The evolution of human-specific lateralised functions such as language has been linked to the development of structural asymmetries in the brain. Here we applied state of the art image analysis techniques to measure Sylvian Fissure (SF) asymmetry and Occipital Bending (OB) in 3D Magnetic Resonance (MR) images of the brain obtained in-vivo for 30 humans and 30 chimpanzees (Pan troglodytes). SF morphology differed between species, with the human SF terminating more superiorly in right inferior parietal lobe, an asymmetry that was on average absent in chimpanzees (F (1,52) = 5.963, p = 0.018). Irrespective of morphology, Total SF Length was, as previously reported, leftward in humans but not in chimpanzees, although the difference did not reach significance between species. However, when only brains possessing comparable bilateral SF bifurcation morphology were compared, humans showed previously reported "Typical" left-lateralised Anterior-Horizontal (AH-SF) and right-lateralised Vertical (V-SF) SF asymmetries. In contrast, chimpanzees lacked both asymmetries, and this approached being a significant difference between-species in the AH-SF segment (F (1, 34) = 3.680, p = 0.064). On average in humans the left occipital lobe crossed the midline toward the right (Rightward OB) which was significantly different from the chimpanzee cohort that showed no average OB (Independent-Samples Mann-Whitney U Test, p = 0.012). Furthermore, OB was related to SF asymmetry in humans, such that the more rightward V-SF and leftward AH-SF, the more rightward the OB. This "Default" pattern of SF and OB asymmetries was found in 41.7% of human individuals with bilateral SF bifurcation but none of the chimpanzees. To our knowledge, this is the first study highlighting that a pattern of SF and OB asymmetry distinguishes the human from the chimpanzee brain, and suggests this may be associated with a unique trajectory of brain development and functional abilities in humans.
Multivoxel pattern analysis (MVPA) methods have been widely applied in recent years to classify human brain states in functional magnetic resonance imaging (fMRI) data analysis. Voxel selection plays an important role in MVPA studies not only because it can improve decoding accuracy but also because it is useful for understanding brain functions. There are many voxel selection methods that have been proposed in fMRI literature. However, most of these methods either overlook the structure information of fMRI data or require additional crossvalidation procedures to determine the hyperparameters of the models. In the present work, we proposed a voxel selection method for binary brain decoding called group sparse Bayesian logistic regression (GSBLR). This method utilizes the group sparse property of fMRI data by using a grouped automatic relevance determination (GARD) as a prior for model parameters. All the parameters in the GSBLR can be estimated automatically, thereby avoiding additional cross-validation. Experimental results based on two publicly available fMRI datasets and simulated datasets demonstrate that GSBLR achieved better classification accuracies and yielded more stable solutions than several state-of-the-art methods.
The error-related negativity (ERN) is an event-related potential in the electroencephalogram (EEG) observed within the first 100 ms after commission of an error. Increased ERN amplitudes have been observed in several psychological disorders characterized by high negative affect. While the ERN has extensively been studied in tasks using exteroceptive stimuli, its relation to interoceptive stimuli is unknown. Since errors related to interoception might be particularly relevant for survival and negative affect, this study aimed to explore the ERN for errors related to interoceptive, respiratory sensations (intERN). Moreover, we compared the intERN with a commonly observed ERN related to exteroceptive, visual stimuli (extERN) and examined their associations with interoception-related negative affect. We studied the ERN using a respiratory occlusion task (intERN) and a visual flanker task (extERN) in 40 healthy volunteers during continuous 129 channel EEG recordings. In the occlusion task, participants received inspiratory occlusions of two different durations and indicated whether each occlusion was short or long. In the Flanker task, participants indicated the direction of arrowheads. Interoception-related negative affect was assessed with the Anxiety Sensitivity Index. Comparable with the extERN, the intERN was observed at fronto-central scalp positions after error commission in the occlusion task, but it peaked significantly earlier than the extERN. Mean amplitudes of the intERN and extERN showed no significant difference and were not correlated. Moreover, higher levels of anxiety sensitivity were correlated with significantly greater amplitudes of the intERN, but with lower amplitudes of the extERN. The present results firstly demonstrate an error-related negativity EEG-potential that is related to interoceptive sensations (intERN). This intERN is not associated with a commonly observed ERN elicited by exteroceptive stimuli and is distinctly linked to higher levels of interoceptionrelated negative affect. The intERN might be a promising neural marker for future studies on interoception, negative affect and error processing.
Functional connectivity is frequently derived from fMRI data to reduce a complex image of the brain to a graph, or "functional connectome". Often shortest-path algorithms are used to characterize and compare functional connectomes. Previous work on the identification and measurement of semi-metric (shortest circuitous) pathways in the functional connectome has discovered cross-sectional differences in major depressive disorder (MDD), autism spectrum disorder (ASD), and Alzheimer's disease. However, while measurements of shortest path length have been analyzed in functional connectomes, less work has been done to investigate the composition of the pathways themselves, or whether the edges composing pathways differ between individuals. Developments in this area would help us understand how pathways might be organized in mental disorders, and if a consistent pattern can be found. Furthermore, studies in structural brain connectivity and other real-world graphs suggest that shortest pathways may not be as important in functional connectivity studies as previously assumed. In light of this, we present a novel measurement of the consistency of pathways across functional connectomes, and an algorithm for improvement by selecting the most frequently occurring "normative pathways" from the k shortest paths, instead of just the shortest path. We also look at this algorithm's effect on various graph measurements, using randomized matrix simulations to support the efficacy of this method and demonstrate our algorithm on the resting-state fMRI (rs-fMRI) of a group of 34 adolescent control participants. Additionally, a comparison of normative pathways is made with a group of 82 age-matched participants, diagnosed with MDD, and in doing so we find the normative pathways that are most disrupted. Our results, which are carried out with estimates of connectivity derived from correlation, partial correlation, and normalized mutual information connectomes, suggest disruption to the default mode, affective, and ventral attention networks. Normative pathways, especially with partial correlation, make greater use of critical anatomical pathways through the striatum, cingulum, and the cerebellum. In summary, MDD is characterized by a disruption of normative pathways of the ventral attention network, increases in alternative pathways in the frontoparietal network in MDD, and a mixture of both in the default mode network. Additionally, within-and between-groups findings depend on the estimate of connectivity.
We review a relatively new method for studying the developing brain in children and infants with Autism Spectrum Disorder (ASD). Despite advances in behavioral screening and brain imaging, due to paradigms that do not easily allow for testing of awake, very young, and socially-engaged children-i. e., the social and the baby brain-the biological underpinnings of this disorder remain a mystery. We introduce an approach based on functional near-infrared spectroscopy (fNIRS), which offers a noninvasive imaging technique for studying functional activations by measuring changes in the brain's hemodynamic properties. This further enables measurement of brain activation in upright, interactive settings, while maintaining general equivalence to fMRI findings. We review the existing studies that have used fNIRS for ASD, discussing their promise, limitations, and their technical aspects, gearing this study to the researcher who may be new to this technique and highlighting potential targets for future research.
The human brain undergoes explosive growth during the prenatal period and the first few postnatal years, establishing an early infrastructure for the later development of behaviors and cognitions. Revealing the developmental rules during the early phase is essential for understanding the emergence of brain functions and the origin of developmental disorders. Graph-theoretical network modeling in combination with multiple neuroimaging probes provides an important research framework to explore the early development of the topological wiring and organizational paradigms of the brain. Here, we reviewed studies that employed neuroimaging and graph-theoretical modeling to investigate brain network development from approximately 20 gestational weeks to 2 years of age. Specifically, the structural and functional brain networks have evolved to highly efficient topological architectures in the early stage; where the structural network remains ahead and paves the way for the development of the functional network. The brain network develops in a heterogeneous order, from primary to higher-order systems and from a tendency of network segregation to network integration in the prenatal and postnatal periods. The early brain network topologies show abilities in predicting certain cognitive and behavior performance in later life, and their impairments are likely to continue into childhood and even adulthood. These macroscopic topological changes may be associated with possible microstructural maturations, such as axonal growth and myelinations. Collectively, this review provides a detailed delineation of the early changes in the baby brains in a graph-theoretical modeling framework, which opens up a new avenue for understanding the developmental principles of the connectome.
Aging is accompanied by a decline in physical and cognitive function. Vascular aging may provide a major influence on these measures. The purpose of this study was to explore the relationship between renal oxygenation and functional connectivity of the aging brain because of the anatomic and hemodynamic similarities between cerebral and renal vessels. Fifty-two healthy older adults were recruited to undergo a BOLD-fMRI scan of the brain and kidneys, and forty-four healthy younger subjects were recruited as the control group. First, cerebral functional connectivity density (FCD) was used to evaluate functional connectivity. Renal medullary and cortical R2* values were extracted respectively, and the ratio of medullary and cortical R2* values (MCR) was calculated. Then, the association between brain FCD and renal MCR was analyzed. Compared with younger adults, the elderly group showed higher renal medullary R2* and MCR, which might reflect a slight abnormality of renal oxygenation with aging. The older subjects also showed enhanced FCD in bilateral motor-related regions and decreased FCD in regions of the default mode network (DMN). The findings indicated that the functional connectivity in the DMN and motor cortices was vulnerable to aging. Moreover, the altered brain FCD values in the watershed regions, DMN and motor cortices were significantly correlated with the renal MCR value in the elderly group. The association between renal oxygenation abnormalities and spontaneous activity in the brain might reflect vascular aging and its influence on the kidney and brain during aging to some extent. This study provided a new perspective for understanding the relationship between tissue oxygenation and brain functional connectivity.
Guilt and shame are usually evoked during interpersonal interactions. However, no study has compared guilt and shame processing under such circumstances. In the present study, we investigated guilt and shame in an interpersonal context using functional magnetic resonance imaging (fMRI). Behaviorally, participants reported more "guilt" when their wrong advice caused a confederate's economic loss, whereas they reported more "shame" when their wrong advice were correctly refused by the confederate. The fMRI results showed that both guilt and shame activated regions related to the integration of theory of mind and self-referential information (dorsal medial prefrontal cortex, dmPFC) and to the emotional processing (anterior insula). Guilt relative to shame activated regions linked with theory of mind (supramarginal gyrus and temporo-parietal junction) and cognitive control (orbitofrontal cortex/ventrolateral prefrontal cortex and dorsolateral prefrontal cortex). Shame relative to guilt revealed no significant results. Using multivariate pattern analysis, we demonstrated that in addition to the regions found in the univariate activation analysis, the ventral anterior cingulate cortex and dmPFC could also distinguish guilt and shame. These results do not only echo previous studies of guilt and shame using recall and imagination paradigms but also provide new insights into the psychological and neural mechanisms of guilt and shame.
Memory for spatial sequences does not depend solely on the number of locations to be stored, but also on the presence of spatial regularities. Here, we show that the human brain quickly stores spatial sequences by detecting geometrical regularities at multiple time scales and encoding them in a format akin to a programming language. We measured gaze-anticipation behavior while spatial sequences of variable regularity were repeated. Participants' behavior suggested that they quickly discovered the most compact description of each sequence in a language comprising nested rules, and used these rules to compress the sequence in memory and predict the next items. Activity in dorsal inferior prefrontal cortex correlated with the amount of compression, while right dorsolateral prefrontal cortex encoded the presence of embedded structures. Sequence learning was accompanied by a progressive differentiation of multi-voxel activity patterns in these regions. We propose that humans are endowed with a simple "language of geometry" which recruits a dorsal prefrontal circuit for geometrical rules, distinct from but close to areas involved in natural language processing.
A mental set generally refers to the human brain's tendency to persist with a familiar solution and stubbornly ignore alternatives. However, if a familiar solution is unable to solve a problem similar to a previous problem, does it continue to hinder alternative solutions, and if so, how and why? To answer these questions, a Chinese character decomposition task was adopted in this study. Participants were asked to perform a practice problem that could be solved by a familiar loose chunk decomposition (LCD) solution followed by a test problem that was similar to the practice problem but could only be solved by an unfamiliar tight chunk decomposition (TCD) solution or were asked to repeatedly perform 3-5 practice problems followed by a test problem; the former is the base-set condition, and the latter is the enhanced-set condition. The results showed that the test problem recruited more activation of the inferior frontal gyrus (IFG), middle occipital cortex (MOG), superior parietal lobule (SPL) and dorsal anterior cingulate cortex (dACC) than the practice problem in the latter operation and verification stage, but almost equal activation of the dACC occurred in the early exploration stage. This likely implied that people did not think that the familiar but currently invalid LCD solution could not be used to solve the test problem; thus, it continuously competed for attention with the unfamiliar TCD solution, which required more executive control to suppress. Moreover, compared with the base-set condition, the test problem in the enhanced-set condition recruited greater activations of the IFG, SPL and dACC in the latter verification stage but less activations of regions in the left IFG and MOG in the early exploration stage. These results revealed that people less actively explored and had to work harder to operate the unfamiliar TCD solution, particularly to resolve competition from the familiar but currently invalid LCD solution. In conclusion, people lost the ability to identify errors in the familiar but currently invalid solution, which in turn decreased the exploration efforts and increased the processing demands associated with alternative solutions in the form of attentional bias and competition. This finding broadly explains the dilemma of creative problem solving.
Metabolic brain network, which is based on functional correlation patterns of F-18-fluorodeoxyglucose (FDG) positron emission tomography (PET) images, has been widely applied in both basic and clinical neuroscience. Exploring the properties of the metabolic brain network can provide valuable insight to the physiologic and pathologic processes of the brain. Based on the network theory, modular architecture has the ability to limit the spread of local perturbation impact and therefore modular networks are more robust against external damage. However, whether the metabolic brain network has modular architecture remains unknown. Methods: 77 rats performed F-18-FDG PET brain imaging. The metabolic brain network was then constructed by measuring interregional metabolic correlation in inter-subject manner. Afterwards, modular architecture of the network was detected by a greedy algorithm. Further, we perturbed the metabolic brain network by inducing focal photothrombotic ischemia in the bilateral motor cortex and then measured the glucose metabolic change of each brain region using FDG-PET. Results: A significant modular architecture was found in the metabolic brain network. The network could be divided into four modules which corresponding approximately to executive, learning/memory, visual/auditory and sensorimotor processing functional domains. After inducing the focal ischemia on the bilateral motor cortex, most of the significantly changed brain regions (13 of 17) belong to the sensorimotor module. Conclusion: Our results revealed an inherent modular architecture in the metabolic brain network and gave an experimental evidence that the modularity of the metabolism brain network could limit the spread of local perturbation impact.
Although considerable research has been published on pure tone processing, its spatiotemporal pattern is not well understood. Specifically, the link between neural activity in the auditory pathway measured by functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) markers of pure tone processing in the P1, N1, P2, and N4 components is not well established. In this study, we used single-trial EEG-fMRI as a multi-modal fusion approach to integrate concurrently acquired EEG and fMRI data, in order to understand the spatial and temporal aspects of the pure tone processing pathway. Data were recorded from 33 subjects who were presented with stochastically alternating pure tone sequences with two different frequencies: 200 and 6400 Hz. Brain network correlated with trial-to-trial variability of the task-discriminating EEG amplitude was identified. We found that neural responses responding to pure tone perception are spatially along the auditory pathway and temporally divided into three stages: (1) the early stage (P1), wherein activation occurs in the midbrain, which constitutes a part of the low level auditory pathway; (2) the middle stage (N1, P2), wherein correlates were found in areas associated with the posterodorsal auditory pathway, including the primary auditory cortex and the motor cortex; (3) the late stage (N4), wherein correlation was found in the motor cortex. This indicates that trial-by-trial variation in neural activity in the P1, N1, P2, and N4 components reflects the sequential engagement of low- and high-level parts of the auditory pathway for pure tone processing. Our results demonstrate that during simple pure tone listening tasks, regions associated with the auditory pathway transiently correlate with trial-to-trial variability of the EEG amplitude, and they do so on a millisecond timescale with a distinct temporal ordering.
Functional magnetic imaging (fMRI) has been widely used to examine the functional neural networks in both the evoked and resting states. However, most fMRI studies in rodents are performed under anesthesia, which greatly limits the scope of their application, and behavioral relevance. Efforts have been made to image rodents in the awake condition, either in the resting state or in response to sensory or optogenetic stimulation. However, fMRI in awake behaving rodents has not yet been achieved. In the current study, a novel fMRI paradigm for awake and behaving mice was developed, allowing functional imaging of the mouse brain in an olfaction-based go/no-go task. High resolution functional imaging with limited motion and image distortion were achieved at 9.4T with a cryogenic coil in awake and behaving mice. Distributed whole-brain spatiotemporal patterns were revealed, with drastically different activity profiles for go versus no-go trials. Therefore, we have demonstrated the feasibility of functional imaging of an olfactory behavior in awake mice. This fMRI paradigm in awake behaving mice could lead to novel insights into neural mechanisms underlying behaviors at a whole-brain level.
We describe an approach to multivariate analysis, termed structured kernel principal component regression (sKPCR), to identify associations in voxel-level connectomes using resting-state functional magnetic resonance imaging (rsfMRI) data. This powerful and computationally efficient multivariate method can identify voxel-phenotype associations based on the whole-brain connectivity pattern of voxels, and it can detect linear and nonlinear signals in both volume-based and surface-based rsfMRI data. For each voxel, sKPCR first extracts low-dimensional signals from the spatially smoothed connectivities by structured kernel principal component analysis, and then tests the voxel-phenotype associations by an adaptive regression model. The method's power is derived from appropriately modelling the spatial structure of the data when performing dimension reduction, and then adaptively choosing an optimal dimension for association testing using the adaptive regression strategy. Simulations based on real connectome data have shown that sKPCR can accurately control the false-positive rate and that it is more powerful than many state-of-the-art approaches, such as the connectivity-wise generalized linear model (GLM) approach, multivariate distance matrix regression (MDMR), adaptive sum of powered score (aSPU) test, and least-square kernel machine (LSKM). Moreover, since sKPCR can reduce the computational cost of non-parametric permutation tests, its computation speed is much faster. To demonstrate the utility of sKPCR for real data analysis, we have also compared sKPCR with the above methods based on the identification of voxel-wise differences between schizophrenic patients and healthy controls in four independent rsfMRI datasets. The results showed that sKPCR had better between-sites reproducibility and a larger proportion of overlap with existing schizophrenia meta-analysis findings. Code for our approach can be downloaded from https://github.com/weikanggong/sKPCR.
In early life auditory discrimination ability can be enhanced by passive sound exposure. In contrast, in adulthood passive exposure seems to be insufficient to promote discrimination ability, but this has been tested only with a single short exposure session in humans. We tested whether passive exposure to unfamiliar auditory stimuli can result in enhanced cortical discrimination ability and change detection in adult humans, and whether the possible learning effect generalizes to different stimuli. To address these issues, we exposed adult Finnish participants to Chinese lexical tones passively for 2 h per day on 4 consecutive days. Behavioral responses and the brain's event-related potentials (ERPs) were measured before and after the exposure for the same stimuli applied in the exposure phase and to sinusoidal sounds roughly mimicking the frequency contour in speech sounds. Passive exposure modulated the ERPs to speech sound changes in both ignore (mismatch negativity latency, P3a amplitude and P3a latency) and attend (P3b amplitude) test conditions, but not the behavioral responses. Furthermore, effect of passive exposure transferred to the processing of the sinusoidal sounds as indexed by the latency of the mismatch negativity. No corresponding effects in the ERPs were found in a control group that participated to the test measurements, but received no exposure to the sounds. The results show that passive exposure to foreign speech sounds in adulthood can enhance cortical discrimination ability and attention orientation toward changes in speech sounds and that the learning effect can transfer to non-speech sounds.