Neuroscience. When is a word not a word?
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Brain↗
Biomedical subjects
Anthony R McIntosh
Publications and source records attributed to Anthony R McIntosh.
Evidence from functional neuroimaging of a compensatory prefrontal network in Alzheimer's disease.
Previous experiments have found that individuals with Alzheimer's disease (AD) show increased activity in prefrontal regions compared with healthy age-matched controls during cognitive tasks. This has been interpreted as compensatory reallocation of cognitive resources, but direct evidence for a facilitating effect on performance has been lacking. To address this we measured neural activity during semantic and episodic memory tasks in mildly demented AD patients and healthy elderly controls. Controls recruited a left hemisphere network of regions, including prefrontal and temporal cortices in both the semantic and episodic tasks. Patients engaged a unique network involving bilateral dorsolateral prefrontal and posterior cortices. Critically, activity in this network of regions was correlated with better performance on both the semantic and episodic tasks in the patients. This provides the most direct evidence to date that AD patients can use additional neural resources in prefrontal cortex, presumably those mediating executive functions, to compensate for losses attributable to the degenerative process of the disease.
Adaptation, Physiological↗
Age-related differences in the functional connectivity of the hippocampus during memory encoding.
The purpose of the current experiment was to examine the functional connectivity of the hippocampus during encoding in young and old adults, and the way in which this connectivity was related to recognition performance. Functional connectivity was defined as the correlation between activity in the hippocampus and activity in the rest of the brain, as measured by neuroimaging. During encoding of words and pictures of objects in young adults, hippocampal activity was correlated with activity in the ventral prefrontal and extrastriate regions, and increased activity in all these regions was associated with better recognition. In contrast, older adults showed correlations between hippocampal activity and the dorsolateral prefrontal and parietal regions, and positive correlations between activity in these regions and better memory performance. This ventral/dorsal distinction suggests a shift in the cognitive resources used with age from more perceptually based processes to those involved in executive and organizational functions. The results of this study provide evidence that aging is associated with alterations in hippocampal function, including how it is functionally connected with prefrontal cortex, and that these alterations have an impact on memory performance.
Adult↗
Memory encoding and hippocampally-based novelty/familiarity discrimination networks.
Novelty discrimination refers to the ability to decide whether information is new or has been previously encountered. Recent functional neuroimaging work has demonstrated that the hippocampus plays an important function in novelty discrimination. In the study described here, we explored the idea that novelty discrimination does not depend on the hippocampus alone but involves large-scale functional neural networks consisting of spatially remote brain regions. We measured blood flow with positron emission tomography (PET) while subjects semantically encoded visually and auditorily presented situationally novel and familiar words. Following each PET scan, subjects' memory was tested with a standard yes/no recognition test. Blood flow data were analyzed with the covariance-based seed partial least squares (PLS) method. Behaviorally, subjects' recognition performance was higher for novel than familiar words. Neurally, two large-scale functional networks involving the same region of the hippocampus were identified which showed coherent activity either during the encoding of situationally novel (but not familiar) items or situationally familiar (but not novel) items. These findings indicate that different neural networks are active in the processing of situationally novel and familiar information. The observation that the hippocampus participates in both networks supports the principle of neural context.
Adolescent↗
The functional anatomy of parkinsonian bradykinesia.
To investigate the difficulty that patients with Parkinson's disease (PD) have in performing fast movements, we used H(2)(15)O PET to study regional cerebral blood flow (rCBF) associated with performance of a simple predictive visuomanual tracking task at three different velocities. Tracking movements in PD patients (versus tracking with the eyes alone) were associated with a general underactivation of the areas normally activated by the task (sensorimotor cortex contralateral to the moving arm, bilateral dorsal premotor cortices, and ipsilateral cerebellum). Presupplementary motor cortex (pre-SMA) ipsilateral to the moving arm had greater than normal movement-related activations. Increasing movement velocity led to increased rCBF in multiple premotor and parietal cortical areas and basal ganglia in the patients as opposed to the few cerebral locations that are normally velocity-related. The functional correlates of PD bradykinesia are: (1) impaired recruitment of cortical and subcortical systems that normally regulate kinematic parameters of movement such as velocity; and (2) increased recruitment of multiple premotor areas including both regions specialized for visuomotor control (ventral premotor and parietal cortices) and some that are not (pre-SMA). The overactivation of cortical regions observed in patients may be functional correlates of compensatory mechanisms and/or impaired suppression as a facet of the primary pathophysiology of PD.
Brain↗
Multivariate analysis of neuronal interactions in the generalized partial least squares framework: simulations and empirical studies.
Identification of spatiotemporal interactions within/between neuron populations is critical for detection and characterization of large-scale neuronal interactions underlying perception, cognition, and behavior. Univariate analysis has been employed successfully in many neuroimaging studies. However, univariate analysis does not explicitly test for interactions between distributed areas of activity and is not sensitive to distributed responses across the brain. Multivariate analysis can explicitly test for multiple statistical models, including the designed paradigm, and allows for spatial and temporal model detection. Here, we investigate multivariate analysis approaches that take into consideration the 4D (time and space) covariance structure of the data. Principal component analysis (PCA) and independent component analysis (ICA) are two popular multivariate approaches with distinct mathematical constraints. Common difficulties in using these two different decompositions include the following: classification of the revealed components (task-related signal versus noise), overall signal-to-noise sensitivity, and the relatively low computational efficiency (multivariate analysis requires the entire raw data set and more time for model identification analysis). Using both Monte Carlo simulations and empirical data, we derived and tested the generalized partial least squares (gPLS) framework, which can incorporate both PCA and ICA decompositions with computational efficiency. The gPLS method explicitly incorporates the experimental design to simplify the identification of characteristic spatiotemporal patterns. We performed parametric modeling studies of a blocked-design experiment under various conditions, including background noise distribution, sampling rate, and hemodynamic response delay. We used a randomized grouping approach to manipulate the degrees of freedom of PCA and ICA in gPLS to characterize both paradigm coherent and transient brain responses. Simulation data suggest that in the gPLS framework, PCA mostly outperforms ICA as measured by the receiver operating curves (ROCs) in SNR from 0.01 to 100, the hemodynamic response delays from 0 to 3 TR in fMRI, background noise models of Guassian, sub-Gaussian, and super-Gaussian distributions and the number of observations from 5, 10, to 20 in each block of a six-block experiment. Further, due to selective averaging, the gPLS method performs robustly in low signal-to-noise ratio (<1) experiments. We also tested PCA and ICA using PLS in a simulated event-related fMRI data to show their similar detection. Finally, we tested our gPLS approach on empirical fMRI motor data. Using the randomized grouping method, we are able to identify both transient responses and consistent paradigm/model coherent components in the 10-epoch block design motor fMRI experiment. Overall, studies of synthetic and empirical data suggest that PLS analysis, using PCA decomposition, provides a stable and powerful tool for exploration of fMRI/behavior data.
Algorithms↗
An empirical comparison of SPM preprocessing parameters to the analysis of fMRI data.
We present the results from two sets of Monte Carlo simulations aimed at evaluating the robustness of some preprocessing parameters of SPM99 for the analysis of functional magnetic resonance imaging (fMRI). Statistical robustness was estimated by implementing parametric and nonparametric simulation approaches based on the images obtained from an event-related fMRI experiment. Simulated datasets were tested for combinations of the following parameters: basis function, global scaling, low-pass filter, high-pass filter and autoregressive modeling of serial autocorrelation. Based on single-subject SPM analysis, we derived the following conclusions that may serve as a guide for initial analysis of fMRI data using SPM99: (1) The canonical hemodynamic response function is a more reliable basis function to model the fMRI time series than HRF with time derivative. (2) Global scaling should be avoided since it may significantly decrease the power depending on the experimental design. (3) The use of a high-pass filter may be beneficial for event-related designs with fixed interstimulus intervals. (4) When dealing with fMRI time series with short interstimulus intervals (<8 s), the use of first-order autoregressive model is recommended over a low-pass filter (HRF) because it reduces the risk of inferential bias while providing a relatively good power. For datasets with interstimulus intervals longer than 8 seconds, temporal smoothing is not recommended since it decreases power. While the generalizability of our results may be limited, the methods we employed can be easily implemented by other scientists to determine the best parameter combination to analyze their data.
Algorithms↗
Aging gracefully: compensatory brain activity in high-performing older adults.
Whereas some older adults show significant cognitive deficits, others perform as well as young adults. We investigated the neural basis of these different aging patterns using positron emission tomography (PET). In PET and functional MRI (fMRI) studies, prefrontal cortex (PFC) activity tends to be less asymmetric in older than in younger adults (Hemispheric Asymmetry Reduction in Old Adults or HAROLD). This change may help counteract age-related neurocognitive decline (compensation hypothesis) or it may reflect an age-related difficulty in recruiting specialized neural mechanisms (dedifferentiation hypothesis). To compare these two hypotheses, we measured PFC activity in younger adults, low-performing older adults, and high-performing older adults during recall and source memory of recently studied words. Compared to recall, source memory was associated with right PFC activations in younger adults. Low-performing older adults recruited similar right PFC regions as young adults, but high-performing older adults engaged PFC regions bilaterally. Thus, consistent with the compensation hypothesis and inconsistent with the dedifferentiation hypothesis, a hemispheric asymmetry reduction was found in high-performing but not in low-performing older adults. The results suggest that low-performing older adults recruited a similar network as young adults but used it inefficiently, whereas high-performing older adults counteracted age-related neural decline through a plastic reorganization of neurocognitive networks.
Adult↗
Relationships among the metabolic patterns that correlate with mnemonic, visuospatial, and mood symptoms in Parkinson's disease.
OBJECTIVE: A multivariate analysis of baseline brain metabolism was used to investigate the relationships among pathophysiological mechanisms responsible for cognitive dysfunction and dysphoria in nondemented patients with Parkinson's disease. METHOD: Using [(18)F]fluorodeoxyglucose positron emission tomography and neuropsychological tests, the authors studied 15 nondemented patients who had Parkinson's disease without major depression (DSM-III-R). Their mean age was 59.2 years (SD=9.2), the mean rating of Parkinson's disease stage (Hoehn and Yahr scale) was 3.3 (SD=0.9), and all had Mini-Mental State Examination scores of 24 or higher. To identify specific regional patterns of brain metabolism associated with abnormal cognitive and mood functioning, the data were analyzed by using brain-behavior partial least squares. This multivariate voxel-based analysis allowed detection of significant topographic patterns of metabolic activity and quantification of the extent to which each topographic pattern correlated with scores on mnemonic, visuospatial, and dysphoric tests. RESULTS: Two significant, independent topographic patterns were identified. Pattern 1 included parieto-occipito-temporal and medial temporal brain regions, and pattern 2 included the lateral frontal and anterior limbic cortex. Patterns 1 and 2 exhibited a double dissociation in their behavioral correlates: pattern 1 correlated with both visuospatial and mnemonic functioning but not with dysphoria; pattern 2 correlated with dysphoria but not with the cognitive measures. CONCLUSIONS: The authors used the independence of topographic patterns and the size of correlations between topographic patterns and behavior to infer relationships among the pathophysiological processes responsible for the correlations. The finding that mildly abnormal mnemonic and visuospatial functioning correlated with the same topographic pattern suggests that a common pathophysiology underlies this marker of cognition in Parkinson's disease. By contrast, the independence of the two topographic patterns supports the notion that different mechanisms underlie cognitive and dysphoric symptoms in nondemented patients with Parkinson's disease.
Adult↗
Dissecting the effect of aging on the neural substrates of memory: deterioration, preservation or functional reorganization?
One of the most common deficits observed during late adulthood is a loss in the ability to learn and remember new information. This cognitive ability depends mainly on the integrity of the hippocampal formation and the prefrontal cortex, which are especially susceptible to the effects of age. Here we provide a selective review of the literature gathered from studies carried out in humans and animals, examining the effect of aging on the functional anatomy of memory. We discuss some of the methodological and theoretical difficulties associated with the current approach to the study of aging and, in turn, a series of strategies that may be implemented to ensure the most accurate interpretation of the data. Altogether, the evidence discussed in this review supports the idea that there is no general age-related deterioration of the neural substrates of memory, but rather a differential effect in which some brain areas may be adversely affected while others may compensate for the neurobiological deterioration associated with age.
Aging↗