Age-related neural dedifferentiation in the motor system

Recent neuroimaging studies using multi-voxel pattern analysis (MVPA) show that distributed patterns of brain activation elicited by different visual stimuli are less distinctive in older adults than in young adults. However, less is known about the effects of aging on the neural representation of movement. The present study used MVPA to compare the distinctiveness of motor representations in young and older adults. We also investigated the contributions of brain structure to age differences in the distinctiveness of motor representations. We found that neural distinctiveness was reduced in older adults throughout the motor control network. Although aging was also associated with decreased gray matter volume in these regions, age differences in motor distinctiveness remained significant after controlling for gray matter volume. Our results suggest that age-related neural dedifferentiation is not restricted to sensory perception and is instead a more general feature of the aging brain.     

A Sparse Representation-Based Algorithm for Pattern Localization in Brain Imaging Data Analysis

Considering the two-class classification problem in brain imaging data analysis, we propose a sparse representation-based multi-variate pattern analysis (MVPA) algorithm to localize brain activation patterns corresponding to different stimulus classes/brain states respectively. Feature selection can be modeled as a sparse representation (or sparse regression) problem. Such technique has been successfully applied to voxel selection in fMRI data analysis. However, single selection based on sparse representation or other methods is prone to obtain a subset of the most informative features rather than all. Herein, our proposed algorithm recursively eliminates informative features selected by a sparse regression method until the decoding accuracy based on the remaining features drops to a threshold close to chance level. In this way, the resultant feature set including all the identified features is expected to involve all the informative features for discrimination. According to the signs of the sparse regression weights, these selected features are separated into two sets corresponding to two stimulus classes/brain states. Next, in order to remove irrelevant/noisy features in the two selected feature sets, we perform a nonparametric permutation test at the individual subject level or the group level. In data analysis, we verified our algorithm with a toy data set and an intrinsic signal optical imaging data set. The results show that our algorithm has accurately localized two class-related patterns. As an application example, we used our algorithm on a functional magnetic resonance imaging (fMRI) data set. Two sets of informative voxels, corresponding to two semantic categories (i.e., “old people” and “young people”), respectively, are obtained in the human brain.     

Decoding lifespan changes of the human brain using resting-state functional connectivity MRI

The development of large-scale functional brain networks is a complex, lifelong process that can be investigated using resting-state functional connectivity MRI (rs-fcMRI). In this study, we aimed to decode the developmental dynamics of the whole-brain functional network in seven decades (8-79 years) of the human lifespan. We first used parametric curve fitting to examine linear and nonlinear age effect on the resting human brain, and then combined manifold learning and support vector machine methods to predict individuals' "brain ages" from rs-fcMRI data. We found that age-related changes in interregional functional connectivity exhibited spatially and temporally specific patterns. During brain development from childhood to senescence, functional connections tended to linearly increase in the emotion system and decrease in the sensorimotor system; while quadratic trajectories were observed in functional connections related to higher-order cognitive functions. The complex patterns of age effect on the whole-brain functional network could be effectively represented by a low-dimensional, nonlinear manifold embedded in the functional connectivity space, which uncovered the inherent structure of brain maturation and aging. Regression of manifold coordinates with age further showed that the manifold representation extracted sufficient information from rs-fcMRI data to make prediction about individual brains' functional development levels. Our study not only gives insights into the neural substrates that underlie behavioral and cognitive changes over age, but also provides a possible way to quantitatively describe the typical and atypical developmental progression of human brain function using rs-fcMRI.     

Regional variation in neurovascular coupling and why we still lack a Rosetta Stone

Functional magnetic resonance imaging (fMRI) is the dominant tool in cognitive neuroscience although its relation to underlying neural activity, particularly in the human brain, remains largely unknown. A major research goal, therefore, has been to uncover a ‘Rosetta Stone’ providing direct translation between the blood oxygen level-dependent (BOLD) signal, the local field potential and single-neuron activity. Here, I evaluate the proposal that BOLD signal changes equate to changes in gamma-band activity, which in turn may partially relate to the spiking activity of neurons. While there is some support for this idea in sensory cortices, findings in deeper brain structures like the hippocampus instead suggest both regional and frequency-wise differences. Relatedly, I consider four important factors in linking fMRI to neural activity: interpretation of correlations between these signals, regional variability in local vasculature, distributed neural coding schemes and varying fMRI signal quality. Novel analytic fMRI techniques, such as multivariate pattern analysis (MVPA), employ the distributed patterns of voxels across a brain region to make inferences about information content rather than whether a small number of voxels go up or down relative to baseline in response to a stimulus. Although unlikely to provide a Rosetta Stone, MVPA, therefore, may represent one possible means forward for better linking BOLD signal changes to the information coded by underlying neural activity.This article is part of the theme issue ‘Key relationships between non-invasive functional neuroimaging and the underlying neuronal activity’.     

Decoding Multiple Sound Categories in the Human Temporal Cortex Using High Resolution fMRI

Perception of sound categories is an important aspect of auditory perception. The extent to which the brain’s representation of sound categories is encoded in specialized subregions or distributed across the auditory cortex remains unclear. Recent studies using multivariate pattern analysis (MVPA) of brain activations have provided important insights into how the brain decodes perceptual information. In the large existing literature on brain decoding using MVPA methods, relatively few studies have been conducted on multi-class categorization in the auditory domain. Here, we investigated the representation and processing of auditory categories within the human temporal cortex using high resolution fMRI and MVPA methods. More importantly, we considered decoding multiple sound categories simultaneously through multi-class support vector machine-recursive feature elimination (MSVM-RFE) as our MVPA tool. Results show that for all classifications the model MSVM-RFE was able to learn the functional relation between the multiple sound categories and the corresponding evoked spatial patterns and classify the unlabeled sound-evoked patterns significantly above chance. This indicates the feasibility of decoding multiple sound categories not only within but across subjects. However, the across-subject variation affects classification performance more than the within-subject variation, as the across-subject analysis has significantly lower classification accuracies. Sound category-selective brain maps were identified based on multi-class classification and revealed distributed patterns of brain activity in the superior temporal gyrus and the middle temporal gyrus. This is in accordance with previous studies, indicating that information in the spatially distributed patterns may reflect a more abstract perceptual level of representation of sound categories. Further, we show that the across-subject classification performance can be significantly improved by averaging the fMRI images over items, because the irrelevant variations between different items of the same sound category are reduced and in turn the proportion of signals relevant to sound categorization increases.     

Associations between age and gray matter volume in anatomical brain networks in middle‐aged to older adults

Aging is associated with cognitive decline, diminished brain function, regional brain atrophy, and disrupted structural and functional brain connectivity. Understanding brain networks in aging is essential, as brain function depends on large‐scale distributed networks. Little is known of structural covariance networks to study inter‐regional gray matter anatomical associations in aging. Here, we investigate anatomical brain networks based on structural covariance of gray matter volume among 370 middle‐aged to older adults of 45–85 years. For each of 370 subjects, we acquired a T1‐weighted anatomical MRI scan. After segmentation of structural MRI scans, nine anatomical networks were defined based on structural covariance of gray matter volume among subjects. We analyzed associations between age and gray matter volume in anatomical networks using linear regression analyses. Age was negatively associated with gray matter volume in four anatomical networks (P < 0.001, corrected): a subcortical network, sensorimotor network, posterior cingulate network, and an anterior cingulate network. Age was not significantly associated with gray matter volume in five networks: temporal network, auditory network, and three cerebellar networks. These results were independent of gender and white matter hyperintensities. Gray matter volume decreases with age in networks containing subcortical structures, sensorimotor structures, posterior, and anterior cingulate cortices. Gray matter volume in temporal, auditory, and cerebellar networks remains relatively unaffected with advancing age.     

Mapping informative clusters in a hierarchical [corrected] framework of FMRI multivariate analysis

Pattern recognition methods have become increasingly popular in fMRI data analysis, which are powerful in discriminating between multi-voxel patterns of brain activities associated with different mental states. However, when they are used in functional brain mapping, the location of discriminative voxels varies significantly, raising difficulties in interpreting the locus of the effect. Here we proposed a hierarchical framework of multivariate approach that maps informative clusters rather than voxels to achieve reliable functional brain mapping without compromising the discriminative power. In particular, we first searched for local homogeneous clusters that consisted of voxels with similar response profiles. Then, a multi-voxel classifier was built for each cluster to extract discriminative information from the multi-voxel patterns. Finally, through multivariate ranking, outputs from the classifiers were served as a multi-cluster pattern to identify informative clusters by examining interactions among clusters. Results from both simulated and real fMRI data demonstrated that this hierarchical approach showed better performance in the robustness of functional brain mapping than traditional voxel-based multivariate methods. In addition, the mapped clusters were highly overlapped for two perceptually equivalent object categories, further confirming the validity of our approach. In short, the hierarchical framework of multivariate approach is suitable for both pattern classification and brain mapping in fMRI studies.     

Mapping Informative Clusters in a Hierarchial Framework of fMRI Multivariate Analysis

Pattern recognition methods have become increasingly popular in fMRI data analysis, which are powerful in discriminating between multi-voxel patterns of brain activities associated with different mental states. However, when they are used in functional brain mapping, the location of discriminative voxels varies significantly, raising difficulties in interpreting the locus of the effect. Here we proposed a hierarchical framework of multivariate approach that maps informative clusters rather than voxels to achieve reliable functional brain mapping without compromising the discriminative power. In particular, we first searched for local homogeneous clusters that consisted of voxels with similar response profiles. Then, a multi-voxel classifier was built for each cluster to extract discriminative information from the multi-voxel patterns. Finally, through multivariate ranking, outputs from the classifiers were served as a multi-cluster pattern to identify informative clusters by examining interactions among clusters. Results from both simulated and real fMRI data demonstrated that this hierarchical approach showed better performance in the robustness of functional brain mapping than traditional voxel-based multivariate methods. In addition, the mapped clusters were highly overlapped for two perceptually equivalent object categories, further confirming the validity of our approach. In short, the hierarchical framework of multivariate approach is suitable for both pattern classification and brain mapping in fMRI studies.     

The Statistical Analysis of Multi-Voxel Patterns in Functional Imaging

The goal of multi-voxel pattern analysis (MVPA) in BOLD imaging is to determine whether patterns of activation across multiple voxels change with experimental conditions. MVPA is a powerful technique, its use is rapidly growing, but it poses serious statistical challenges. For instance, it is well-known that the slow nature of the BOLD response can lead to greatly exaggerated performance estimates. Methods are available to avoid this overestimation, and we present those here in tutorial fashion. We go on to show that, even with these methods, standard tests of significance such as Students’ T and the binomial tests are invalid in typical MRI experiments. Only a carefully constructed permutation test correctly assesses statistical significance. Furthermore, our simulations show that performance estimates increase with both temporal as well as spatial signal correlations among multiple voxels. This dependence implies that a comparison of MVPA performance between areas, between subjects, or even between BOLD signals that have been preprocessed in different ways needs great care.     

Age-related white matter microstructural differences partly mediate age-related decline in processing speed but not cognition

Aging is associated with declining cognitive performance as well as structural changes in brain gray and white matter (WM). The WM deterioration contributes to a disconnection among distributed brain networks and may thus mediate age-related cognitive decline. The present diffusion tensor imaging (DTI) study investigated age-related differences in WM microstructure and their relation to cognition (episodic memory, visuospatial processing, fluency, and speed) in a large group of healthy subjects (n = 287) covering 6 decades of the human life span. Age related decreases in fractional anisotropy (FA) and increases in mean diffusivity (MD) were observed across the entire WM skeleton as well as in specific WM tracts, supporting the WM degeneration hypothesis. The anterior section of the corpus callosum was more susceptible to aging compared to the posterior section, lending support to the anterior-posterior gradient of WM integrity in the corpus callosum. Finally, and of critical interest, WM integrity differences were found to mediate age-related reductions in processing speed but no significant mediation was found for episodic memory, visuospatial ability, or fluency. These findings suggest that compromised WM integrity is not a major contributing factor to declining cognitive performance in normal aging. This article is part of a Special Issue entitled: Imaging Brain Aging and Neurodegenerative disease.     

In Defense of Some `Cartesian' Assumptions Concerning the Brain and Its Operation

I argue against a growing radical trend in current theoretical cognitive science that moves from the premises of embedded cognition, embodied cognition, dynamical systems theory and/or situated robotics to conclusions either to the effect that the mind is not in the brain or that cognition does not require representation, or both. I unearth the considerations at the foundation of this view: Haugeland's bandwidth-component argument to the effect that the brain is not a component in cognitive activity, and arguments inspired by dynamical systems theory and situated robotics to the effect that cognitive activity does not involve representations. Both of these strands depend not only on a shift of emphasis from higher cognitive functions to things like sensorimotor processes, but also depend on a certain understanding of how sensorimotor processes are implemented - as closed-loop control systems. I describe a much more sophisticated model of sensorimotor processing that is not only more powerful and robust than simple closed-loop control, but for which there is great evidence that it is implemented in the nervous system. The is the emulation theory of representation, according to which the brain constructs inner dynamical models, or emulators, of the body and environment which are used in parallel with the body and environment to enhance motor control and perception and to provide faster feedback during motor processes, and can be run off-line to produce imagery and evaluate sensorimotor counterfactuals. I then show that the emulation framework is immune to the radical arguments, and makes apparent why the brain is a component in the cognitive activity, and exactly what the representations are in sensorimotor control.     

Virtual navigation in humans: the impact of age, sex, and hormones on place learning

Certain cognitive processes, including spatial ability, decline with normal aging. Spatial ability is also a cognitive domain with robust sex differences typically favoring males. However, tests of spatial ability do not seem to measure a homogeneous class of processes. For many, mentally matching rotated three-dimensional images is the gold standard for measuring spatial cognition in humans, while the Morris water task (MWT) is a preferred method in the domain of nonhuman animal research. The MWT is sensitive to hippocampal damage, a structure critical for normal learning and memory and often implicated in age-related cognitive decline. A computerized (virtual) version of the MWT (VMWT) appears to require and engage human hippocampal circuitry, and has proven useful in studying sex differences and testing spatial learning theories. In Experiment 1, we tested participants (20-90 years of age) in the VMWT and compared their performance to that on the Vandenberg Mental Rotation Test. We report an age-related deficit in performance on both tasks. In Experiment 2, we tested young (age 20-39) and elderly (age >60) participants in the VMWT and correlated their performance to the circulating levels of testosterone and cortisol. Our findings indicate that the persistence of male spatial advantage may be related to circulating testosterone, but not cortisol levels, and independent of generalized age-related cognitive decline.     

Incorporating prior information with fused sparse group lasso: Application to prediction of clinical measures from neuroimages

Predicting clinical variables from whole‐brain neuroimages is a high‐dimensional problem that can potentially benefit from feature selection or extraction. Penalized regression is a popular embedded feature selection method for high‐dimensional data. For neuroimaging applications, spatial regularization using the or norm of the image gradient has shown good performance, yielding smooth solutions in spatially contiguous brain regions. Enormous resources have been devoted to establishing structural and functional brain connectivity networks that can be used to define spatially distributed yet related groups of voxels. We propose using the fused sparse group lasso (FSGL) penalty to encourage structured, sparse, and interpretable solutions by incorporating prior information about spatial and group structure among voxels. We present optimization steps for FSGL penalized regression using the alternating direction method of multipliers algorithm. With simulation studies and in application to real functional magnetic resonance imaging data from the Autism Brain Imaging Data Exchange, we demonstrate conditions under which fusion and group penalty terms together outperform either of them alone.     

Genetic context drives age-related disparities in synaptic maintenance and structure across cortical and hippocampal neuronal circuits

The disconnection of neuronal circuitry through synaptic loss is presumed to be a major driver of age-related cognitive decline. Age-related cognitive decline is heterogeneous, yet whether genetic mechanisms differentiate successful from unsuccessful cognitive decline through maintenance or vulnerability of synaptic connections remains unknown. Previous work using rodent and primate models leveraged various techniques to imply that age-related synaptic loss is widespread on pyramidal cells in prefrontal cortex (PFC) circuits but absent on those in area CA1 of the hippocampus. Here, we examined the effect of aging on synapses on projection neurons forming a hippocampal-cortico-thalamic circuit important for spatial working memory tasks from two genetically distinct mouse strains that exhibit susceptibility (C57BL/6J) or resistance (PWK/PhJ) to cognitive decline during aging. Across both strains, synapse density on CA1-to-PFC projection neurons appeared completely intact with age. In contrast, we found synapse loss on PFC-to-nucleus reuniens (RE) projection neurons from aged C57BL/6J but not PWK/PhJ mice. Moreover, synapses from aged PWK/PhJ mice but not from C57BL/6J exhibited altered morphologies that suggest increased efficiency to drive depolarization in the parent dendrite. Our findings suggest resistance to age-related cognitive decline results in part by age-related synaptic adaptations, and identification of these mechanisms in PWK/PhJ mice could uncover new therapeutic targets for promoting successful cognitive aging and extending human health span.     

Using FMRI brain activation to identify cognitive states associated with perception of tools and dwellings

Previous studies have succeeded in identifying the cognitive state corresponding to the perception of a set of depicted categories, such as tools, by analyzing the accompanying pattern of brain activity, measured with fMRI. The current research focused on identifying the cognitive state associated with a 4s viewing of an individual line drawing (1 of 10 familiar objects, 5 tools and 5 dwellings, such as a hammer or a castle). Here we demonstrate the ability to reliably (1) identify which of the 10 drawings a participant was viewing, based on that participant's characteristic whole-brain neural activation patterns, excluding visual areas; (2) identify the category of the object with even higher accuracy, based on that participant's activation; and (3) identify, for the first time, both individual objects and the category of the object the participant was viewing, based only on other participants' activation patterns. The voxels important for category identification were located similarly across participants, and distributed throughout the cortex, focused in ventral temporal perceptual areas but also including more frontal association areas (and somewhat left-lateralized). These findings indicate the presence of stable, distributed, communal, and identifiable neural states corresponding to object concepts.     

Micro-MRI Study of Cerebral Aging: Ex Vivo Detection of Hippocampal Subfield Reorganization,Microhemorrhages and Amyloid Plaques in Mouse Lemur Primates

Mouse lemurs are non-human primate models of cerebral aging and neurodegeneration. Much smaller than other primates, they recapitulate numerous features of human brain aging, including progressive cerebral atrophy and correlation between regional atrophy and cognitive impairments. Characterization of brain atrophy in mouse lemurs has been done by MRI measures of regional CSF volume and by MRI measures of regional atrophy. Here, we further characterize mouse lemur brain aging using ex vivo MR microscopy (31 µm in-plane resolution). First, we performed a non-biased, direct volumetric quantification of dentate gyrus and extended Ammon''s horn. We show that both dentate gyrus and Ammon''s horn undergo an age-related reorganization leading to a growth of the dentate gyrus and an atrophy of the Ammon''s horn, even in the absence of global hippocampal atrophy. Second, on these first MR microscopic images of the mouse lemur brain, we depicted cortical and hippocampal hypointense spots. We demonstrated that their incidence increases with aging and that they correspond either to amyloid deposits or to cerebral microhemorrhages.     

Ageing,Neuronal Connectivity and Brain Disorders: An Unsolved Ripple Effect

Cognitive decline associated with ageing and age-related disorders emerges as one of the greatest health challenges in the next decades. To date, the molecular mechanisms underlying the onset of neuronal physiological changes in the central nervous system remain unclear. Functional MRI and PET studies have indicated the decline in working memory performance in older adults. Similarly, age-related disorders, such as Alzheimer’s disease, are associated with changes in the prefontral cortex and related neural circuitry, which underlines the decline of integrative function between different brain regions. This is mainly attributed to the loss of synaptic connectivity, which is a feature commonly observed in neurodegenerative disorders. In humans, the morphological and functional changes in neurons, such as reduction of spine numbers and synaptic dysfunction, precede the first signs of cognitive decline and likely contribute to pathology progression. Thus, a new scenario emerges in which apparently unrelated diseases present common features, such as the remodelling of neuronal circuitries promoted by ageing. For many years, ageing was considered a process of slow deterioration triggered by accidental environmental factors. Conversely, it is now evident that ageing is a biological process tightly controlled by evolutionary highly conserved signalling pathways. Importantly, genetic mutations that enhance longevity significantly delay the loss of synaptic connectivity and, therefore, the onset of age-related brain disorders. Accordingly, tweaking ageing might be an attractive approach to prevent cognitive decline caused by age-related synaptic dysfunction.     

White Matter Hyperintensities among Older Adults Are Associated with Futile Increase in Frontal Activation and Functional Connectivity during Spatial Search

The mechanisms by which aging and other processes can affect the structure and function of brain networks are important to understanding normal age-related cognitive decline. Advancing age is known to be associated with various disease processes, including clinically asymptomatic vascular and inflammation processes that contribute to white matter structural alteration and potential injury. The effects of these processes on the function of distributed cognitive networks, however, are poorly understood. We hypothesized that the extent of magnetic resonance imaging white matter hyperintensities would be associated with visual attentional control in healthy aging, measured using a functional magnetic resonance imaging search task. We assessed cognitively healthy older adults with search tasks indexing processing speed and attentional control. Expanding upon previous research, older adults demonstrate activation across a frontal-parietal attentional control network. Further, greater white matter hyperintensity volume was associated with increased activation of a frontal network node independent of chronological age. Also consistent with previous research, greater white matter hyperintensity volume was associated with anatomically specific reductions in functional magnetic resonance imaging functional connectivity during search among attentional control regions. White matter hyperintensities may lead to subtle attentional network dysfunction, potentially through impaired frontal-parietal and frontal interhemispheric connectivity, suggesting that clinically silent white matter biomarkers of vascular and inflammatory injury can contribute to differences in search performance and brain function in aging, and likely contribute to advanced age-related impairments in cognitive control.     

Aging alters the multichemical networking profile of the human brain: an in vivo (1)H-MRS study of young versus middle-aged subjects

In our most recent study of normal aging, we found decreased concentration of multiple chemicals in the brain of middle-aged subjects, as compared with younger subjects using in vivo proton magnetic resonance spectroscopy ((1)H-MRS). We hypothesized that these age-dependent differences in brain chemistry changes might be a reflection of the multichemical-networking-profile (MCNP) changes during aging. Using (1)H-MRS and correlation analysis, we examined the patterns of regional chemical levels and MCNP within and across multiple brain regions for all nine chemicals of (1)H-MR spectra. The brain chemistry changes and MCNP patterns were compared between 21 young (19--31-year-old) and 31 middle-aged (40--52-year-old) normal volunteers. Middle-aged subjects demonstrated a significant decrease of chemical levels in the prefrontal cortex and sensorimotor cortex (SMC), as compared with the young age group. Of these, neurotransmitters GABA and glutamate in the dorsolateral prefrontal cortex (DLPFC) were altered the most. We also found a significant increase of overall chemical correlation strength in MCNP within and across all studied brain regions with increased age. These changes were caused by alterations in the pattern of negative chemical connectivity across brain regions, which become weaker (less negative) in middle-aged subjects. The interregional chemical connectivity for the cingulate cortex, SMC and the thalamus was changed the most with increased age. Increased levels of chemical correlation strength across brain regions in aging were found for most chemicals studied (including neurotransmitters GABA and glutamate), and not for N-acetyl aspartate. These age-related differences in the connectivity of neurotransmitters were not region dependent. The results suggest that aging is associated with changes of the regional brain chemistry and the brain MCNP. The latter process may reflect an adaptive or compensatory response (possibly related to the elongation of dendrites with aging) to reduced levels of regional brain chemicals. The (1)H-MRS approach proposed here can be used as a valuable tool in the study of the brain chemistry, MCNP and their relationships in normal and abnormal aging.     

Modified test statistics by inter-voxel variance shrinkage with an application to f MRI

Functional magnetic resonance imaging (f MRI) is a noninvasive technique which is commonly used to quantify changes in blood oxygenation and flow coupled to neuronal activation. One of the primary goals of f MRI studies is to identify localized brain regions where neuronal activation levels vary between groups. Single voxel t-tests have been commonly used to determine whether activation related to the protocol differs across groups. Due to the generally limited number of subjects within each study, accurate estimation of variance at each voxel is difficult. Thus, combining information across voxels is desirable in order to improve efficiency. Here, we construct a hierarchical model and apply an empirical Bayesian framework for the analysis of group f MRI data, employing techniques used in high-throughput genomic studies. The key idea is to shrink residual variances by combining information across voxels and subsequently to construct an improved test statistic. This hierarchical model results in a shrinkage of voxel-wise residual sample variances toward a common value. The shrunken estimator for voxel-specific variance components on the group analyses outperforms the classical residual error estimator in terms of mean-squared error. Moreover, the shrunken test statistic decreases false-positive rates when testing differences in brain contrast maps across a wide range of simulation studies. This methodology was also applied to experimental data regarding a cognitive activation task.