fMRI Activation Patterns Research Articles

On the (Non-)equivalency of monopolar and bipolar settings for deep brain stimulation fMRI studies of Parkinson's disease patients.

The majority of Parkinson's disease patients with deep brain stimulation (DBS) use a monopolar configuration, which presents challenges for EEG and MRI studies. The literature reports algorithms to convert monopolar to bipolar settings. To assess brain responses of Parkinson's disease patients implanted with DBS during fMRI studies using their clinical and presumed equivalent settings using a published conversion recipe. Prospective. Thirteen DBS patients. 1.5T and 3T, fMRI using gradient echo-planar imaging. Patients underwent 30/30sec ON/OFF DBS fMRI scans using monopolar and bipolar settings. To convert to a bipolar setting, the negative contact used for the monopolar configuration remained constant and the adjacent dorsal contact was rendered positive, while increasing the voltage by 30%. fMRI activation/deactivation maps and motor Unified Parkinson's Disease Rating Scale (UPDRS-III) scores were compared for patients in both configurations. T-tests were used to compare UPDRS scores and volumes of tissue activated (VTA) diameters in monopolar and bipolar configurations. The patterns of fMRI activation in the monopolar and bipolar configurations were generally different. The thalamus, pallidum, and visual cortices exhibited higher activation using the patient's clinical settings than the presumed equivalent settings. VTA diameters were lower (7 mm vs. 6.3 mm, P = 0.047) and UPDRS scores were generally higher in the bipolar (33.2 ± 16) than in the monopolar configuration (28.3 ± 17.4), without reaching statistical significance (P > 0.05). Monopolar and bipolar configurations result in different patterns of brain activation while using a previously published monopolar-bipolar conversion algorithm. Clinical benefits may be achieved with varying patterns of brain responses. Blind conversion from one to the other should be avoided for purposes of understanding the mechanisms of DBS. 1 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2018.

Task difficulty modulates age-related differences in the behavioral and neural bases of language production

Older adults typically show decline in a variety of cognitive functions including inhibitory control and language production, with corresponding age-related increases in fMRI activation. However, it remains unclear whether such increases are compensatory or whether they reflect neural decline. One factor that may influence these brain-behavior relationships is difficulty. The current study investigated the effect of difficulty on age-related differences in the behavioral and neural bases of language production and inhibitory control using a phonological Go/No-Go picture naming task. Task demands were manipulated by varying the proportion of naming trials (Go trials) and inhibition trials (No-Go trials) across runs. All participants showed task-difficulty related declines in behavioral performance and increases in fMRI activation. Behaviorally, older adults were more sensitive to task difficulty, and elicited more fMRI activation than younger adults. Older adults were less neurally responsive to additional task demands (i.e., picture naming alone vs. Go/No-Go picture naming), but interestingly showed similar within-task increases as younger adults (e.g., Go Bias vs. No-Go Bias). Moreover, the relationships between fMRI activation and behavioral performance in older adults were multifaceted and the strength of these relations changed as a function of task difficulty. Specifically, activation in pre- and post- central gyri, right supramarginal and angular gyri was negatively correlated with naming reaction times, suggesting that activation in these regions may help mitigate age-related declines in language production. These findings are partially consistent with the CRUNCH model, highlighting the important influence of task difficulty on older adults’ behavioral performance and their patterns of fMRI activation during language production.

Verbal Paired Associates and the Hippocampus: The Role of Scenes.

It is widely agreed that patients with bilateral hippocampal damage are impaired at binding pairs of words together. Consequently, the verbal paired associates (VPA) task has become emblematic of hippocampal function. This VPA deficit is not well understood and is particularly difficult for hippocampal theories with a visuospatial bias to explain (e.g., cognitive map and scene construction theories). Resolving the tension among hippocampal theories concerning the VPA could be important for leveraging a fuller understanding of hippocampal function. Notably, VPA tasks typically use high imagery concrete words and so conflate imagery and binding. To determine why VPA engages the hippocampus, we devised an fMRI encoding task involving closely matched pairs of scene words, pairs of object words, and pairs of very low imagery abstract words. We found that the anterior hippocampus was engaged during processing of both scene and object word pairs in comparison to abstract word pairs, despite binding occurring in all conditions. This was also the case when just subsequently remembered stimuli were considered. Moreover, for object word pairs, fMRI activity patterns in anterior hippocampus were more similar to those for scene imagery than object imagery. This was especially evident in participants who were high imagery users and not in mid and low imagery users. Overall, our results show that hippocampal engagement during VPA, even when object word pairs are involved, seems to be evoked by scene imagery rather than binding. This may help to resolve the issue that visuospatial hippocampal theories have in accounting for verbal memory.

Stability of representational geometry across a wide range of fMRI activity levels

Fine-grained activity patterns, as measured with functional magnetic resonance imaging (fMRI), are thought to reflect underlying neural representations. Multivariate analysis techniques, such as representational similarity analysis (RSA), can be used to test models of brain representation by quantifying the representational geometry (the collection of pair-wise dissimilarities between activity patterns). One important caveat, however, is that non-linearities in the coupling between neural activity and the fMRI signal may lead to significant distortions in the representational geometry estimated from fMRI activity patterns. Here we tested the stability of representational dissimilarity measures in primary sensory-motor (S1 and M1) and early visual regions (V1/V2) across a large range of activation levels. Participants were visually cued with different letters to perform single finger presses with one of the 5 fingers at a rate of 0.3–2.6 Hz. For each stimulation frequency, we quantified the difference between the 5 activity patterns in M1, S1, and V1/V2. We found that the representational geometry remained relatively stable, even though the average activity increased over a large dynamic range. These results indicate that the representational geometry of fMRI activity patterns can be reliably assessed, largely independent of the average activity in the region. This has important methodological implications for RSA and other multivariate analysis approaches that use the representational geometry to make inferences about brain representations.

Curved features are critical for animate/inanimate categorization in macaques

In an earlier fMRI study, we showed that multivoxel activity patterns, measured with support vector machine classification, encoded animate vs. inanimate categories in the macaque inferior temporal cortex. However, the classification accuracy was reduced to chance after removing the variance in the fMRI activity patterns that was explained by the curved and rectilinear image features, as quantified using curved and rectilinear Gabor filters. These results indicate that categorization in the macaque inferior temporal cortex might not stem from acquired semantic knowledge of the characteristics that distinguish animate from inanimate object categories, but rather from the unique image-based features. The current experiment was designed to directly examine those two possibilities using behavioral tests. First, one rhesus macaque was trained to categorize images of animate and inanimate objects. Then, the same monkey was tested on a large number of trial-unique images of animate and inanimate objects across five days to assess whether this training generalized to unfamiliar objects. We found that the animal's classification accuracy for these unfamiliar objects averaged 84.55%, supporting our fMRI conclusion that animate/inanimate categorization does not stem from acquired semantic knowledge of animate vs. inanimate categories. We also tested whether image features that differ substantially between the two object categories, such as curvilinear and rectilinear information, contribute to the monkey's classification accuracy. The same animal was tested across five days on sets of synthetic animate and inanimate images created using an algorithm that significantly distorted the global shape of the original images, while maintaining the original images' intermediate features (e.g. curvilinear and rectilinear information). We found that the animal's classification accuracy was significantly above chance (63.57%), suggesting that unique image-based features, such as curvilinear features, distinguish animate from inanimate objects and give rise to the formation of animate/inanimate categorization in macaques to some extent. Meeting abstract presented at VSS 2018

GLMdenoise improves multivariate pattern analysis of fMRI data

GLMdenoise is a denoising technique for task-based fMRI. In GLMdenoise, estimates of spatially correlated noise (which may be physiological, instrumental, motion-related, or neural in origin) are derived from the data and incorporated as nuisance regressors in a general linear model (GLM) analysis. We previously showed that GLMdenoise outperforms a variety of other denoising techniques in terms of cross-validation accuracy of GLM estimates (Kay et al., 2013a). However, the practical impact of denoising for experimental studies remains unclear. Here we examine whether and to what extent GLMdenoise improves sensitivity in the context of multivariate pattern analysis of fMRI data. On a large number of participants (31 participants across 4 experiments; 3 T, gradient-echo, spatial resolution 2–3.75 mm, temporal resolution 1.3–2 s, number of conditions 32–75), we perform representational similarity analysis (Kriegeskorte et al., 2008a) as well as pattern classification (Haxby et al., 2001). We find that GLMdenoise substantially improves replicability of representational dissimilarity matrices (RDMs) across independent splits of each participant's dataset (average RDM replicability increases from r = 0.46 to r = 0.61). Additionally, we find that GLMdenoise substantially improves pairwise classification accuracy (average classification accuracy increases from 79% correct to 84% correct). We show that GLMdenoise often improves and never degrades performance for individual participants and that GLMdenoise also improves across-participant consistency. We conclude that GLMdenoise is a useful tool that can be routinely used to maximize the amount of information extracted from fMRI activity patterns.

Active Sound Localization Sharpens Spatial Tuning in Human Primary Auditory Cortex.

Spatial hearing sensitivity in humans is dynamic and task-dependent, but the mechanisms in human auditory cortex that enable dynamic sound location encoding remain unclear. Using functional magnetic resonance imaging (fMRI), we assessed how active behavior affects encoding of sound location (azimuth) in primary auditory cortical areas and planum temporale (PT). According to the hierarchical model of auditory processing and cortical functional specialization, PT is implicated in sound location ("where") processing. Yet, our results show that spatial tuning profiles in primary auditory cortical areas (left primary core and right caudo-medial belt) sharpened during a sound localization ("where") task compared with a sound identification ("what") task. In contrast, spatial tuning in PT was sharp but did not vary with task performance. We further applied a population pattern decoder to the measured fMRI activity patterns, which confirmed the task-dependent effects in the left core: sound location estimates from fMRI patterns measured during active sound localization were most accurate. In PT, decoding accuracy was not modulated by task performance. These results indicate that changes of population activity in human primary auditory areas reflect dynamic and task-dependent processing of sound location. As such, our findings suggest that the hierarchical model of auditory processing may need to be revised to include an interaction between primary and functionally specialized areas depending on behavioral requirements.SIGNIFICANCE STATEMENT According to a purely hierarchical view, cortical auditory processing consists of a series of analysis stages from sensory (acoustic) processing in primary auditory cortex to specialized processing in higher-order areas. Posterior-dorsal cortical auditory areas, planum temporale (PT) in humans, are considered to be functionally specialized for spatial processing. However, this model is based mostly on passive listening studies. Our results provide compelling evidence that active behavior (sound localization) sharpens spatial selectivity in primary auditory cortex, whereas spatial tuning in functionally specialized areas (PT) is narrow but task-invariant. These findings suggest that the hierarchical view of cortical functional specialization needs to be extended: our data indicate that active behavior involves feedback projections from higher-order regions to primary auditory cortex.

Modulation of network-to-network connectivity via spike-timing-dependent noninvasive brain stimulation.

Human cognitive abilities and behavior are linked to functional coupling of many brain regions organized in distinct networks. Gaining insights on the role those networks' dynamics play in cognition and pathology requires their selective, reliable, and reversible manipulation. Here we document the possibility to manipulate the interplay between two brain networks in a controlled manner, by means of a Transcranial Magnetic Stimulation (TMS) protocol inducing spike timing dependent plasticity (STDP). Pairs of TMS pulses at specific inter-stimulus intervals, repeatedly delivered over two negatively correlated nodes of the default mode network (DMN) and the task-positive network (TPN) defined on the basis of individual functional magnetic resonance imaging (fMRI) data, induced a modulation of network-to-network connectivity, even reversing correlation from negative to slightly positive in 30% of cases. Results also suggest a baseline-dependent effect, with a greater connectivity modulation observed in participants with weaker between-networks connectivity strength right before TMS. Finally, modulation of task-evoked fMRI activity patterns during a sustained attention task was also observed after stimulation, with a faster or slower switch between rest and task blocks according to the timing of TMS pulses. The present findings promote paired associative TMS as a promising technique for controlled manipulation of fMRI connectivity dynamics in humans, as well as the causal investigation of brain-behavior relations.

The impact of brain-derived neurotrophic factor Val66Met polymorphism on cognition and functional brain networks in patients with intractable partial epilepsy.

Medial temporal lobe epilepsy (mTLE) is the most common refractory focal epilepsy in adults. Around 30%-40% of patients have prominent memory impairment and experience significant postoperative memory and language decline after surgical treatment. BDNF Val66Met polymorphism has also been associated with cognition and variability in structural and functional hippocampal indices in healthy controls and some patient groups. We examined whether BDNF Val66Met variation was associated with cognitive impairment in mTLE. In this study, we investigated the association of Val66Met polymorphism with cognitive performance (n=276), postoperative cognitive change (n=126) and fMRI activation patterns during memory encoding and language paradigms in 2 groups of patients with mTLE (n=37 and 34). mTLE patients carrying the Met allele performed more poorly on memory tasks and showed reduced medial temporal lobe activation and reduced task-related deactivations within the default mode networks in both the fMRI memory and language tasks than Val/Val patients. Although cognitive impairment in epilepsy is the result of a complex interaction of factors, our results suggest a role of genetic factors on cognitive impairment in mTLE.

Neural correlates of free recall of \u201cfamous events\u201d in a \u201chypermnestic\u201d individual as compared to an age- and education-matched reference group

BackgroundMemory performance of an individual (within the age range: 50–55 years old) showing superior memory abilities (protagonist PR) was compared to an age- and education-matched reference group in a historical facts (“famous events”) retrieval task.ResultsContrasting task versus baseline performance both PR and the reference group showed fMRI activation patterns in parietal and occipital brain regions. The reference group additionally demonstrated activation patterns in cingulate gyrus, whereas PR showed additional widespread activation patterns comprising frontal and cerebellar brain regions. The direct comparison between PR and the reference group revealed larger fMRI contrasts for PR in right frontal, superior temporal and cerebellar brain regions.ConclusionsIt was concluded that PR generally recruits brain regions as normal memory performers do, but in a more elaborate way, and furthermore, that he applied a memory-strategy that potentially includes executively driven multi-modal transcoding of information and recruitment of implicit memory resources.

Neuromodulation of sensory networks in monkey brain by focused ultrasound with MRI guidance and detection

Focused ultrasound (FUS) has gained recognition as a technique for non-invasive neuromodulation with high spatial precision and the ability to both excite and inhibit neural activity. Here we demonstrate that MRI-guided FUS is capable of exciting precise targets within areas 3a/3b in the monkey brain, causing downstream activations in off-target somatosensory and associated brain regions which are simultaneously detected by functional MRI. The similarity between natural tactile stimulation-and FUS- evoked fMRI activation patterns suggests that FUS likely can excite populations of neurons and produce associated spiking activities that may be subsequently transmitted to other functionally related touch regions. The across-region differences in fMRI signal changes relative to area 3a/3b between tactile and FUS conditions also indicate that FUS modulated the tactile network differently. The significantly faster rising (>1 sec) fMRI signals elicited by direct FUS stimulation at the targeted cortical region suggest that a different neural hemodynamic coupling mechanism may be involved in generating fMRI signals. This is the first demonstration of imaging neural excitation effects of FUS with BOLD fMRI on a specific functional circuit in non-human primates.

Computational mechanisms underlying cortical responses to the affordance properties of visual scenes.

Biologically inspired deep convolutional neural networks (CNNs), trained for computer vision tasks, have been found to predict cortical responses with remarkable accuracy. However, the internal operations of these models remain poorly understood, and the factors that account for their success are unknown. Here we develop a set of techniques for using CNNs to gain insights into the computational mechanisms underlying cortical responses. We focused on responses in the occipital place area (OPA), a scene-selective region of dorsal occipitoparietal cortex. In a previous study, we showed that fMRI activation patterns in the OPA contain information about the navigational affordances of scenes; that is, information about where one can and cannot move within the immediate environment. We hypothesized that this affordance information could be extracted using a set of purely feedforward computations. To test this idea, we examined a deep CNN with a feedforward architecture that had been previously trained for scene classification. We found that responses in the CNN to scene images were highly predictive of fMRI responses in the OPA. Moreover the CNN accounted for the portion of OPA variance relating to the navigational affordances of scenes. The CNN could thus serve as an image-computable candidate model of affordance-related responses in the OPA. We then ran a series of in silico experiments on this model to gain insights into its internal operations. These analyses showed that the computation of affordance-related features relied heavily on visual information at high-spatial frequencies and cardinal orientations, both of which have previously been identified as low-level stimulus preferences of scene-selective visual cortex. These computations also exhibited a strong preference for information in the lower visual field, which is consistent with known retinotopic biases in the OPA. Visualizations of feature selectivity within the CNN suggested that affordance-based responses encoded features that define the layout of the spatial environment, such as boundary-defining junctions and large extended surfaces. Together, these results map the sensory functions of the OPA onto a fully quantitative model that provides insights into its visual computations. More broadly, they advance integrative techniques for understanding visual cortex across multiple level of analysis: from the identification of cortical sensory functions to the modeling of their underlying algorithms.

Discriminability of numerosity-evoked fMRI activity patterns in human intra-parietal cortex reflects behavioral numerical acuity

Areas of the primate intraparietal cortex have been identified as an important substrate of numerical cognition. In human fMRI studies, activity patterns in these and other areas have allowed researchers to read out the numerosity a subject is viewing, but the relation of such decodable information with behavioral numerical proficiency remains unknown.Here, we estimated the precision of behavioral numerosity discrimination (internal Weber fraction) in twelve adult subjects based on psychophysical testing in a delayed numerosity comparison task outside the scanner. FMRI data were then recorded during a similar task, to obtain the accuracy with which the same sample numerosities could be read out from evoked brain activity patterns, as a measure of the precision of the neuronal representation. Sample numerosities were decodable in both early visual and intra-parietal cortex with approximately equal accuracy on average. In parietal cortex, smaller numerosities were better discriminated than larger numerosities of the same ratio, paralleling smaller behavioral Weber fractions for smaller numerosities. Furthermore, in parietal but not early visual cortex, fMRI decoding performance was correlated with behavioral number discrimination acuity across subjects (subjects with a more precise behavioral Weber fraction measured prior to scanning showed greater discriminability of fMRI activity patterns in intraparietal cortex, and more specifically, the right LIP region).These results suggest a crucial role for intra-parietal cortex in supporting a numerical representation which is explicitly read out for numerical decisions and behavior.

Domain-General and Domain-Specific Patterns of Activity Supporting Metacognition in Human Prefrontal Cortex.

Metacognition is the capacity to evaluate the success of one's own cognitive processes in various domains; for example, memory and perception. It remains controversial whether metacognition relies on a domain-general resource that is applied to different tasks or if self-evaluative processes are domain specific. Here, we investigated this issue directly by examining the neural substrates engaged when metacognitive judgments were made by human participants of both sexes during perceptual and memory tasks matched for stimulus and performance characteristics. By comparing patterns of fMRI activity while subjects evaluated their performance, we revealed both domain-specific and domain-general metacognitive representations. Multivoxel activity patterns in anterior prefrontal cortex predicted levels of confidence in a domain-specific fashion, whereas domain-general signals predicting confidence and accuracy were found in a widespread network in the frontal and posterior midline. The demonstration of domain-specific metacognitive representations suggests the presence of a content-rich mechanism available to introspection and cognitive control.SIGNIFICANCE STATEMENT We used human neuroimaging to investigate processes supporting memory and perceptual metacognition. It remains controversial whether metacognition relies on a global resource that is applied to different tasks or if self-evaluative processes are specific to particular tasks. Using multivariate decoding methods, we provide evidence that perceptual- and memory-specific metacognitive representations coexist with generic confidence signals. Our findings reconcile previously conflicting results on the domain specificity/generality of metacognition and lay the groundwork for a mechanistic understanding of metacognitive judgments.

Decoding intentions of self and others from fMRI activity patterns

Previous studies using multi-voxel pattern analysis have decoded the content of participants' delayed intentions from patterns of fMRI data. Here we investigate whether this technique can be used to decode not only participants' own intentions, but also their representation of the intentions held by other people. In other words: if Sam is thinking about Hoki, can we decode the content of Hoki's intention by scanning Sam's brain? We additionally distinguished two components of intentions: action-plans versus goals, and included novel control analyses that allowed us to distinguish intending an outcome from simply expecting it to occur or simulating its consequences. Regions of frontal, parietal, and occipital cortex contained patterns from which it was possible to decode intentions of both self and other. Furthermore, crossclasification between self and other was possible, suggesting overlap between the two. Control analyses suggested that these results reflected visuo-spatial processes by which intentions were generated in our paradigm, rather than anything special about intentions per se. There was no evidence for any representation of intentions as mental states distinct from visuospatial processes involved in generating their content and/or simulating their outcomes. These findings suggest that the brain activity patterns decoded in intention-decoding fMRI studies may reflect domain-general processes rather than being intention-specific.

FMRI correlates of jumping-to-conclusions in patients with delusions: Connectivity patterns and effects of metacognitive training.

BackgroundReasoning biases such as the jumping-to-conclusions bias (JTC) are thought to contribute to delusions. Interventions targeting these biases such as metacognitive training (MCT) may improve delusions. So far, it is not clear whether JTC depends on dopaminergic reward areas that constitute the main action locus of antipsychotic drugs, or on additional cortical areas. The present study aimed to investigate fMRI activation and functional connectivity patterns underlying JTC, and their changes following MCT, in patients with delusions.MethodsParticipants were 25 healthy individuals and 26 patients with current delusions who were either medication-free or on stable medication without sufficient response. We assessed (1) BOLD activity in the task-positive (TPN), task-negative (TNN), and subcortical reward network (RN); (2) Psychophysiological interactions (PPI) of peak activation areas.ResultsPresence of JTC (irrespective of group) was associated with lower RN activity during conclusion events, and with increased effective connectivity between TPN and TNN during draw events. Following MCT, changes were observed in TPN activity and in effective connectivity of inferior parietal cortex (part of the TPN) with all three target networks.ConclusionJTC is associated not only with reward system areas that constitute the main target of antipsychotic drugs, but also with cortical areas, particularly of the TPN.

Generalizable representations of pain, cognitive control, and negative emotion in medial frontal cortex.

The medial frontal cortex (MFC), including anterior midcingulate cortex, has been linked to multiple psychological domains, including cognitive control, pain, and emotion. However, it is unclear whether this region encodes representations of these domains that are generalizable across studies and subdomains. Additionally, if there are generalizable representations, do they reflect a single underlying process shared across domains, or multiple domain-specific processes? We decomposed multivariate patterns of fMRI activity from 270 participants across 18 studies into study-specific, subdomain-specific, and domain-specific components, and identified latent multivariate representations that generalized across subdomains but were specific to each domain. Pain representations were localized to anterior midcingulate cortex, negative emotion representations to ventromedial prefrontal cortex, and cognitive control representations to portions of the dorsal midcingulate. These findings provide evidence for MFC representations that generalize across studies and subdomains, but are specific to distinct psychological domains rather than reducible to a single underlying process.

Reliability-Weighted Integration of Audiovisual Signals Can Be Modulated by Top-down Attention.

Behaviorally, it is well established that human observers integrate signals near-optimally weighted in proportion to their reliabilities as predicted by maximum likelihood estimation. Yet, despite abundant behavioral evidence, it is unclear how the human brain accomplishes this feat. In a spatial ventriloquist paradigm, participants were presented with auditory, visual, and audiovisual signals and reported the location of the auditory or the visual signal. Combining psychophysics, multivariate functional MRI (fMRI) decoding, and models of maximum likelihood estimation (MLE), we characterized the computational operations underlying audiovisual integration at distinct cortical levels. We estimated observers’ behavioral weights by fitting psychometric functions to participants’ localization responses. Likewise, we estimated the neural weights by fitting neurometric functions to spatial locations decoded from regional fMRI activation patterns. Our results demonstrate that low-level auditory and visual areas encode predominantly the spatial location of the signal component of a region’s preferred auditory (or visual) modality. By contrast, intraparietal sulcus forms spatial representations by integrating auditory and visual signals weighted by their reliabilities. Critically, the neural and behavioral weights and the variance of the spatial representations depended not only on the sensory reliabilities as predicted by the MLE model but also on participants’ modality-specific attention and report (i.e., visual vs. auditory). These results suggest that audiovisual integration is not exclusively determined by bottom-up sensory reliabilities. Instead, modality-specific attention and report can flexibly modulate how intraparietal sulcus integrates sensory signals into spatial representations to guide behavioral responses (e.g., localization and orienting).

3D spatially-adaptive canonical correlation analysis: Local and global methods

Local spatially-adaptive canonical correlation analysis (local CCA) with spatial constraints has been introduced to fMRI multivariate analysis for improved modeling of activation patterns. However, current algorithms require complicated spatial constraints that have only been applied to 2D local neighborhoods because the computational time would be exponentially increased if the same method is applied to 3D spatial neighborhoods.In this study, an efficient and accurate line search sequential quadratic programming (SQP) algorithm has been developed to efficiently solve the 3D local CCA problem with spatial constraints. In addition, a spatially-adaptive kernel CCA (KCCA) method is proposed to increase accuracy of fMRI activation maps. With oriented 3D spatial filters anisotropic shapes can be estimated during the KCCA analysis of fMRI time courses. These filters are orientation-adaptive leading to rotational invariance to better match arbitrary oriented fMRI activation patterns, resulting in improved sensitivity of activation detection while significantly reducing spatial blurring artifacts. The kernel method in its basic form does not require any spatial constraints and analyzes the whole-brain fMRI time series to construct an activation map. Finally, we have developed a penalized kernel CCA model that involves spatial low-pass filter constraints to increase the specificity of the method.The kernel CCA methods are compared with the standard univariate method and with two different local CCA methods that were solved by the SQP algorithm. Results show that SQP is the most efficient algorithm to solve the local constrained CCA problem, and the proposed kernel CCA methods outperformed univariate and local CCA methods in detecting activations for both simulated and real fMRI episodic memory data.

Visually Grounded and Textual Semantic Models Differentially Decode Brain Activity Associated with Concrete and Abstract Nouns

Important advances have recently been made using computational semantic models to decode brain activity patterns associated with concepts; however, this work has almost exclusively focused on concrete nouns. How well these models extend to decoding abstract nouns is largely unknown. We address this question by applying state-of-the-art computational models to decode functional Magnetic Resonance Imaging (fMRI) activity patterns, elicited by participants reading and imagining a diverse set of both concrete and abstract nouns. One of the models we use is linguistic, exploiting the recent word2vec skipgram approach trained on Wikipedia. The second is visually grounded, using deep convolutional neural networks trained on Google Images. Dual coding theory considers concrete concepts to be encoded in the brain both linguistically and visually, and abstract concepts only linguistically. Splitting the fMRI data according to human concreteness ratings, we indeed observe that both models significantly decode the most concrete nouns; however, accuracy is significantly greater using the text-based models for the most abstract nouns. More generally this confirms that current computational models are sufficiently advanced to assist in investigating the representational structure of abstract concepts in the brain.