Brain Areas Associated with Force Steadiness and Intensity During Isometric Ankle Dorsiflexion in Men and Women

Although maintenance of steady contractions is required for many daily tasks, there is little understanding of brain areas that modulate lower limb force accuracy. Functional magnetic resonance imaging was used to determine brain areas associated with steadiness and force during static (isometric) lower limb target-matching contractions at low and high intensities. Fourteen young adults (6 men and 8 women; 27.1 ± 9.1 years) performed three sets of 16-s isometric contractions with the ankle dorsiflexor muscles at 10, 30, 50, and 70 % of maximal voluntary contraction (MVC). Percent signal changes (PSCs, %) of the blood oxygenation level-dependent response were extracted for each contraction using region of interest analysis. Mean PSC increased with contraction intensity in the contralateral primary motor area (M1), supplementary motor area, putamen, pallidum cingulate cortex, and ipsilateral cerebellum (p \u3c 0.05). The amplitude of force fluctuations (standard deviation, SD) increased from 10 to 70 % MVC but relative to the mean force (coefficient of variation, CV %) was greatest at 10 % MVC. The CV of force was associated with PSC in the ipsilateral parietal lobule (r = −0.28), putamen (r = −0.29), insula (r = −0.33), and contralateral superior frontal gyrus (r = −0.33, p \u3c 0.05). There were minimal sex differences in brain activation across the isometric motor tasks indicating men and women were similarly motivated and able to activate cortical motor centers during static tasks. Control of steady lower limb contractions involves cortical and subcortical motor areas in both men and women and provides insight into key areas for potential cortical plasticity with impaired or enhanced leg function

Function-based Intersubject Alignment of Human Cortical Anatomy

Making conclusions about the functional neuroanatomical organization of the human brain requires methods for relating the functional anatomy of an individual's brain to population variability. We have developed a method for aligning the functional neuroanatomy of individual brains based on the patterns of neural activity that are elicited by viewing a movie. Instead of basing alignment on functionally defined areas, whose location is defined as the center of mass or the local maximum response, the alignment is based on patterns of response as they are distributed spatially both within and across cortical areas. The method is implemented in the two-dimensional manifold of an inflated, spherical cortical surface. The method, although developed using movie data, generalizes successfully to data obtained with another cognitive activation paradigm—viewing static images of objects and faces—and improves group statistics in that experiment as measured by a standard general linear model (GLM) analysis

The coordinate-based meta-analysis of neuroimaging data

Neuroimaging meta-analysis is an area of growing interest in statistics. The special characteristics of neuroimaging data render classical meta-analysis methods inapplicable and therefore new methods have been developed. We review existing methodologies, explaining the benefits and drawbacks of each. A demonstration on a real dataset of emotion studies is included. We discuss some still-open problems in the field to highlight the need for future research

Self-Regulation of Amygdala Activation Using Real-Time fMRI Neurofeedback

Real-time functional magnetic resonance imaging (rtfMRI) with neurofeedback allows investigation of human brain neuroplastic changes that arise as subjects learn to modulate neurophysiological function using real-time feedback regarding their own hemodynamic responses to stimuli. We investigated the feasibility of training healthy humans to self-regulate the hemodynamic activity of the amygdala, which plays major roles in emotional processing. Participants in the experimental group were provided with ongoing information about the blood oxygen level dependent (BOLD) activity in the left amygdala (LA) and were instructed to raise the BOLD rtfMRI signal by contemplating positive autobiographical memories. A control group was assigned the same task but was instead provided with sham feedback from the left horizontal segment of the intraparietal sulcus (HIPS) region. In the LA, we found a significant BOLD signal increase due to rtfMRI neurofeedback training in the experimental group versus the control group. This effect persisted during the Transfer run without neurofeedback. For the individual subjects in the experimental group the training effect on the LA BOLD activity correlated inversely with scores on the Difficulty Identifying Feelings subscale of the Toronto Alexithymia Scale. The whole brain data analysis revealed significant differences for Happy Memories versus Rest condition between the experimental and control groups. Functional connectivity analysis of the amygdala network revealed significant widespread correlations in a fronto-temporo-limbic network. Additionally, we identified six regions — right medial frontal polar cortex, bilateral dorsomedial prefrontal cortex, left anterior cingulate cortex, and bilateral superior frontal gyrus — where the functional connectivity with the LA increased significantly across the rtfMRI neurofeedback runs and the Transfer run. The findings demonstrate that healthy subjects can learn to regulate their amygdala activation using rtfMRI neurofeedback, suggesting possible applications of rtfMRI neurofeedback training in the treatment of patients with neuropsychiatric disorders

Methods for Identifying Regions of Brain Activation Using FMRI Meta-Data

Functional neuroimaging is a relatively young discipline within the neurosciences that has led to significant advances in our understanding of the human brain and progress in neuroscientific research related to public health. Accurately identifying activated regions in the brain showing a strong association with an outcome of interest is crucial in terms of disease prediction and prevention. Functional magnetic resonance imaging (fMRI) is the most widely used method for this type of study as it has the ability to measure and identify the location of changes in tissue perfusion, blood oxygenation, and blood volume. In practice, the three-dimensional brain locations or coordinates of the local maximum of these changes are reported. By nature, fMRIs are noninvasive, slowly becoming more available, have relatively high spatiotemporal resolution, and have the remarkable ability to map the entire network of the brain’s function during the thought process. However, due to their high costs, fMRI studies tend to have a very small number of participants, which cause inflated type II error and lack reproducibility. This gives rise to the need for fMRI meta analyzes, which combines studies in order to increase overall sample size and testing power. In this dissertation, two methods are proposed that aim to identify regions of brain activation using fMRI coordinate-based meta analysis; a spatial Cox process and a mixture of Dirichlet processes model. The first method was motivated by the desire to identify significant regions of brain activation using fMRI coordinate-based meta data. To identify these regions we elected to implement a Bayesian spatial Cox process. We considered two levels of clustering, latent foci center and study activation center, utilizing the Dirichlet process (DP) built into a spatial Cox process used to model the distribution of foci. Commonly used spatial clustering methods model the random variation of the intensity governed by a process such that peaks in these processes would relate to areas of elevated aggregation in the events. However, methods of this type all assume three-dimensional normality, which is inappropriate for fMRI due to the nature of brain functioning and brain structure, and can possibly cause misclassification of foci and increase error in prediction and estimation. We relax this normality assumption and model intensity as a function of distance between the focus and the center of the cluster of foci using Gaussian kernels and the foci center will be identified by the use of a Dirichlet process. Simulation studies were conducted to evaluate the sensitivity and robustness with respect to cluster identification and underlying data distributions. An additional application of the proposed method was applied to an fMRI meta data of emotion foci. Both simulations and real data application produced promising results that highlighted the ability to correctly cluster. The second method was motivated by the spatial Cox process’ inability to statistically distinguish between clusters via a limitation to the Dirichlet process. However, it still aimed to identify significant regions of brain activation. This method modeled the realization of the data as a linear association with the overall mean of the data and adjusts for some study effect. The mean of the data was modeled as a mixture of unknown finite number of components and adjusted for a study effect modeled as a Dirichlet process. Similarly, each component was modeled as a Dirichlet process. Conditional on the mean of the data and some study effect, the distribution of the random error is standard multivariate normal. By modeling the mean as a mixture of Dirichlet processes, this still allows the method flexibility in capturing irregular spatial patterns and relaxes the typical normality assumptions, but can also statistically distinguish between a cluster or a mode within a cluster. The Bayesian framework was again implemented to draw model inferences. Simulation studies were conducted to explore the sensitivity and robustness of the method, but illustrated a mediocre ability to correctly identify clusters. As an additional application, we applied the proposed method to the same fMRI meta data as was done in the first proposed method. The number of clusters identified were significantly lower and cluster centers identified were not in close proximity to any of those identified in the first proposed method. Both simulation studies and real data applications indicate this second proposed method is not sensitive enough to correctly identify clusters

Using Multivariate Pattern Analysis to Identify Conceptual Knowledge Representation in the Brain

Representation of semantic knowledge is an important aspect of cognitive function. The processing of concrete (e.g., book) and abstract (e.g., freedom) semantic concepts show systematic differences on various behavioral measures in both healthy and clinical populations. However, previous studies examining the difference in the neural substrates correlating with abstract and concrete concept representations have reached inconsistent conclusions. This dissertation used multiple novel data analyses approaches on functional magnetic resonance imaging (fMRI) data, to investigate representational differences of abstract and concrete concepts and to provide converging evidence that the representations of abstract and concrete semantic knowledge in the brain rely on different mechanisms. Study 1 used meta-analysis method on a combined sample of 303 participants to quantitatively summarize the published neuroimaging studies on the brain regions with category-specific activations. Results suggested greater engagement of working memory and language system for processing abstract concepts, and greater engagement of the visual perceptual system for processing of concrete concepts, likely via mental imagery. Study 2 showed successful identifications of single trial fMRI data as being associated with the processing of either abstract or concrete concepts based on multivoxel activity patterns in widespread brain areas, suggesting that abstract vs. concrete differences were represented by multiple mechanisms. Study 3 investigated the classification based on condition-specific connectivity patterns. Results showed successful identifications of the connectivity patterns as abstract or concrete for an individual based on the connectivity patterns of other individuals, both by the connectivity for a priory selected seed regions as well as by the whole-brain voxel-by-voxel connectivity patterns. The results indicated the existence of condition-specific connectivity patterns that were consistent across individuals on a whole-brain scale. Moreover, the results also suggested the representation of abstract and concrete concepts differs from the semantic association perspective in addition to differences on coding forms. Study 4 illustrated the application of MVPA as a cross-modal prediction approach, which is a promising method for further investigation of semantic knowledge representation in the brain, by investigating the role of general semantic system on person-specific knowledge. Overall, the work described in this dissertation provides converging evidence of the representational difference between abstract and concrete concepts. The differences are suggested to occur at various levels, including the dependence on modality-specific perceptual systems, the organization of associations among different semantic-related systems, and the difficulty and strategy of retrieving contextual information

Incorporating structured assumptions with probabilistic graphical models in fMRI data analysis

With the wide adoption of functional magnetic resonance imaging (fMRI) by cognitive neuroscience researchers, large volumes of brain imaging data have been accumulated in recent years. Aggregating these data to derive scientific insights often faces the challenge that fMRI data are high-dimensional, heterogeneous across people, and noisy. These challenges demand the development of computational tools that are tailored both for the neuroscience questions and for the properties of the data. We review a few recently developed algorithms in various domains of fMRI research: fMRI in naturalistic tasks, analyzing full-brain functional connectivity, pattern classification, inferring representational similarity and modeling structured residuals. These algorithms all tackle the challenges in fMRI similarly: they start by making clear statements of assumptions about neural data and existing domain knowledge, incorporating those assumptions and domain knowledge into probabilistic graphical models, and using those models to estimate properties of interest or latent structures in the data. Such approaches can avoid erroneous findings, reduce the impact of noise, better utilize known properties of the data, and better aggregate data across groups of subjects. With these successful cases, we advocate wider adoption of explicit model construction in cognitive neuroscience. Although we focus on fMRI, the principle illustrated here is generally applicable to brain data of other modalities.Comment: update with the version accepted by Neuropsychologi

Neural Correlates of Long-Term Memory Enhancement Following Retrieval Practice

Retrieval practice, relative to further study, leads to long-term memory enhancement known as the "testing effect." The neurobiological correlates of the testing effect at retrieval, when the learning benefits of testing are expressed, have not been fully characterized. Participants learned Swahili-English word-pairs and were assigned randomly to either the Study-Group or the Test-Group. After a week delay, all participants completed a cued-recall test while undergoing functional magnetic resonance imaging (fMRI). The Test-Group had superior memory for the word-pairs compared to the Study-Group. While both groups exhibited largely overlapping activations for remembered word-pairs, following an interaction analysis the Test-Group exhibited differential performance-related effects in the left putamen and left inferior parietal cortex near the supramarginal gyrus. The same analysis showed the Study-Group exhibited greater activations in the dorsal MPFC/pre-SMA and bilateral frontal operculum for remembered vs. forgotten word-pairs, whereas the Test-Group showed the opposite pattern of activation in the same regions. Thus, retrieval practice during training establishes a unique striatal-supramarginal network at retrieval that promotes enhanced memory performance. In contrast, study alone yields poorer memory but greater activations in frontal regions.This study was partially funded by the Basque Government and the MIT Integrated Learning Initiative