Decoding accuracy in supplementary motor cortex correlates with perceptual sensitivity to tactile roughness

Perceptual sensitivity to tactile roughness varies across individuals for the same degree of roughness. A number of neurophysiological studies have investigated the neural substrates of tactile roughness perception, but the neural processing underlying the strong individual differences in perceptual roughness sensitivity remains unknown. In this study, we explored the human brain activation patterns associated with the behavioral discriminability of surface texture roughness using functional magnetic resonance imaging (fMRI). First, a wholebrain searchlight multi-voxel pattern analysis (MVPA) was used to find brain regions from which we could decode roughness information. The searchlight MVPA revealed four brain regions showing significant decoding results: the supplementary motor area (SMA), contralateral postcentral gyrus (S1), and superior portion of the bilateral temporal pole (STP). Next, we evaluated the behavioral roughness discrimination sensitivity of each individual using the just-noticeable difference (JND) and correlated this with the decoding accuracy in each of the four regions. We found that only the SMA showed a significant correlation between neuronal decoding accuracy and JND across individuals; Participants with a smaller JND (i.e., better discrimination ability) exhibited higher decoding accuracy from their voxel response patterns in the SMA. Our findings suggest that multivariate voxel response patterns presented in the SMA represent individual perceptual sensitivity to tactile roughness and people with greater perceptual sensitivity to tactile roughness are likely to have more distinct neural representations of different roughness levels in their SMA. © 2015 Kim et al.close0

Investigation of working memory representations bridging perception & action in the somatosensory domain

This dissertation comprises original experimental work exploring the intermediate stages of the perception-action loop in the somatosensory domain. The stages correspond to the maintenance of working memory (WM) content, the goal-directed manipulation of the content, and the formation of memory-based decisions. Concurrently, the thesis addresses ongoing debates in cognitive neuroscience, specifically, the localization of the respective WM- and decision-making content. For both debates, the central issue rests with the extent of influence that experimental design has on the localization of the resulting representations. By taking advantage of modifications of experimental paradigms, advanced whole-brain data analysis techniques, and the extensive literature in the somatosensory domain, the dissertation provides evidence in favour of the distributed representation of WM content. The distribution of WM representations is not limited to either frontal or sensory regions. Indeed, the fronto-parietal network - specifically the intraparietal sulcus, inferior frontal gyrus, and the premotor cortex - is necessary for the successful performance of the intermediate stages of the perception-action loop. Therefore, the maintenance of WM content, the manipulation and maintenance of the resulting content, and the computation of the decision variable, all take place in a network consisting primarily of frontal and parietal regions with the specific distribution of WM content depending on the experimental paradigm

FUNCTIONAL NETWORK CONNECTIVITY IN HUMAN BRAIN AND ITS APPLICATIONS IN AUTOMATIC DIAGNOSIS OF BRAIN DISORDERS

The human brain is one of the most complex systems known to the mankind. Over the past 3500 years, mankind has constantly investigated this remarkable system in order to understand its structure and function. Emerging of neuroimaging techniques such as functional magnetic resonance imaging (fMRI) have opened a non-invasive in-vivo window into brain function. Moreover, fMRI has made it possible to study brain disorders such as schizophrenia from a different angle unknown to researchers before. Human brain function can be divided into two categories: functional segregation and integration. It is well-understood that each region in the brain is specialized in certain cognitive or motor tasks. The information processed in these specialized regions in different temporal and spatial scales must be integrated in order to form a unified cognition or behavior. One way to assess functional integration is by measuring functional connectivity (FC) among specialized regions in the brain. Recently, there is growing interest in studying the FC among brain functional networks. This type of connectivity, which can be considered as a higher level of FC, is termed functional network connectivity (FNC) and measures the statistical dependencies among brain functional networks. Each functional network may consist of multiple remote brain regions. Four studies related to FNC are presented in this work. First FNC is compared during the resting-state and auditory oddball task (AOD). Most previous FNC studies have been focused on either resting-state or task-based data but have not directly compared these two. Secondly we propose an automatic diagnosis framework based on resting-state FNC features for mental disorders such as schizophrenia. Then, we investigate the proper preprocessing for fMRI time-series in order to conduct FNC studies. Specifically the impact of autocorrelated time-series on FNC will be comprehensively assessed in theory, simulation and real fMRI data. At the end, the notion of autoconnectivity as a new perspective on human brain functionality will be proposed. It will be shown that autoconnectivity is cognitive-state and mental-state dependent and we discuss how this source of information, previously believed to originate from physical and physiological noise, can be used to discriminate schizophrenia patients with high accuracy

Somatic and Vicarious Pain are Represented by Dissociable Multivariate Brain Patterns

Understanding how humans represent others’ pain is critical for understanding pro-social behavior. ‘Shared experience’ theories propose common brain representations for somatic and vicarious pain, but other evidence suggests that specialized circuits are required to experience others’ suffering. Combining functional neuroimaging with multivariate pattern analyses, we identified dissociable patterns that predicted somatic (high versus low: 100%) and vicarious (high versus low: 100%) pain intensity in out-of-sample individuals. Critically, each pattern was at chance in predicting the other experience, demonstrating separate modifiability of both patterns. Somatotopy (upper versus lower limb: 93% accuracy for both conditions) was also distinct, located in somatosensory versus mentalizing-related circuits for somatic and vicarious pain, respectively. Two additional studies demonstrated the generalizability of the somatic pain pattern (which was originally developed on thermal pain) to mechanical and electrical pain, and also demonstrated the replicability of the somatic/vicarious dissociation. These findings suggest possible mechanisms underlying limitations in feeling others’ pain, and present new, more specific, brain targets for studying pain empathy

Brain Representations of Dexterous Hand Control: Investigating the Functional Organization of Individuated Finger Movements and Somatosensory Integration

Using our hands to manipulate objects in our daily life requires both dexterous movements and the integration of somatosensory information across fingers. Although the primary motor (M1) and somatosensory cortices (S1) are critical for these two complementary roles, it is unclear how neural populations in these regions functionally represent these processes. This thesis examined the functional organization of brain representations (the representational geometry) in M1 and S1 for dexterous hand control and somatosensory processing. To that end, representational geometries were estimated from fine-grained brain activity patterns measured with functional MRI (fMRI). Since fMRI measures a blood-based proxy of neural activity, any non-linearities in the coupling between neural activity and the fMRI signal could distort the representational geometries. Chapter 2 therefore evaluated the stability of representational geometries. Human participants made individuated finger presses at varying pressing speeds, such that overall activity was modulated across a broad range. Representational geometries were relatively stable across pressing speeds in M1 and S1, validating the use of this analysis framework with fMRI data. Chapter 3 then explored how M1 is organized for dexterous hand control. In agreement with previous research, representations of each finger were quite distinct. However, representations of the same finger moving in different directions were very similar. Insight into this observation was gained by comparing the fMRI results to neural spiking data recorded in monkeys trained to perform an identical task. By leveraging the complementary perspectives offered by fMRI and spiking, a new organization of M1 for finger control was proposed. Chapter 4 then examined how somatosensory inputs from multiple fingers are integrated in S1. The full nature of this integration is unknown. Here, human participants experienced simulation of all possible single- and multi-finger combinations. Representational model analyses revealed that unique non-linear interactions between finger sensory inputs occur throughout S1, with stronger (and more spatially distant) interactions occurring in posterior S1. Altogether, these results provide new insight into how M1 and S1 are functionally organized to serve the motoric and sensory processes of the hand, and more broadly demonstrate how fMRI can be used to make inferences about the underlying functional organization of brain representations

TMS application in both health and disease

Transcranial magnetic stimulation (TMS) can be useful for therapeutic purposes for a variety of clinical conditions. Numerous studies have indicated the potential of this noninvasive brain stimulation technique to recover brain function and to study physiological mechanisms. Following this line, the articles contemplated in this Research Topic show that this field of knowledge is rapidly expanding and considerable advances have been made in the last few years. There are clinical protocols already approved for Depression (and anxiety comorbid with major depressive disorder), Obsessive compulsive Disorder (OCD), migraine headache with aura, and smoking cessation treatment but many studies are concentrating their efforts on extending its application to other diseases, e.g., as a treatment adjuvant. In this Research Topic we have the example of using TMS for pain, post-stroke depression, or smoking cessation, but other diseases/injuries of the central nervous system need attention (e.g., tinnitus or the surprising epilepsy). Further, the potential of TMS in health is being explored, in particular regarding memory enhancement or the mapping of motor control regions, which might also have implications for several diseases. TMS is a non-invasive brain stimulation technique that can be used for modulating brain activation or to study connectivity between brain regions. It has proven efficacy against neurological and neuropsychiatric illnesses but the response to this stimulation is still highly variable. Research works devoted to studying the response variability to TMS, as well as large-scale studies demonstrating its efficacy in different sub-populations, are therefore of utmost importance. In this editorial, we summarize the main findings and viewpoints detailed within each of the 12 contributing articles using TMS for health and/or disease applications.publishe

A sensitive and specific neural signature for picture-induced negative affect

Neuroimaging has identified many correlates of emotion but has not yet yielded brain representations predictive of the intensity of emotional experiences in individuals. We used machine learning to identify a sensitive and specific signature of emotional responses to aversive images. This signature predicted the intensity of negative emotion in individual participants in cross validation (n =121) and test (n = 61) samples (high–low emotion = 93.5% accuracy). It was unresponsive to physical pain (emotion–pain = 92% discriminative accuracy), demonstrating that it is not a representation of generalized arousal or salience. The signature was comprised of mesoscale patterns spanning multiple cortical and subcortical systems, with no single system necessary or sufficient for predicting experience. Furthermore, it was not reducible to activity in traditional “emotion-related” regions (e.g., amygdala, insula) or resting-state networks (e.g., “salience,” “default mode”). Overall, this work identifies differentiable neural components of negative emotion and pain, providing a basis for new, brain-based taxonomies of affective processes

Novel Paradigms For Visual Field Mapping With Functional Magnetic Resonance Imaging

The overall goal of this study is to evaluate the existing, and develop new visual field mapping paradigms, which consist of visual stimulation scheme, post-processing and displaying tools using fMRI for both research and clinical applications. We first directly compared phase mapping and random multifocal mapping paradigms with respect to clinically relevant factors. Multifocal mapping was superior in immunity to noise and was able to accurately decompose the response of single voxels to multiple stimulus locations. In contrast, phase mapping activated more extrastriate visual areas and was more efficient per run in achieving a statistically efficient response in a minimum time but required separate runs to map eccentricity and angle. Multifocal mapping became less efficient as the number of simultaneous stimulus locations increased from 13 to 25 to 49 and when duty cycle increased from 25% to 50%. In sum, each paradigm offers advantages that may be optimal for different applications. Given the respective advantages and weaknesses of phase-encoded design and random multifocal design, we further developed a novel paradigm by combining the phase-encoded stimuli and one or two isolated random segments. The addition of the random stimuli was shown to have insignificant effect on the retinotopic mapping by the phase-encoded stimuli. Three applications were demonstrated for this combined paradigm: Simultaneous mapping the retinotopy and selected ROIs, automated calibration of the temporal phases, and delineation of the hemodynamic response function for selected voxels. At present the representation of the visual field by the visual cortex is displayed as a diagram of a subject\u27s visual field with circular symbols placed at locations to which voxels have shown a response. The diagram provides an intuitive way of interpreting the fMRI cortical maps in terms of visual function. However, it provides little information about the relative probability of obtaining a brain response from different locations within the field of view. Therefore, we derived a quantitative form of such a diagram, on which a probability distribution could be drawn. The quantitative diagrams from five subjects showed highly variable patterns of coverage, which made it questionable whether any meaningful probabilistic distribution can be obtained