fMRI Activation Patterns Research Articles

P 146 Differences in processing of action and semantic tool knowledge – evidence from fMRI and dynamic causal modelling

Objective Whether processing of function knowledge about tools (i.e., knowing what a tool is used for) draws upon action or semantic knowledge remains controversial. Methods We investigated this issue using fMRI. 17 subjects performed three experimental tasks tapping into different aspects of tool knowledge. Data were analyzed using SPM and dynamic causal modeling (DCM). Results The behavioral data and fMRI activation pattern suggested that function knowledge about tools was processed similarly as non-motor-related semantic tool knowledge (here: the monetary value of a tool) and could therefore be classified as “motor-related semantic knowledge”, whereas action knowledge (i.e., how a tool is used) was processed differently. Processing of action knowledge activated areas of the ventro-dorsal stream (i.e., the lateral occipito-temporal cortex (LOTC) bilaterally, the left intraparietal sulcus (IPS), the left supramarginal gyrus (SMG), and the left inferior frontal gyrus (IFG)). In contrast, ventral stream areas were activated by the processing of semantic tool knowledge: the fusiform gyrus (FFG) bilaterally, the angular gyrus (ANG) bilaterally, and the medial prefrontal cortex (mPFC). Analyzing the effective connectivity between these areas with DCM revealed interactions between the processing streams (between LOTC and FFG as well as between SMG and ANG) and reciprocal modulatory effects. Conclusions This fMRI-study substantiates that action and semantic knowledge about tools are processed along two separate routes and that function knowledge (i.e., knowing what a tool is used for) draws upon the semantic system. In addition, connectivity analysis with DCM revealed relevant interactions between the processing streams at lower and higher levels.

Altered Coupling between Motion-Related Activation and Resting-State Brain Activity in the Ipsilesional Sensorimotor Cortex after Cerebral Stroke.

Functional connectivity maps using resting-state functional magnetic resonance imaging (rs-fMRI) can closely resemble task fMRI activation patterns, suggesting that resting-state brain activity may predict task-evoked activation or behavioral performance. However, this conclusion was mostly drawn upon a healthy population. It remains unclear whether the predictive ability of resting-state brain activity for task-evoked activation would change under different pathological conditions. This study investigated dynamic changes of coupling between patterns of resting-state functional connectivity (RSFC) and motion-related activation in different stages of cerebral stroke. Twenty stroke patients with hand motor function impairment were involved. rs-fMRI and hand motion-related fMRI data were acquired in the acute, subacute, and early chronic stages of cerebral stroke on a 3-T magnetic resonance (MR) scanner. Sixteen healthy participants were enrolled as controls. For each subject, an activation map of the affected hand was first created using general linear model analysis on task fMRI data, and then an RSFC map was determined by seeding at the peak region of hand motion activation during the intact hand task. We then measured the extent of coupling between the RSFC maps and motion-related activation maps. Dynamic changes of the coupling between the two fMRI maps were estimated using one-way repeated measures analysis of variance across the three stages. Moreover, imaging parameters were correlated with motor performances. Data analysis showed that there were different coupling patterns between motion-related activation and RSFC maps associating with the affected motor regions during the acute, subacute, and early chronic stages of stroke. Coupling strengths increased as the recovery from stroke progressed. Coupling strengths were correlated with hand motion performance in the acute stage, while coupling recovery was negatively correlated with the recovery outcome of hand motion performance in the early chronic stages. Couplings between RSFC and motion-related activation were dynamically changed with stroke progression, which suggested changes in the prediction of resting-state brain activity for task-evoked brain activity in different pathological states. The changes in coupling strength between these two types of brain activity implicate a reparative mechanism of brain injury and may represent a biomarker for predicting motor recovery in cerebral stroke.

Reading-induced shifts of perceptual speech representations in auditory cortex

Learning to read requires the formation of efficient neural associations between written and spoken language. Whether these associations influence the auditory cortical representation of speech remains unknown. Here we address this question by combining multivariate functional MRI analysis and a newly-developed ‘text-based recalibration’ paradigm. In this paradigm, the pairing of visual text and ambiguous speech sounds shifts (i.e. recalibrates) the perceptual interpretation of the ambiguous sounds in subsequent auditory-only trials. We show that it is possible to retrieve the text-induced perceptual interpretation from fMRI activity patterns in the posterior superior temporal cortex. Furthermore, this auditory cortical region showed significant functional connectivity with the inferior parietal lobe (IPL) during the pairing of text with ambiguous speech. Our findings indicate that reading-related audiovisual mappings can adjust the auditory cortical representation of speech in typically reading adults. Additionally, they suggest the involvement of the IPL in audiovisual and/or higher-order perceptual processes leading to this adjustment. When applied in typical and dyslexic readers of different ages, our text-based recalibration paradigm may reveal relevant aspects of perceptual learning and plasticity during successful and failing reading development.

Predicting the brain activation pattern associated with the propositional content of a sentence: Modeling neural representations of events and states.

Even though much has recently been learned about the neural representation of individual concepts and categories, neuroimaging research is only beginning to reveal how more complex thoughts, such as event and state descriptions, are neurally represented. We present a predictive computational theory of the neural representations of individual events and states as they are described in 240 sentences. Regression models were trained to determine the mapping between 42 neurally plausible semantic features (NPSFs) and thematic roles of the concepts of a proposition and the fMRI activation patterns of various cortical regions that process different types of information. Given a semantic characterization of the content of a sentence that is new to the model, the model can reliably predict the resulting neural signature, or, given an observed neural signature of a new sentence, the model can predict its semantic content. The models were also reliably generalizable across participants. This computational model provides an account of the brain representation of a complex yet fundamental unit of thought, namely, the conceptual content of a proposition. In addition to characterizing a sentence representation at the level of the semantic and thematic features of its component concepts, factor analysis was used to develop a higher level characterization of a sentence, specifying the general type of event representation that the sentence evokes (e.g., a social interaction versus a change of physical state) and the voxel locations most strongly associated with each of the factors. Hum Brain Mapp 38:4865-4881, 2017. © 2017 Wiley Periodicals, Inc.

Effect of verbal task complexity in a working memory paradigm in patients with type 1 diabetes. A fMRI study.

Type 1 diabetes (T1D) is commonly diagnosed in childhood and adolescence, and the developing brain has to cope with its deleterious effects. Although brain adaptation to the disease may not result in evident cognitive dysfunction, the effects of T1D on neurodevelopment could alter the pattern of BOLD fMRI activation. The aim of this study was to explore the neural BOLD activation pattern in patients with T1D versus that of healthy matched controls while performing two visuospatial working memory tasks, which included a pair of assignments administered through a block design. In the first task (condition A), the subjects were shown a trial sequence of 3 or 4 white squares positioned pseudorandomly around a fixation point on a black background. After a fixed delay, a second corresponding sequence of 3 or 4 red squares was shown that either resembled (direct, 50%) or differed from (50%) the previous stimulation order. The subjects were required to press one button if the two spatial sequences were identical or a second button if they were not. In condition B, the participants had to determine whether the second sequence of red squares appeared in inverse order (inverse, 50%) or not (50%) and respond by pressing a button. If the latter sequence followed an order distinct from the inverse sequence, the subjects were instructed to press a different button. Sixteen patients with normal IQ and without diabetes complications and 16 healthy control subjects participated in the study. In the behavioral analysis, there were no significant differences between the groups in the pure visuo-spatial task, but the patients with diabetes exhibited poorer performance in the task with verbal stimuli (p < .001). However, fMRI analyses revealed that the patients with T1D showed significantly increased activation in the prefrontal inferior cortex, subcortical regions and the cerebellum (in general p < .001). These different activation patterns could be due to adaptive compensation mechanisms that are devoted to improving efficiency while solving more complex cognitive tasks.

Potential role of a cognitive rehabilitation program following left temporal lobe epilepsy surgery.

To assess the effects of a memory rehabilitation program in patients with left temporal lobe epilepsy following surgery. Twenty-four patients agreed to participate and 18 completed the study; nine received memory rehabilitation while nine had no input and were designated as controls. Verbal learning efficiency, naming abilities, memory subjective ratings, ecological activity measures and a language fMRI paradigm were used as outcome measures. Improved verbal learning and naming test performance, increase in memory strategy use and improved self-perception were observed following the rehabilitation. Changes in fMRI activation patterns were seen in the rehabilitation group over the long term. The findings support the potential role of a cognitive rehabilitation program following left temporal lobe surgery.

A shared representation of order between encoding and recognition in visual short-term memory

Many complex tasks require people to bind individual events into a sequence that can be held in short term memory (STM). For this purpose information about the order of the individual events in the sequence needs to be maintained in an active and accessible form in STM over a period of few seconds. Here we investigated how the temporal order information is shared between the presentation and response phases of an STM task. We trained a classification algorithm on the fMRI activity patterns from the presentation phase of the STM task to predict the order of the items during the subsequent recognition phase. While voxels in a number of brain regions represented positional information during either presentation and recognition phases, only voxels in the lateral prefrontal cortex (PFC) and the anterior temporal lobe (ATL) represented position consistently across task phases. A shared positional code in the ATL might reflect verbal recoding of visual sequences to facilitate the maintenance of order information over several seconds.

Exercise-Induced Fitness Changes Correlate with Changes in Neural Specificity in Older Adults.

Neural specificity refers to the degree to which neural representations of different stimuli can be distinguished. Evidence suggests that neural specificity, operationally defined as stimulus-related differences in functional magnetic resonance imaging (fMRI) activation patterns, declines with advancing adult age, and that individual differences in neural specificity are associated with individual differences in fluid intelligence. A growing body of literature also suggests that regular physical activity may help preserve cognitive abilities in old age. Based on this literature, we hypothesized that exercise-induced improvements in fitness would be associated with greater neural specificity among older adults. A total of 52 adults aged 59–74 years were randomly assigned to one of two aerobic-fitness training regimens, which differed in intensity. Participants in both groups trained three times a week on stationary bicycles. In the low-intensity (LI) group, the resistance was kept constant at a low level (10 Watts). In the high-intensity (HI) group, the resistance depended on participants’ heart rate and therefore typically increased with increasing fitness. Before and after the 6-month training phase, participants took part in a functional MRI experiment in which they viewed pictures of faces and buildings. We used multivariate pattern analysis (MVPA) to estimate the distinctiveness of neural activation patterns in ventral visual cortex (VVC) evoked by face or building stimuli. Fitness was also assessed before and after training. In line with our hypothesis, training-induced changes in fitness were positively associated with changes in neural specificity. We conclude that physical activity may protect against age-related declines in neural specificity.

Neuroscientific insights into the development of analogical reasoning.

Analogical reasoning, or the ability to find correspondences between entities based on shared relationships, supports knowledge acquisition. As such, the development of this ability during childhood is thought to promote learning. Here, we sought to better understand the mechanisms by which analogical reasoning about semantic relations improves over childhood and adolescence (e.g. chalk is to chalkboard as pen is to…?). We hypothesized that age‐related differences would manifest as differences in the brain regions associated with one or more of the following cognitive functions: (1) controlled semantic retrieval, or the ability to retrieve task‐relevant semantic associations; (2) response control, or the ability to override the tendency to respond to a salient distractor; and/or (3) relational integration, or the ability to consider jointly two mental relations. In order to test these hypotheses, we analyzed patterns of fMRI activation during performance of a pictorial propositional analogy task across 95 typically developing children between the ages of 6 and 18 years old. Despite large age‐related differences in task performance, particularly over ages 6–10 but through to around age 14, participants across the whole age range recruited a common network of frontal, parietal and temporal regions. However, activation in a brain region that has been implicated in controlled semantic retrieval – left anterior prefrontal cortex (BA 47/45) – was positively correlated with age, and also with performance after controlling for age. This finding indicates that improved performance over middle childhood and early adolescence on this analogical reasoning task is driven largely by improvements in the ability to selectively retrieve task‐relevant semantic relationships.

Holographic brain: Distributed versus local activation patterns in fMRI

Discovering brain activity patterns associated with specific stimuli, behaviors, or mental disorders is a primary objective of many neuroimaging studies. In particular, functional magnetic resonance imaging analyses are often concerned with finding brain areas “most relevant” to a mental state of interest. In this paper, we consider the predictive accuracy of a subset of voxels (smallest units of a three-dimensional image) identified by multivariate sparse regression as a better relevance measure than the commonly used mass-univariate correlations between the task and individual voxels. Sparse regression detects synergistic multivoxel interactions missed by univariate techniques. Moreover, when invoked iteratively, it allows us to discover novel patterns of information distribution through the brain. Indeed, unlike univariate techniques that typically imply a sharp transition from task-relevant to task-irrelevant brain areas, our approach reveals that for some tasks, task-relevant information is distributed rather “holographically” through the entire brain, yielding multiple relevant subsets of voxels, with gradually declining predictive accuracy, and without any clear separation between task-relevant and task-irrelevant ones. Interestingly, as our preliminary results show, such phenomena occur in certain “complex” experiences such as pain perception, but not in relatively “simple” tasks such as finger tapping.

A family of locally constrained CCA models for detecting activation patterns in fMRI

Canonical correlation analysis (CCA) has been used in Functional Magnetic Resonance Imaging (fMRI) for improved detection of activation by incorporating time series from multiple voxels in a local neighborhood. To improve the specificity of local CCA methods, spatial constraints were previously proposed. In this study, constraints are generalized by introducing a family model of spatial constraints for CCA to further increase both sensitivity and specificity in fMRI activation detection.The proposed locally-constrained CCA (cCCA) model is formulated in terms of a multivariate constrained optimization problem and solved efficiently with numerical optimization techniques. To evaluate the performance of this cCCA model, simulated data are generated with a Signal-To-Noise Ratio of 0.25, which is realistic to the noise level contained in episodic memory fMRI data. Receiver operating characteristic (ROC) methods are used to compare the performance of different models. The cCCA model with optimum parameters (called optimum-cCCA) obtains the largest area under the ROC curve. Furthermore, a novel validation method is proposed to validate the selected optimum-cCCA parameters based on ROC from simulated data and real fMRI data. Results for optimum-cCCA are then compared with conventional fMRI analysis methods using data from an episodic memory task. Wavelet-resampled resting-state data are used to obtain the null distribution of activation.For simulated data, accuracy in detecting activation increases for the optimum-cCCA model by about 43% as compared to the single voxel analysis with comparable Gaussian smoothing. Results from the real fMRI data set indicate a significant increase in activation detection, particularly in hippocampus, para-hippocampal area and nearby medial temporal lobe regions with the proposed method.

Automated selection of brain regions for real-time fMRI brain–computer interfaces

Objective. Brain–computer interfaces (BCIs) implemented with real-time functional magnetic resonance imaging (rt-fMRI) use fMRI time-courses from predefined regions of interest (ROIs). To reach best performances, localizer experiments and on-site expert supervision are required for ROI definition. To automate this step, we developed two unsupervised computational techniques based on the general linear model (GLM) and independent component analysis (ICA) of rt-fMRI data, and compared their performances on a communication BCI. Approach. 3 T fMRI data of six volunteers were re-analyzed in simulated real-time. During a localizer run, participants performed three mental tasks following visual cues. During two communication runs, a letter-spelling display guided the subjects to freely encode letters by performing one of the mental tasks with a specific timing. GLM- and ICA-based procedures were used to decode each letter, respectively using compact ROIs and whole-brain distributed spatio-temporal patterns of fMRI activity, automatically defined from subject-specific or group-level maps. Main results. Letter-decoding performances were comparable to supervised methods. In combination with a similarity-based criterion, GLM- and ICA-based approaches successfully decoded more than 80% (average) of the letters. Subject-specific maps yielded optimal performances. Significance. Automated solutions for ROI selection may help accelerating the translation of rt-fMRI BCIs from research to clinical applications.

Visual Prediction Error Spreads Across Object Features in Human Visual Cortex.

We address a key question in predictive visual cognition: how does the brain combine multiple concurrent expectations for different features of a single object such as its color and motion trajectory? By combining a behavioral protocol that independently varies expectation of (and attention to) multiple object features with computational modeling and fMRI, we demonstrate that behavior and fMRI activity patterns in visual cortex are best accounted for by a model in which prediction error in one object feature spreads to other object features. These results demonstrate how predictive vision forms object-level expectations out of multiple independent features.

27.3 NEURAL RESPONSE TO FAMILY-FOCUSED THERAPY VERSUS PSYCHOEDUCATION IN YOUTH AT-RISK FOR BIPOLAR DISORDER

Family-focused therapy (FFT) has been shown to be effective in stabilization of mood symptoms in youth at risk for bipolar disorder. Less is known about the underlying brain changes that occur in response to treatment, particularly as it relates to family problem solving. In this study, we compared pre- to posttreatment fMRI activation patterns between FFT and a comparison therapy from a randomized controlled trial. We also examined the relative role of the components of FFT [psychoeducation vs. family skills training (FST)] on changes in the brain after treatment.

Multivariate patterns of fMRI activity in human V2 predict the misbinding of color and motion

Multivariate patterns of fMRI activity in human V2 predict the misbinding of color and motion

FMRI studies prior and post acupuncture treatment concerning smoking cessation

Introduction Acupuncture has been widely reported to be a popular and safe intervention for smoking cessation. This study demonstrates the potential effects of acupuncture treatment for smoking cessation using the MRI technique of functional Magnetic Resonance Imaging (fMRI). Purpose Substance dependence or addiction is nowadays uderstood in a multifactorial etiological model, which includes psychological, neurobiological, genetic, social and environmental factors. This study illustrates patterns of fMRI activation in various specific brain anatomies, such as frontal, temporal parietal, occipital and cerebellum that correlate with smoking. Materials and methods Participants of both genders were volunteers willing to quit smoking. The inclusion criteria were age (28–42 y) and rate of smoking >15 cigarrettes per day and had no previous experience of acupuncture treatment. Group A received real acupuncture, Group B sham acupuncture and Group C was the control group with no smokers. Acupuncture points used: Du-20, LI-20, LI-4,TB-5, ST-36, LIV-3 and treatments lasted for 20 min. fMRI was applied prior and post acupuncture treatment during the first 24 hours of smoking cessation. During the fMRI experiment a series of smoking related images were presented to each participant via a set of goggles in a block time-design manner. Results Increased brain response areas were observed, such as: dorsolateral prefrontal, primary motor gyrus, parahippocampal gyrus, hippocampus, insula, posterior cingulate cortex, somatosensory cortex, visual areas and external basal ganglia. Differences between Groups A and B, as well as interesting correlations between acupuncture points, fMRI findings and neurobiological areas are presented. Conclusion Our results provide evidences over a pattern of slightly different brain activation following real and sham acupuncture after a short term smoking abstinence. The observed differences are in areas involved in drug addiction and smoking maintenance/abstinence literature.

Effectiveness of Reference Signal-Based Methods for Removal of EEG Artifacts Due to Subtle Movements During fMRI Scanning.

Subtle motion of an epileptic patient examined with co-registered EEG and functional MRI (EEG-fMRI) may often lead to spurious fMRI activation patterns when true epileptic spikes are contaminated with motion artefacts. In recent years, methods relying on reference signals for correcting these subtle movements in the EEG have emerged. In this study, the performance of two reference-based devices are compared to the template-based method with regard to their ability to remove movement-related artifacts in EEG measured during scanning. Measurements were performed with a novel double layer cap consisting of 29 EEG and 29 reference electrodes, and with a current loop cap consisting of 60 electrodes and three current loop wires attached to the cap. EEG was acquired inside the scanner during resting state, as well as when the subject was performing a cued movement task. For the double layer cap recordings, newly developed artifact removal algorithms are introduced and both reference signal-based methods are compared to a template-based correction method. The BCG artifacts occurring at resting state could be removed successfully by both the reference signal-based methods as well as by the template-based method. However, the reference signal-based methods were also capable of removing EEG artifacts induced by subtle movements, whereas the template-based method failed to remove these artifacts. Reference signal-based methods enable to correct for artifacts due to subtle movements, which are not removed by commonly used template-based removal algorithms. Sensitivity of EEG-fMRI analysis in patients with focal epilepsy is improved by avoiding erroneous detections of subtle movements as epileptic spikes in the EEG.

I can see where you would be: Patterns of fMRI activity reveal imagined landmarks

Visual mental imagery arises when perceptual information is accessed from memory, originating the experience of “seeing with the mind's eye”. Different content-dependent brain areas in the human ventral visual stream are activated during visual mental imagery, similarly to what happens during visual perception. The neural patterns within these regions, but not in the early visual cortex, are similar during imagery and perception, suggesting that, in the absence of perceptual stimulation, content-dependent brain areas are able to re-instantiate specific neural patterns allowing for mental imagery. However, it remains unknown whether these areas contain adequate neural representations that create mental images or need to interact with other regions in the brain, such as the hippocampus (HC), to access the necessary information from memory. To test this hypothesis, we used functional magnetic resonance imaging and both multivoxel pattern classification and psychophysiological interaction analyses. Participants were scanned while viewing or imagining scenes of familiar environments. We found that the identity of familiar places can be decoded from the neural patterns in the parahippocampal place area (PPA), retrosplenial complex/parieto-occipital sulcus (RSC/POS) and HC, during both imagery and perception, and that item-specific information from perceived places was re-instantiated during mental imagery of the same places and vice versa. Furthermore, the right PPA significantly interacted with the right HC and RSC/POS according to the performed task. Specifically, the functional coupling between PPA and HC was higher during mental imagery, whereas the functional coupling between PPA and RSC/POS was higher during perception. Our investigation provides an important contribution to the understanding of how the brain uses previously acquired knowledge to build a mental representation of the world.

Predicting Neural Activity Patterns Associated with Sentences Using a Neurobiologically Motivated Model of Semantic Representation

We introduce an approach that predicts neural representations of word meanings contained in sentences then superposes these to predict neural representations of new sentences. A neurobiological semantic model based on sensory, motor, social, emotional, and cognitive attributes was used as a foundation to define semantic content. Previous studies have predominantly predicted neural patterns for isolated words, using models that lack neurobiological interpretation. Fourteen participants read 240 sentences describing everyday situations while undergoing fMRI. To connect sentence-level fMRI activation patterns to the word-level semantic model, we devised methods to decompose the fMRI data into individual words. Activation patterns associated with each attribute in the model were then estimated using multiple-regression. This enabled synthesis of activation patterns for trained and new words, which were subsequently averaged to predict new sentences. Region-of-interest analyses revealed that prediction accuracy was highest using voxels in the left temporal and inferior parietal cortex, although a broad range of regions returned statistically significant results, showing that semantic information is widely distributed across the brain. The results show how a neurobiologically motivated semantic model can decompose sentence-level fMRI data into activation features for component words, which can be recombined to predict activation patterns for new sentences.

EP 1. Brain activity after acute left hemispheric stroke in imitation and tool associated actions

Background Apraxia, a disorder of higher motor control, frequently leads to impaired tool use or imitation capacities due to left hemispheric lesions. However, despite similar lesion size or location, patients present with different apraxic deficits thus different early reorganization mechanism may be assumed. To understand the impaired praxis network after stroke on a functional level, we correlated behavioral performance in imitation and tool use tasks with activation patterns in fMRI of 47 acute left-hemispheric stroke patients. Methods A cohort of 47 acute stroke patients (mean age 63.5 years ± SD 13.6; 13 female) with first ever embolic stroke in the left arteria cerebri media territory were tested for deficits in imitation of meaningless hand and finger postures and tool associated actions (pantomime of tool use, imitation of tool use, actual tool use). Testing was performed mean 4.8 days ± SD 2.9 post stroke. FMRI was acquired during presentation of video sequences of tool-associated actions viewed from a first-person perspective. Voxel wise linear regression analyses for task scores only and with lesion volume as covariate as well as group differences for categorized behavioral performance (deficit vs. no deficit) were calculated to analyze activity associated with behavioral performance in tool use and meaningless imitation. Analyses were performed using GLMflex. Results are presented on a p Results Intact imitation of meaningless postures was associated with activation in the left middle occipital gyrus as well as in the left superior temporal lobe in linear regression analyses and categorical group differences (deficit vs. no deficit). Tool associated tasks correlated with activation in the left supramarginal gyrus and superior temporal lobe without occipital activation. For group differences in pantomime of tool use (deficit vs. no deficit) an additional activation for patients with low behavioral performance could be found in the posterior middle temporal gyrus. Conclusion For intact imitation of meaningless postures and tool associated actions, specific left hemispheric activation patterns in the acute phase after stroke could be determined. These areas are commonly associated to the left hemispheric praxis network. While intact imitation of meaningless gestures seems to rely on intact left middle occipital gyrus for visual analysis of the presented movement tool associated actions rather seem to rely on integrity of the supramarginal gyrus, known to store movement engrams. Patients with deficits in pantomime of tool use additionally show activation of posterior middle temporal gyrus for semantic movement information. These mechanisms possibly can be interpreted as early compensatory effort.