuronal activity during working memory performance via an N-BACK task (0-BACK and 1-BACK) in 27 pre-HD, 17 symp-HD, and 23 control participants. Whole-brain analysis of activation and region-of-interest analysis of functional connectivity was applied to longitudinal fMRI data collected at baseline and 18 months follow-up.

Results

Compared with controls, the pre-HD group showed significantly increased activation longitudinally during 1-BACK versus 0-BACK in the lateral and medial prefrontal, anterior cingulate, primary motor, and temporal areas cortically, and caudate and putamen subcortically. Pre-HD far from onset, compared with controls, showed further longitudinal increases in the right and left dorsolateral prefrontal cortex (DLPFC). Longitudinal increased activation in anterior cingulate and medial primary motor areas were associated with disease burden in the pre-HD group. Moreover, in pre-HD increased activation over time in primary motor and putamen regions were associated with average response time during 1-BACK performance. During 1-BACK, functional connectivity between the right DLPFC and posterior parietal, anterior cingulate, and caudate was significantly reduced over 18 months only in the pre-HD group.

Conclusions

Longitudinal reductions in connectivity over 18 months may represent an early signature of cortico-cortical and cortico-striatal functional disconnectivity in pre-HD, whereas the concomitant increased cortical and subcortical activation may reflect a compensatory response to the demands for cognitive resources required during task performance. Our findings demonstrate that functional imaging modalities have the potential to serve as sensitive methods for the assessment of cortical and subcortical responses to future treatment measures.     

The regression trunk approach to discover treatment covariate interaction

The regression trunk approach (RTA) is an integration of regression trees and multiple linear regression analysis. In this paper RTA is used to discover treatment covariate interactions, in the regression of one continuous variable on a treatment variable withmultiple covariates. The performance of RTA is compared to the classical method of forward stepwise regression. The results of two simulation studies, in which the true interactions are modeled as threshold interactions, show that RTA detects the interactions in a higher number of cases (82.3% in the first simulation study, and 52.3% in the second) than stepwise regression (56.5% and 20.5%). In a real data example the final RTA model has a higher cross-validated variance-accounted-for (29.8%) than the stepwise regression model (12.5%). All of these results indicate that RTA is a promising alternative method for demonstrating differential effectiveness of treatments. Supported by The Netherlands Organization for Scientific Research (NWO), grant 030-56403 to Jacqueline Meulman for the Pioneer project “Subject-Oriented Multivariate Analysis,” and grant 451-02-058 to Elise Dusseldorp for the Veni project “Modeling interaction effects as small trees in regression and classification.” We thank Bram Bakker for making the data of his doctoral dissertation available, and David Hand, Jerome Friedman, Bart Jan van Os, Philip Spinhoven, and the anonymous reviewers for their helpful suggestions. Special thanks are due to Lawrence Hubert.     

Categorical invariance and structural complexity in human concept learning

An alternative account of human concept learning based on an invariance measure of the categorical stimulus is proposed. The categorical invariance model (CIM) characterizes the degree of structural complexity of a Boolean category as a function of its inherent degree of invariance and its cardinality or size. To do this we introduce a mathematical framework based on the notion of a Boolean differential operator on Boolean categories that generates the degrees of invariance (i.e., logical manifold) of the category in respect to its dimensions. Using this framework, we propose that the structural complexity of a Boolean category is indirectly proportional to its degree of categorical invariance and directly proportional to its cardinality or size. Consequently, complexity and invariance notions are formally unified to account for concept learning difficulty. Beyond developing the above unifying mathematical framework, the CIM is significant in that: (1) it precisely predicts the key learning difficulty ordering of the SHJ [Shepard, R. N., Hovland, C. L., & Jenkins, H. M. (1961). Learning and memorization of classifications. Psychological Monographs: General and Applied, 75(13), 1-42] Boolean category types consisting of three binary dimensions and four positive examples; (2) it is, in general, a good quantitative predictor of the degree of learning difficulty of a large class of categories (in particular, the 41 category types studied by Feldman [Feldman, J. (2000). Minimization of Boolean complexity in human concept learning. Nature, 407, 630-633]); (3) it is, in general, a good quantitative predictor of parity effects for this large class of categories; (4) it does all of the above without free parameters; and (5) it is cognitively plausible (e.g., cognitively tractable).     

Contemporary theories of 1/f noise in motor control

1/f noise has been discovered in a number of time series collected in psychological and behavioral experiments. This ubiquitous phenomenon has been ignored for a long time and classical models were not designed for accounting for these long-range correlations. The aim of this paper is to present and discuss contrasted theoretical perspectives on 1/f noise, in order to provide a comprehensive overview of current debates in this domain. In a first part, we propose a formal definition of the phenomenon of 1/f noise, and we present some commonly used methods for measuring long-range correlations in time series. In a second part, we develop a theoretical position that considers 1/f noise as the hallmark of system complexity. From this point of view, 1/f noise emerges from the coordination of the many elements that compose the system. In a third part, we present a theoretical counterpoint suggesting that 1/f noise could emerge from localized sources within the system. In conclusion, we try to draw some lines of reasoning for going beyond the opposition between these two approaches.     

Effects of attention on dichotic listening in elderly and patients with dementia of the Alzheimer type

This article presents an overview of our studies in elderly and Alzheimer patients employing Kimura's dichotic digits paradigm as a measure for left hemispheric predominance for processing language stimuli. In addition to structural brain mechanisms, we demonstrated that attention modulates the direction and degree of ear asymmetry in dichotic listening. Elderly showed increasingly more difficulties focusing attention on the left ear (LE) with advancing age. Alzheimer patients showed severe deficits to allocate attention to the LE, which could result in a right ear advantage. These results may be attributed to a breakdown of the cortical attentional network which is mediated by frontal (inhibitory control of attention) and parietal regions (spatial attention and 'disengagement processes'). Both interhemispheric disconnectivity (callosal atrophy) and intrahemispheric disconnectivity (subcortical white matter lesions) appear to be important factors contributing to these findings.     

Postural and focal inhibition of voluntary movements prepared under various temporal constraints

Large disturbances arising from the moving segments (focal movement) are commonly counteracted by anticipatory postural adjustments (APAs). The aim of this study was to investigate how APAs – focal movement coordination changes under temporal constraint. Ten subjects were instructed to perform an arm raising movement in the reactive (simple reaction time) and predictive (anticipation–coincidence) tasks. A stop paradigm was applied to reveal the coordination. On some unexpected trials, a stop signal indicated to inhibit the movement; it occurred randomly at different delays (SOA) relative to the go signal in the reactive task, and at different delays prior to the focal response initiation in the predictive task. Focal movement was measured using contact switch, accelerometer and EMG from the anterior deltoid. APAs were quantified using centre of pressure displacement and EMG from three postural muscles. The inhibition rates as a function of the SOA produce psychometric functions where the bi-serial points allow the moment of the motor "command release" to be estimated. Repeated measures ANOVAs showed that APAs and focal movement were closely timed in the reactive task but distinct in a predictive task. Data were discussed according to two different models of coordination: (1) hierarchical model where APAs and focal movement are the results of a single motor command; (2) parallel model implying two independent motor commands. The data clearly favor the parallel model when the temporal constraint is low. The stop paradigm appears as a promising technique to explore APAs – focal movement coordination.     

The learning of 90° continuous relative phase with and without Lissajous feedback: external and internally generated bimanual coordination

Results from recent experiments (e.g., Kovacs, Buchanan, & Shea, 2009a–b, 2010a,b) suggest that when salient visual information is presented using Lissajous plots bimanual coordination patterns typically thought to be very difficult to perform without extensive practice can be performed with remarkably low relative phase error and variability with 5 min or less of practice. However, when this feedback is removed, performance deteriorates. The purpose of the present experiment was to determine if reducing the frequency of feedback presentation will decrease the participant's reliance on the feedback and will facilitate the development of an internal representation capable of sustaining performance when the Lissajous feedback is withdrawn. The results demonstrated that reduced frequency Lissajous feedback results in very effective bimanual coordination performance on tests with Lissajous feedback available and when feedback is withdrawn. Taken together the present experiments add to the growing literature that supports the notion that salient perceptual information can override some aspects of the system's intrinsic dynamics typically linked to motor output control. Additionally, the present results suggest that the learning of both externally and internally driven bimanual coordination is facilitated by providing reduced frequency Lissajous feedback.     

I see what you're saying: the integration of complex speech and scenes during language comprehension

The effect of language-driven eye movements in a visual scene with concurrent speech was examined using complex linguistic stimuli and complex scenes. The processing demands were manipulated using speech rate and the temporal distance between mentioned objects. This experiment differs from previous research by using complex photographic scenes, three-sentence utterances and mentioning four target objects. The main finding was that objects that are more slowly mentioned, more evenly placed and isolated in the speech stream are more likely to be fixated after having been mentioned and are fixated faster. Surprisingly, even objects mentioned in the most demanding conditions still show an effect of language-driven eye-movements. This supports research using concurrent speech and visual scenes, and shows that the behavior of matching visual and linguistic information is likely to generalize to language situations of high information load.     

Is everyday causation deterministic or probabilistic?

One view of causation is deterministic: A causes B means that whenever A occurs, B occurs. An alternative view is that causation is probabilistic: the assertion means that given A, the probability of B is greater than some criterion, such as the probability of B given not-A. Evidence about the induction of causal relations cannot readily decide between these alternative accounts, and so we examined how people refute causal assertions. In four experiments most participants judged that a single counterexample of A and not-B refuted assertions of the form, A causes B. And, as a deterministic theory based on mental models predicted, participants were more likely to request multiple refutations for assertions of the form, A enables B. Similarly, refutations of the form not-A and B were more frequent for enabling than causal assertions. Causation in daily life seems to be a deterministic concept.     

Common functional principal components analysis: a new approach to analyzing human movement data

In many human movement studies angle-time series data on several groups of individuals are measured. Current methods to compare groups include comparisons of the mean value in each group or use multivariate techniques such as principal components analysis and perform tests on the principal component scores. Such methods have been useful, though discard a large amount of information. Functional data analysis (FDA) is an emerging statistical analysis technique in human movement research which treats the angle-time series data as a function rather than a series of discrete measurements. This approach retains all of the information in the data. Functional principal components analysis (FPCA) is an extension of multivariate principal components analysis which examines the variability of a sample of curves and has been used to examine differences in movement patterns of several groups of individuals. Currently the functional principal components (FPCs) for each group are either determined separately (yielding components that are group-specific), or by combining the data for all groups and determining the FPCs of the combined data (yielding components that summarize the entire data set). The group-specific FPCs contain both within and between group variation and issues arise when comparing FPCs across groups when the order of the FPCs alter in each group. The FPCs of the combined data may not adequately describe all groups of individuals and comparisons between groups typically use t-tests of the mean FPC scores in each group. When these differences are statistically non-significant it can be difficult to determine how a particular intervention is affecting movement patterns or how injured subjects differ from controls. In this paper we aim to perform FPCA in a manner allowing sensible comparisons between groups of curves. A statistical technique called common functional principal components analysis (CFPCA) is implemented. CFPCA identifies the common sources of variation evident across groups but allows the order of each component to change for a particular group. This allows for the direct comparison of components across groups. We use our method to analyze a biomechanical data set examining the mechanisms of chronic Achilles tendon injury and the functional effects of orthoses.