Memory retrieval is believed to involve a disparate network of areas, including medial prefrontal and medial temporal cortices, but the mechanisms underlying their coordination remain elusive. One suggestion is that oscillatory coherence mediates inter-regional communication, implicating theta phase and theta-gamma phase-amplitude coupling in mnemonic function across species. To examine this hypothesis, we used non-invasive whole-head magnetoencephalography (MEG) as participants retrieved the location of objects encountered within a virtual environment. We demonstrate that, when participants are cued with the image of an object whose location they must subsequently navigate to, there is a significant increase in 4–8 Hz theta power in medial prefrontal cortex (mPFC), and the phase of this oscillation is coupled both with ongoing theta phase in the medial temporal lobe (MTL) and perceptually induced 65–85 Hz gamma amplitude in medial parietal cortex. These results suggest that theta phase coupling between mPFC and MTL and theta-gamma phase-amplitude coupling between mPFC and neocortical regions may play a role in human spatial memory retrieval. © 2014 The Authors. Hippocampus Published by Wiley Periodicals, Inc.
oscillations; mPFC; MTL; hippocampus; MEG
Contrasts of verbal fluency and automatic speech provide an opportunity to evaluate the neural underpinnings of generativity and flexibility in autism spectrum disorders (ASD).
We used functional magnetic resonance imaging (fMRI) to contrast brain activity in high functioning ASD (n=17, mean verbal IQ=117) and neurotypical (NT; n=20, mean verbal IQ=112) adolescent and young adult males (12-23 years). Participants responded to three word generation conditions: automatic speech (reciting months), category fluency, and letter fluency.
Our paradigm closely mirrored behavioral fluency tasks by requiring overt, free recall word generation while controlling for differences in verbal output between the groups and systematically increasing the task demand. The ASD group showed reduced neural response compared to the NT participants during fluency tasks in multiple regions of left anterior and posterior cortices, and sub-cortical structures. Six of these regions fell in corticostriatal circuits previously linked to repetitive behaviors (Langen, et al, 2011), and activity in two of them (putamen and thalamus) was negatively correlated with autism repetitive behavior symptoms in the ASD group. In addition, response in left inferior frontal gyrus was differentially modulated in the ASD, relative to the NT, group as a function of task demand.
These data indicate a specific, atypical brain response in ASD to demanding generativity tasks that may have relevance to repetitive behavior symptoms in ASD as well as to difficulties generating original verbal responses.
autism; verbal fluency; fMRI; executive function; left inferior frontal gyrus
The Brain Research through Advancing Innovative Neurotechnologies (BRAIN) Initiative has focused scientific attention on the necessary tools to understand the human brain and mind. Here, we outline our collective vision for what we can achieve within a decade with properly targeted efforts, and discuss likely technological deliverables and neuroscience progress.
The goal of resting-state functional magnetic resonance imaging (FMRI) is to investigate the brain’s functional connections by using the temporal similarity between blood oxygenation level dependent (BOLD) signals in different regions of the brain “at rest” as an indicator of synchronous neural activity. Since this measure relies on the temporal correlation of FMRI signal changes between different parts of the brain, any non-neural activity-related process that affects the signals will influence the measure of functional connectivity, yielding spurious results. To understand the sources of these resting-state FMRI confounds, this article describes the origins of the BOLD signal in terms of MR physics and cerebral physiology. Potential confounds arising from motion, cardiac and respiratory cycles, arterial CO2 concentration, blood pressure/cerebral autoregulation, and vasomotion are discussed. Two classes of techniques to remove confounds from resting-state BOLD time series are reviewed: 1) those utilising external recordings of physiology and 2) data-based cleanup methods that only use the resting-state FMRI data itself. Further methods that remove noise from functional connectivity measures at a group level are also discussed. For successful interpretation of resting-state FMRI comparisons and results, noise cleanup is an often over-looked but essential step in the analysis pipeline.
Functional Magnetic Resonance Imaging (FMRI); resting-state; functional connectivity; noise correction; physiological noise
The first two decades of brain research using fMRI have been dominated by studies that measure signal changes in response to a presented task. A rapidly increasing number of studies are showing that consistent activation maps appear by assessment of signal correlations during time periods in which the subjects were not directed to perform any specific task (i.e. “resting state correlations”). Even though neural interactions can happen on much shorter time scales, most “resting state” studies assess these temporal correlations over a period of about 5 to 10 minutes. Here we investigate how these temporal correlations change on a shorter time scale. We examine changes in brain correlations to the posterior cingulate cortex (PCC) across a 10 minutes scan. We show: (1) fMRI correlations fluctuate over time, (2) these fluctuations can be periodic, and (3) correlations between the PCC and other brain regions fluctuate at distinct frequencies. While the precise frequencies of correlation fluctuations vary across subjects and runs, it is still possible to parse brain regions and combinations of brain regions based on fluctuation frequency differences. To evaluate the potential biological significance of these empirical observations, we then use synthetic time series data with identical amplitude spectra, but randomized phase to show that similar effects can still appear even if the timing relationships between voxels are randomized. This implies that observed correlation fluctuations could occur between regions with distinct amplitude spectra, whether or not there are dynamic changes in neural connectivity between such regions. As more studies of brain connectivity dynamics appear, particularly studies using correlation as a key metric, it is vital to better distinguish true neural connectivity dynamics from connectivity fluctuations that are inherently part of this method. Our results also highlight the rich information in the power spectra of fMRI data that can be used to parse brain regions.
BOLD; fMRI; posterior cingulated; Default Mode; Spontaneous fluctuations; Resting-state
Interpretation of fMRI data depends on our ability to understand or model the shape of the hemodynamic response (HR) to a neural event. Although the HR has been studied almost since the beginning of fMRI, we are still far from having robust methods to account for the full range of known HR variation in typical fMRI analyses. This paper reviews how the authors and others contributed to our understanding of HR variation. We present an overview of studies that describe HR variation across voxels, healthy volunteers, populations, and dietary or pharmaceutical modulations. We also describe efforts to minimize the effects of HR variation in intrasubject, group, population, and connectivity analyses and the limits of these methods.
BOLD; fMRI; hemodynamic response; population studies; regional variation; blood vessels
We investigated the decoding of millisecond-order timing information in ocular dominance stimulation from the blood oxygen level dependent (BOLD) signal in human functional magnetic resonance imaging (fMRI). In our experiment, ocular dominance columns were activated by monocular visual stimulation with 500- or 100- ms onset differences. We observed that the event-related hemodynamic response (HDR) in the human visual cortex was sensitive to the subtle onset difference. The HDR shapes were related to the stimulus timings in various manners: the timing difference was represented in either the amplitude of positive peak, amplitude of negative peak, delay of peak time, or response duration of HDR. These complex relationships were different across voxels and subjects. To find an informative feature of HDR for discriminating the subtle timing difference of ocular dominance stimulations, we examined various characteristics of HDR including response amplitude, time to peak, full width at half-maximum response, as inputs for decoding analysis. Using a canonical HDR function for estimating the voxel’s response did not yield good decoding scores, suggesting that information may reside in the variability of HDR shapes. Using all the values from the deconvolved HDR also showed low performance, which could be due to an over-fitting problem with the large data dimensionality. When using either positive or negative peak amplitude of the deconvolved HDR, high decoding performance could be achieved for both the 500ms and the 100ms onset differences. The high accuracy even for the 100ms difference, given that the signal was sampled at a TR of 250 ms and 2×2×3-mm voxels, implies a possibility of spatiotemporally hyper-resolution decoding. Furthermore, both down-sampling and smoothing did not affect the decoding accuracies very much. These results suggest a complex spatiotemporal relationship between the multi-voxel pattern of the BOLD response and the population activation of neuronal columns. The demonstrated possibility of decoding a 100-ms difference of stimulations for columnar-level organization with lower resolution imaging data may broaden the scope of application of the BOLD fMRI.
Multi-voxel pattern analysis; deconvolved hemodynamic response; hyper-spatiotemporal resolution; complex spatiotemporal filter voxel
Lack of tissue contrast and existing inhomogeneous bias fields from multi-channel coils have the potential to degrade the output of registration algorithms; and consequently degrade group analysis and any attempt to accurately localize brain function. Non-invasive ways to improve tissue contrast in fMRI images include the use of low flip angles (FAs) well below the Ernst angle and longer repetition times (TR). Techniques to correct intensity inhomogeneity are also available in most mainstream fMRI data analysis packages; but are not used as part of the pre-processing pipeline in many studies. In this work, we use a combination of real data and simulations to show that simple-to-implement acquisition/pre-processing techniques can significantly improve the outcome of both functional-to-functional and anatomical-to-functional image registrations. We also emphasize the need of tissue contrast on EPI images to be able to appropriately evaluate the quality of the alignment. In particular, we show that the use of low FAs (e.g., θ≤40°), when physiological noise considerations permit such an approach, significantly improves accuracy, consistency and stability of registration for data acquired at relatively short TRs (TR≤2s). Moreover, we also show that the application of bias correction techniques significantly improves alignment both for array-coil data (known to contain high intensity inhomogeneity) as well as birdcage-coil data. Finally, improvements in alignment derived from the use of the first infinite-TR volumes (ITVs) as targets for registration are also demonstrated. For the purpose of quantitatively evaluating the different scenarios, two novel metrics were developed: Mean Voxel Distance (MVD) to evaluate registration consistency, and Deviation of Mean Voxel Distance (dMVD) to evaluate registration stability across successive alignment attempts.
Pseudo-continuous arterial spin labeling (PCASL) can provide best SNR efficiency with a sufficiently long tag at high fields such as 7T, but it is very sensitive to off-resonance fields at the tagging location. Here a robust Prescan procedure is demonstrated to estimate the PCASL RF phase and gradients parameters required to compensate the off-resonance effects at each vessel location. The Prescan is completed in 1–2 minutes and is based on acquisition of label/control pair-wise ASL data as a function of the RF phase increment applied to the PCASL train. It is shown that this approach can be used to acquire high quality whole-brain PCASL perfusion data of the human brain at 7T.
Arterial spin labeling; 7T; Off-resonance effects; Prescan
We examined how spatial smoothing affects the result of multivariate classification analysis using the linear support vector machine (SVM) for decoding columnar-level organization. It has been suggested that the effect of spatial smoothing on decoding performance is minor because smoothing operation is an invertible data transformation and such invertible transformation does not remove information in multivariate pattern. Our theoretical consideration, however, revealed that generalization score (performance for test samples unused during classifier training) was susceptible to non-uniform scaling of input data; SVM classifier became less sensitive to variability in shrunk dimension. This result indicates that spatial smoothing reduces sensitivity of SVM classifier to high spatial frequency pattern so that the effect of smoothing implies the amount of information distributed in spatial frequencies. We also examined the effect of smoothing in an fMRI experiment of decoding ocular dominance responses. The results of group statistic showed that large smoothing reduced decoding accuracies while the smoothing effect at individual subject were not the same for all subjects. These results suggest that spatial smoothing can have major effect on decoding performance and the informative pattern for columnar level decoding resides in higher frequencies on average across subjects while it may distribute multiple frequencies at individual subject level.
multivoxel pattern analysis; support vector machine; informative spatial frequency; columnar-level decoding
Artifactual sources of resting-state (RS) FMRI can originate from head motion, physiology, and hardware. Of these sources, motion has received considerable attention and was found to induce corrupting effects by differentially biasing correlations between regions depending on their distance. Numerous corrective approaches have relied on the identification and censoring of high-motion time points and the use of the brain-wide average time series as a nuisance regressor to which the data are orthogonalized (Global Signal Regression, GSReg). We first replicate the previously reported head-motion bias on correlation coefficients using data generously contributed by Power et al. (2012). We then show that while motion can be the source of artifact in correlations, the distance-dependent bias—taken to be a manifestation of the motion effect on correlation—is exacerbated by the use of GSReg. Put differently, correlation estimates obtained after GSReg are more susceptible to the presence of motion and by extension to the levels of censoring. More generally, the effect of motion on correlation estimates depends on the preprocessing steps leading to the correlation estimate, with certain approaches performing markedly worse than others. For this purpose, we consider various models for RS FMRI preprocessing and show that WMeLOCAL, as subset of the ANATICOR discussed by Jo et al. (2010), denoising approach results in minimal sensitivity to motion and reduces by extension the dependence of correlation results on censoring.
Resting state functional MRI (rsfMRI) connectivity patterns are not temporally stable, but fluctuate in time at scales shorter than most common rest scan durations (5–10 min). Consequently, connectivity patterns for two different portions of the same scan can differ drastically. To better characterize this temporal variability and understand how it is spatially distributed across the brain, we scanned subjects continuously for 60 min, at a temporal resolution of 1 s, while they rested inside the scanner. We then computed connectivity matrices between functionally-defined regions of interest for non-overlapping 1 min windows, and classified connections according to their strength, polarity, and variability. We found that the most stable connections correspond primarily to inter-hemispheric connections between left/right homologous ROIs. However, only 32% of all within-network connections were classified as most stable. This shows that resting state networks have some long-term stability, but confirms the flexible configuration of these networks, particularly those related to higher order cognitive functions. The most variable connections correspond primarily to inter-hemispheric, across-network connections between non-homologous regions in occipital and frontal cortex. Finally we found a series of connections with negative average correlation, but further analyses revealed that such average negative correlations may be related to the removal of CSF signals during pre-processing. Using the same dataset, we also evaluated how similarity of within-subject whole-brain connectivity matrices changes as a function of window duration (used here as a proxy for scan duration). Our results suggest scanning for a minimum of 10 min to optimize within-subject reproducibility of connectivity patterns across the entire brain, rather than a few predefined networks.
fMRI; connectivity dynamics; stability; rest; sliding window analysis
The brain must dynamically integrate, coordinate, and respond to internal and external stimuli across multiple time scales. Non-invasive measurements of brain activity with fMRI have greatly advanced our understanding of the large-scale functional organization supporting these fundamental features of brain function. Conclusions from previous resting-state fMRI investigations were based upon static descriptions of functional connectivity (FC), and only recently studies have begun to capitalize on the wealth of information contained within the temporal features of spontaneous BOLD FC. Emerging evidence suggests that dynamic FC metrics may index changes in macroscopic neural activity patterns underlying critical aspects of cognition and behavior, though limitations with regard to analysis and interpretation remain. Here, we review recent findings, methodological considerations, neural and behavioral correlates, and future directions in the emerging field of dynamic FC investigations.
Functional connectivity; Resting state; Dynamics; Spontaneous activity; Functional MRI (fMRI); Fluctuations
In 1991, the Biophysics Research Institute at the Medical College of Wisconsin was among the first groups to develop functional Magnetic Resonance Imaging (fMRI). Our story is unique on a few levels: We didn’t have knowledge of the ability to image human brain activation with MRI using blood oxygenation dependent (BOLD) contrast until early August of 1991 when we attended the Society for Magnetic Resonance in Medicine (SMRM) meeting in San Francisco, yet we produced our first BOLD-based maps of motor cortex activation about a month later. The effort started with two graduate students, Eric Wong and myself. Only a few days prior to that extremely important SMRM meeting, we had developed human echo planar imaging (EPI) capability in-house. Wong designed, built, and interfaced a head gradient coil made out of sewer pipe, wire, and epoxy to a standard GE 1.5 T MRI scanner. Also, a few months prior to building this human head gradient coil he developed the EPI pulse sequences and image reconstruction. All of these efforts were towards a different goal – for demonstration of Wong’s novel approach to perfusion imaging in the human brain. Following SMRM, where a plenary lecture by Tom Brady from MGH opened our eyes to human brain activation imaging using BOLD contrast, and where we learned that EPI was extremely helpful if not critical to its success, we worked quickly to achieve our first results on September 14, 1991. The story is also unique in that Jim Hyde had set up the Biophysics Research Institute to be optimal for just this type of rapidly advancing basic technology research. It was well equipped for hardware development, had open and dynamic collaborative relationships with other departments, hospitals on campus, and GE, and had a relatively flat hierarchy and relaxed, flexible, collegial atmosphere internally. Since these first brain activation results, MCW Biophysics has continued to be at the forefront of functional MRI innovation, having helped to pioneer real time fMRI, high-resolution fMRI, and functional connectivity mapping.
To investigate patterns and correlates of cortical thickness in adolescent males with autism spectrum disorders (ASD) versus matched typically developing controls, we applied kernel canonical correlation analysis to whole brain cortical thickness with the explaining variables of diagnosis, age, full-scale IQ, and their interactions. The analysis found that canonical variates (patterns of cortical thickness) correlated with each of these variables. The diagnosis- and age-by-diagnosis-related canonical variates showed thinner cortex for participants with ASD, which is consistent with previous studies using a univariate analysis. In addition, the multivariate statistics found larger affected regions with higher sensitivity than those found using univariate analysis. An IQ-related effect was also found with the multivariate analysis. The effects of IQ and age-by-IQ interaction on cortical thickness differed between the diagnostics groups. For typically developing adolescents, IQ was positively correlated with cortical thickness in orbitofrontal, postcentral and superior temporal regions, and greater thinning with age was seen in dorsal frontal areas in the superior IQ (> 120) group. These associations between IQ and cortical thickness were not seen in the ASD group. Differing relationships between IQ and cortical thickness implies independent associations between measures of intelligence and brain structure in ASD versus typically developing controls. We discuss these findings vis-à-vis prior results obtained utilizing univariate methods.
autism spectrum disorders; kernel canonical correlation analysis; varimax rotation; cortical thickness; developmental change
A central challenge in the fMRI based study of functional connectivity is distinguishing neuronally related signal fluctuations from the effects of motion, physiology, and other nuisance sources. Conventional techniques for removing nuisance effects include modeling of noise time courses based on external measurements followed by temporal filtering. These techniques have limited effectiveness. Previous studies have shown using multi-echo fMRI that neuronally related fluctuations are Blood Oxygen Level Dependent (BOLD) signals that can be characterized in terms of changes in R2* and initial signal intensity (S0) based on the analysis of echo-time (TE) dependence. We hypothesized that if TE-dependence could be used to differentiate BOLD and non-BOLD signals, non-BOLD signal could be removed to denoise data without conventional noise modeling. To test this hypothesis, whole brain multi-echo data were acquired at 3 TEs and decomposed with Independent Components Analysis (ICA) after spatially concatenating data across space and TE. Components were analyzed for the degree to which their signal changes fit models for R2* and S0 change, and summary scores were developed to characterize each component as BOLD-like or not BOLD-like. These scores clearly differentiated BOLD-like “functional network” components from non BOLD-like components related to motion, pulsatility, and other nuisance effects. Using non BOLD-like component time courses as noise regressors dramatically improved seed-based correlation mapping by reducing the effects of high and low frequency non-BOLD fluctuations. A comparison with seed-based correlation mapping using conventional noise regressors demonstrated the superiority of the proposed technique for both individual and group level seed-based connectivity analysis, especially in mapping subcortical-cortical connectivity. The differentiation of BOLD and non-BOLD components based on TE-dependence was highly robust, which allowed for the identification of BOLD-like components and the removal of non BOLD-like components to be implemented as a fully automated procedure.
fMRI; Resting state; Multi-echo; BOLD; TE dependence; ICA; Denoising; Subcortical; Connectivity
Primate inferior temporal (IT) cortex is thought to contain a high-level representation of objects at the interface between vision and semantics. This suggests that the perceived similarity of real-world objects might be predicted from the IT representation. Here we show that objects that elicit similar activity patterns in human IT (hIT) tend to be judged as similar by humans. The IT representation explained the human judgments better than early visual cortex, other ventral-stream regions, and a range of computational models. Human similarity judgments exhibited category clusters that reflected several categorical divisions that are prevalent in the IT representation of both human and monkey, including the animate/inanimate and the face/body division. Human judgments also reflected the within-category representation of IT. However, the judgments transcended the IT representation in that they introduced additional categorical divisions. In particular, human judgments emphasized human-related additional divisions between human and non-human animals and between man-made and natural objects. hIT was more similar to monkey IT than to human judgments. One interpretation is that IT has evolved visual-feature detectors that distinguish between animates and inanimates and between faces and bodies because these divisions are fundamental to survival and reproduction for all primate species, and that other brain systems serve to more flexibly introduce species-dependent and evolutionarily more recent divisions.
object perception; vision; neuronal representation; fMRI; representational similarity analysis; human; primate
About 150 researchers around the world convened at the Chateau Lake Louise on Feb 20-23, 2011 to present and discuss the latest research in human and animal imaging and spectroscopy at field strengths of 7 Tesla or above (termed Ultra High Field or UHF) at the third ISMRM-sponsored high field workshop. The clear overall message from the workshop presentations and discussion is that UHF imaging is gaining momentum with regard to new clinically relevant findings, anatomic and fMRI results, susceptibility contrast advancements, solutions to high field related image quality challenges, and to generally pushing the limits of resolution and speed of high field imaging. This meeting report is organized in a manner reflecting the meeting organization itself, covering the seven sessions that were approximately titled: 1. High field overview from head to body to spectroscopy. 2. Susceptibility imaging. 3. Proffered session on susceptibility, ultra fast imaging, unique contrast at 7T and angiography. 4. Neuroscience applications. 5. Proffered session on coils, shimming, parallel imaging, diffusion tensor imaging, and MRI-PET fusion, 6. High field animal imaging and spectroscopy, as well as a vendor overview, and 7. Cutting edge technology at 7T.
Human inferior temporal cortex contains category-selective visual regions, including the fusiform face area (FFA) and the parahippocampal place area (PPA). These regions are defined by their greater category-average activation to the preferred category (faces and places, respectively) relative to nonpreferred categories. The approach of investigating category-average activation has left unclear to what extent category selectivity holds for individual object images. Here we investigate single-image activation profiles to address (1) whether each image from the preferred category elicits greater activation than any image outside the preferred category (categorical ranking), (2) whether there are activation differences within and outside the preferred category (gradedness), and (3) whether the activation profile falls off continuously across the category boundary or exhibits a discontinuity at the boundary (category step). We used functional magnetic resonance imaging to measure the activation elicited in the FFA and PPA by each of 96 object images from a wide range of categories, including faces and places, but also humans and animals, and natural and manmade objects. Results suggest that responses in FFA and PPA exhibit almost perfect categorical ranking, are graded within and outside the preferred category, and exhibit a category step. The gradedness within the preferred category was more pronounced in FFA; the category step was more pronounced in PPA. These findings support the idea that these regions have category-specific functions, but are also consistent with a distributed object representation emphasizing categories while still distinguishing individual images.
We make a few additional points regarding our discussion with Sirotin and Das’ 2009 Nature paper and their 2010 Neuroimage response to our commentary. While we find their data interesting in itself, we remain concerned with how the data are interpreted by the authors. We discuss two categories of methodological issues that limit the conclusions one can draw from their results. (1) The measures of fit quality between the optical and electrical data: kernel shape variation, variance of predicted/measured signals, and R2, interact with each other and are confounded by the fact that one condition has a lower signal magnitude and therefore, lower signal-to-noise-ratio (SNR). (2) Hemodynamic responses to distinct events will be incorrectly or inefficiently estimated if the hemodynamic responses overlap across periodic trials that are not jittered and have an inter-trial interval less than 15 seconds. Most importantly, the overlapping responses across trials might cause transient effects that look similar to the anticipatory effects presented by Sirotin and Das. While their study demonstrates a potentially useful way to probe neurovascular coupling, we believe the current results have little practical relevance for interpreting hemodynamic measures of neural activity such as those used in fMRI. We conclude by making several suggestions for future analyses, which might help elucidate the mechanisms behind these observations and lead to a better understanding of how these observations relate to hemodynamic based measures of neural activation.
In their 2009 Nature article: “Anticipatory haemodynamic signals in sensory cortex not predicted by local neuronal activity,” Yevginiy Sirotin and Aniruddha Das suggest that hemodynamic signals, the basis of functional MRI (fMRI), can arise without any measurable neuronal activity. They report that hemodynamic signals in visual cortex were associated with and time-locked to the anticipation of a visual stimulus, and importantly, without any associated neuronal activity as measured with direct electrophysiological recordings. In this commentary, we demonstrate, using an assessment of their own data, that their claims are not strongly supported. In fact, we found that specific LFP frequency ranges predicted with a high degree of accuracy, the “dark” or “anticipatory” hemodynamic response. For other frequency ranges, we found differences in phase but not magnitude of the measured and predicted hemodynamic response. Importantly, when comparing simply the magnitude as well as the time series standard deviation of the electrophysiological recordings with those of the measured hemodynamic responses, we found a direct correspondence of the dark/stimulated magnitude and standard deviation between the electrophysiological recordings and the hemodynamic responses. All of these analyses strongly imply that anticipatory hemodynamic responses are, in fact, accurately predicted in phase and magnitude by several LFP frequency bands, and are predicted in standard deviation and magnitude by the standard deviation and magnitude of even a wider range of LFP frequencies. We argue that rather than casting doubt on fMRI signal changes, these studies open up an interesting window into exploring more subtle neurovascular relationships.
Aerobic activity is a powerful stimulus for improving mental health and for generating structural changes in the brain. We review the literature documenting these structural changes and explore exactly where in the brain these changes occur as well as the underlying substrates of the changes including neural, glial, and vasculature components. Aerobic activity has been shown to produce different types of changes in the brain. The presence of novel experiences or learning is an especially important component in how these changes are manifest. We also discuss the distinct time courses of structural brain changes with both aerobic activity and learning as well as how these effects might differ in diseased and elderly groups.
exercise; plasticity; hippocampus; neurogenesis; angiogenesis; learning; environmental enrichment; aging
A multimodal neuroimaging study of virtual spatial navigation extends the role of the hippocampal theta rhythm to human memory and self-directed learning.
The hippocampus is crucial for episodic or declarative memory and the theta rhythm has been implicated in mnemonic processing, but the functional contribution of theta to memory remains the subject of intense speculation. Recent evidence suggests that the hippocampus might function as a network hub for volitional learning. In contrast to human experiments, electrophysiological recordings in the hippocampus of behaving rodents are dominated by theta oscillations reflecting volitional movement, which has been linked to spatial exploration and encoding. This literature makes the surprising cross-species prediction that the human hippocampal theta rhythm supports memory by coordinating exploratory movements in the service of self-directed learning. We examined the links between theta, spatial exploration, and memory encoding by designing an interactive human spatial navigation paradigm combined with multimodal neuroimaging. We used both non-invasive whole-head Magnetoencephalography (MEG) to look at theta oscillations and Functional Magnetic Resonance Imaging (fMRI) to look at brain regions associated with volitional movement and learning. We found that theta power increases during the self-initiation of virtual movement, additionally correlating with subsequent memory performance and environmental familiarity. Performance-related hippocampal theta increases were observed during a static pre-navigation retrieval phase, where planning for subsequent navigation occurred. Furthermore, periods of the task showing movement-related theta increases showed decreased fMRI activity in the parahippocampus and increased activity in the hippocampus and other brain regions that strikingly overlap with the previously observed volitional learning network (the reverse pattern was seen for stationary periods). These fMRI changes also correlated with participant's performance. Our findings suggest that the human hippocampal theta rhythm supports memory by coordinating exploratory movements in the service of self-directed learning. These findings directly extend the role of the hippocampus in spatial exploration in rodents to human memory and self-directed learning.
Neural activity both within and across brain regions can oscillate in different frequency ranges (such as alpha, gamma, and theta frequencies), and these different ranges are associated with distinct functions. In behaving rodents, for example, theta rhythms (4–12 Hz) in the hippocampus are prominent during the initiation of movement and have been linked to spatial exploration. Recent evidence in humans, however, suggests that the human hippocampus is involved in guiding self-directed learning. This suggests that the human hippocampal theta rhythm supports memory by coordinating exploratory movements in the service of self-directed learning. In this study, we tested whether there is a human analogue for the movement-initiation-related theta rhythm found in the rodent hippocampus by using a virtual navigation paradigm, combined with non-invasive recordings and functional imaging techniques. Our recordings showed that, indeed, theta power increases are linked to movement initiation. We also examined the relationship to memory encoding, and we found that hippocampal theta oscillations related to pre-retrieval planning predicted memory performance. Imaging results revealed that periods of the task showing movement-related theta also showed increased activity in the hippocampus, as well as other brain regions associated with self-directed learning. These findings directly extend the role of the hippocampal theta rhythm in rodent spatial exploration to human memory and self-directed learning.
A popular method for investigating whether stimulus information is present in fMRI response patterns is to attempt to “decode” the stimuli from the response patterns with a multivariate classifier. The sensitivity for detecting the information depends on the particular classifier used. However, little is known about the relative performance of different classifiers on fMRI data. Here we compared six multivariate classifiers and investigated how the response-amplitude estimate used (beta or t-value) and different pattern normalizations affect classification performance. The compared classifiers were a pattern-correlation classifier, a k-nearest-neighbors classifier, Fisher’s linear discriminant, Gaussian naïve Bayes, and linear and nonlinear (radial-basis-function-kernel) support vector machines. We compared these classifiers’ accuracy at decoding the category of visual objects from response patterns in human early visual and inferior temporal cortex acquired in an event-related design with BOLD fMRI at 3T using SENSE and isotropic voxels of about 2-mm width. Overall, Fisher’s linear discriminant (with an optimal-shrinkage covariance estimator) and the linear support vector machine performed best. The pattern-correlation classifier often performed similarly as those two classifiers. The nonlinear classifiers never performed better and sometimes significantly worse than the linear classifiers, suggesting overfitting. Defining response patterns by t-values (or in error-standard-deviation units) rather than by beta estimates (in % signal change) to define the patterns appeared advantageous. Cross-validation by a leave-one-stimulus-pair-out method gave higher accuracies than a leave-one-run-out method, suggesting that generalization to independent runs (which more safely ensures independence of the test set) is more challenging than generalization to novel stimuli within the same category. Independent selection of fewer more visually responsive voxels tended to yield better decoding performance for all classifiers. Normalizing mean and standard deviation of the response patterns either across stimuli or across voxels had no significant effect on decoding performance. Overall our results suggest that linear decoders based on t-value patterns may perform best in the present scenario of visual object representations measured for about 60-minutes per subject with 3T fMRI.
Multi-voxel pattern analysis; decoding; classification analysis; fMRI; normalization
Face recognition is a complex cognitive process that requires distinguishable neuronal representations of individual faces. Previous functional magnetic resonance imaging (fMRI) studies using the “fMRI-adaptation” technique have suggested the existence of face-identity representations in face-selective regions, including the fusiform face area (FFA). Here, we present face-identity adaptation findings that are not well explained in terms of face-identity representations. We performed blood-oxygen level–dependent (BOLD) fMRI measurements, while participants viewed familiar faces that were shown repeatedly throughout the experiment. We found decreased activation for repeated faces in face-selective regions, as expected based on previous studies. However, we found similar effects in regions that are not face-selective, including the parahippocampal place area (PPA) and early visual cortex (EVC). These effects were present for exact-image (same view and lighting) as well as different-image (different view and/or lighting) repetition, but more widespread for exact-image repetition. Given the known functional properties of PPA and EVC, it appears unlikely that they contain domain-specific face-identity representations. Alternative interpretations include general attentional effects and carryover of activation from connected regions. These results remind us that fMRI stimulus-change effects can have a range of causes and do not provide conclusive evidence for a neuronal representation of the changed stimulus property.
attention; face-identity; fMRI-adaptation; fusiform face area; neuronal representation