An automated pipeline for obtaining labeled ICA-templates corresponding to functional brain systems.

The complexity of our actions and thinking is likely reflected in functional brain networks. Independent component analysis (ICA) is a popular data-driven method to compute group differences between such networks. A common way to investigate network differences is based on ICA maps which are generated from study-specific samples. However, this approach limits the generalizability and reproducibility of the results. Alternatively, network ICA templates can be used, but up to date, few such templates exist and are limited in terms of the functional systems they cover. Here, we propose a simple two-step procedure to obtain ICA-templates corresponding to functional brain systems of the researcher's choice: In step 1, the functional system of interest needs to be defined by means of a statistical parameter map (input), which one can generate with open-source software such as NeuroSynth or BrainMap. In step 2, that map is correlated to group-ICA maps provided by the Human Connectome Project (HCP), which is based on a large sample size and uses high quality and standardized acquisition procedures. The HCP-provided ICA-map with the highest correlation to the input map is then used as an ICA template representing the functional system of interest, for example, for subsequent analyses such as dual regression. We provide a toolbox to complete step 2 of the suggested procedure and demonstrate the usage of our pipeline by producing an ICA templates that corresponds to "motor function" and nine additional brain functional systems resulting in an ICA maps with excellent alignment with the gray matter/white matter boundaries of the brain. Our toolbox generates data in two different file formats: volumetric-based (NIFTI) and combined surface/volumetric files (CIFTI). Compared to 10 existing templates, our procedure output component maps with systematically stronger contribution of gray matter to the ICA z-values compared to white matter voxels in 9/10 cases by at least a factor of 2. The toolbox allows users to investigate functional networks of interest, which will enhance interpretability, reproducibility, and standardization of research investigating functional brain networks.

Early remission in multiple sclerosis is linked to altered coherence of the Cerebellar Network.

BACKGROUND: The development of permanent disability in multiple sclerosis (MS) is highly variable among patients, and the exact mechanisms that contribute to this disability remain unknown. METHODS: Following the idea that the brain has intrinsic network organization, we investigated changes of functional networks in MS patients to identify possible links between network reorganization and remission from clinical episodes in MS. Eighteen relapsing-remitting MS patients (RRMS) in their first clinical manifestation underwent resting-state functional MRI and again during remission. We used ten template networks, identified from independent component analysis, to compare changes in network coherence for each patient compared to those of 44 healthy controls from the Human Connectome Project test-retest dataset (two-sample t-test of pre-post differences). Combining a binomial test with Monte Carlo procedures, we tested four models of how functional coherence might change between the first clinical episode and remission: a network can change its coherence (a) with itself ("one-with-self"), (b) with another network ("one-with-other"), or (c) with a set of other networks ("one-with-many"), or (d) multiple networks can change their coherence with respect to one common network ("many-with-one"). RESULTS: We found evidence supporting two of these hypotheses: coherence decreased between the Executive Control Network and several other networks ("one-with-many" hypothesis), and a set of networks altered their coherence with the Cerebellar Network ("many-with-one" hypothesis). CONCLUSION: Given the unexpected commonality of the Cerebellar Network's altered coherence with other networks (a finding present in more than 70% of the patients, despite their clinical heterogeneity), we conclude that remission in MS may result from learning processes mediated by the Cerebellar Network.

Local and global effects of sedation in resting-state fMRI: a randomized, placebo-controlled comparison between etifoxine and alprazolam.

TSPO ligands are promising alternatives to benzodiazepines in the treatment of anxiety, as they display less pronounced side effects such as sedation, cognitive impairment, tolerance development and abuse potential. In a randomized double-blind repeated-measures study we compare a benzodiazepine (alprazolam) to a TSPO ligand (etifoxine) by assessing side effects and acquiring resting-state fMRI data from 34 healthy participants after 5 days of taking alprazolam, etifoxine or a placebo. To study the effects of the pharmacological interventions in fMRI in detail and across different scales, we combine in our study complementary analysis strategies related to whole-brain functional network connectivity, local connectivity analysis expressed in regional homogeneity, fluctuations in low-frequency BOLD amplitudes and coherency of independent resting-state networks. Participants reported considerable adverse effects such as fatigue, sleepiness and concentration impairments, related to the administration of alprazolam compared to placebo. In resting-state fMRI we found a significant decrease in functional connection density, network efficiency and a decrease in the networks rich-club coefficient related to alprazolam. While observing a general decrease in regional homogeneity in high-level brain networks in the alprazolam condition, we simultaneously could detect an increase in regional homogeneity and resting-state network coherence in low-level sensory regions. Further we found a general increase in the low-frequency compartment of the BOLD signal. In the etifoxine condition, participants did not report any significant side effects compared to the placebo, and we did not observe any corresponding modulations in our fMRI metrics. Our results are consistent with the idea that sedation globally disconnects low-level functional networks, but simultaneously increases their within-connectivity. Further, our results point towards the potential of TSPO ligands in the treatment of anxiety and depression.

Occipital transcranial magnetic stimulation has an activity-dependent suppressive effect.

The effects of transcranial magnetic stimulation (TMS) vary depending on the brain state at the stimulation moment. Four mechanisms have been proposed to underlie these effects: (1) virtual lesion--TMS suppresses neural signals; (2) preferential activation of less active neurons--TMS drives up activity in the stimulated area, but active neurons are saturating; (3) noise generation--TMS adds random neuronal activity, and its effect interacts with stimulus intensity; and (4) noise generation--TMS adds random neuronal activity, and its effect depends on TMS intensity. Here we explore these hypotheses by investigating the effects of TMS on early visual cortex by assessing the contrast response function while varying the adaptation state of the observers. We tested human participants in an orientation discrimination task, in which performance is contingent upon contrast sensitivity. Before each trial, neuronal activation of visual cortex was altered through contrast adaptation to two flickering gratings. In a factorial design, with or without adaptation, a single TMS pulse was delivered simultaneously with targets of varying contrast. Adaptation decreased contrast sensitivity. The effect of TMS on performance was state dependent: TMS decreased contrast sensitivity in the absence of adaptation but increased it after adaptation. None of the proposed mechanisms can account for the results in their entirety, in particular, for the facilitatory effect at intermediate to high contrasts after adaptation. We propose an alternative hypothesis: TMS effects are activity dependent, so that TMS suppresses the most active neurons and thereby changes the balance between excitation and inhibition.

Individualizing Representational Similarity Analysis.

Representational similarity analysis (RSA) is a popular multivariate analysis technique in cognitive neuroscience that uses functional neuroimaging to investigate the informational content encoded in brain activity. As RSA is increasingly being used to investigate more clinically-geared questions, the focus of such translational studies turns toward the importance of individual differences and their optimization within the experimental design. In this perspective, we focus on two design aspects: applying individual vs. averaged behavioral dissimilarity matrices to multiple participants' neuroimaging data and ensuring the congruency between tasks when measuring behavioral and neural representational spaces. Incorporating these methods permits the detection of individual differences in representational spaces and yields a better-defined transfer of information from representational spaces onto multivoxel patterns. Such design adaptations are prerequisites for optimal translation of RSA to the field of precision psychiatry.

Supracategorical fear information revealed by aversively conditioning multiple categories.

Fear-generalization is a critical function for survival, in which an organism extracts information from a specific instantiation of a threat (e.g., the western diamondback rattlesnake in my front yard on Sunday) and learns to fear - and accordingly respond to - pertinent higher-order information (e.g., snakes live in my yard). Previous work investigating fear-conditioning in humans has used functional magnetic resonance imaging (fMRI) to demonstrate that activity patterns representing stimuli from an aversively-conditioned category (CS+) are more similar to each other than those of a neutral category (CS-). Here we used fMRI and multiple aversively-conditioned categories to ask whether we would find only similarity increases within the CS+ categories or also similarity increases between the CS+ categories. Using representational similarity analysis, we correlated several models to activity patterns underlying different brain regions and found that, following fear-conditioning, between-category and within-category similarity increased for the CS+ categories in the insula, superior frontal gyrus (SFG), and the right temporal pole. When specifically investigating fear-generalization, these between- and within-category effects were detected in the SFG. These results advance prior pattern-based neuroimaging work by exploring the effect of aversively-conditioning multiple categories and indicate an extended role for such regions in potentially representing supracategorical information during fear-learning.

Temporal Signal-to-Noise Changes in Combined Multislice- and In-Plane-Accelerated Echo-Planar Imaging with a 20- and 64-Channel Coil.

Echo-planar imaging (EPI) is the most common method of functional MRI for acquiring the blood oxygenation level-dependent (BOLD) contrast, allowing the acquisition of an entire brain volume within seconds. However, because imaging protocols are limited by hardware (e.g., fast gradient switching), researchers must compromise between spatial resolution, temporal resolution, or whole-brain coverage. Earlier attempts to circumvent this problem included developing protocols in which slices of a volume were acquired faster (i.e., in-plane acceleration (S)) or simultaneously (i.e., multislice acceleration (M)). However, applying acceleration methods can lead to a reduction in the temporal signal-to-noise ratio (tSNR): a critical measure of signal stability over time. Using a 20- and 64-channel receiver coil, we show that enabling S-acceleration consistently yielded a substantial decrease in tSNR, regardless of the receiver coil, whereas M-acceleration yielded less pronounced tSNR decrease. Moreover, tSNR losses tended to occur in temporal, insular, and medial brain regions and were more noticeable with the 20-channel coil, while with the 64-channel coil, the tSNR in lateral frontoparietal regions remained relatively stable up to six-fold M-acceleration producing comparable tSNR to that of no acceleration. Such methodological explorations can guide researchers and clinicians in optimizing imaging protocols depending on the brain regions under investigation.

Linking Personality Traits to Individual Differences in Affective Spaces.

Different individuals respond differently to emotional stimuli in their environment. Therefore, to understand how emotions are represented mentally will ultimately require investigations into individual-level information. Here we tasked participants with freely arranging emotionally charged images on a computer screen according to their subjective emotional similarity (yielding a unique affective space for each participant) and subsequently sought external validity of the layout of the individuals' affective spaces through the five-factor personality model (Neuroticism, Extraversion, Openness to Experience, Agreeableness, Conscientiousness) assessed via the NEO Five-Factor Inventory. Applying agglomerative hierarchical clustering to the group-level affective space revealed a set of underlying affective clusters whose within-cluster dissimilarity, per individual, was then correlated with individuals' personality scores. These cluster-based analyses predominantly revealed that the dispersion of the negative cluster showed a positive relationship with Neuroticism and a negative relationship with Conscientiousness, a finding that would be predicted by prior work. Such results demonstrate the non-spurious structure of individualized emotion information revealed by data-driven analyses of a behavioral task (and validated by incorporating psychological measures of personality) and corroborate prior knowledge of the interaction between affect and personality. Future investigations can similarly combine hypothesis- and data-driven methods to extend such findings, potentially yielding new perspectives on underlying cognitive processes, disease susceptibility, or even diagnostic/prognostic markers for mental disorders involving emotion dysregulation.

Activation patterns in visual cortex reveal receptive field size-dependent attentional modulation.

Because our brain cannot process all visual information that enters it, we usually pay attention to only a specific aspect of our visual world. Selective visual attention modulates brain activation in cortical areas corresponding to the attended spatial location. However, visual attention has also been associated with the modulation of activation in different brain areas depending on the relevant spatial scale. In this study, we establish a link between attended spatial scale and receptive field size. We demonstrate that attention to a large or a small object in a visual scene increases activation specifically in brain regions with correspondingly large or small receptive field sizes. To analyze and visualize differential brain activation in contiguous cortical areas we used a mapping strategy evaluating the modelling parameters (beta) from functional magnetic resonance imaging data analysis. Assessment of the course of these parameters along traces in different directions in the visual cortex strengthens our conclusion that selective visual attention modulates brain areas with specific neuronal receptive field size properties corresponding to the task at hand. This also confirms predictions of models of selective attention, that attentional modulation of visual processing critically depends on the receptive field size of neurons across the visual cortex.

Cross-decoding supramodal information in the human brain.

Perceptual decision making is the cognitive process wherein the brain classifies stimuli into abstract categories for more efficient downstream processing. A system that, during categorization, can process information regardless of the information's original sensory modality (i.e., a supramodal system) would have a substantial advantage over a system with dedicated processes for specific sensory modalities. While many studies have probed decision processes through the lens of one sensory modality, it remains unclear whether there are such supramodal brain areas that can flexibly process task-relevant information regardless of the original "format" of the information. To investigate supramodality, one must ensure that supramodal information exists somewhere within the functional architecture by rendering information from multiple sensory systems necessary but insufficient for categorization. To this aim, we tasked participants with categorizing auditory and tactile frequency-modulated sweeps according to learned, supramodal categories in a delayed match-to-category paradigm while we measured their blood-oxygen-level dependent signal with functional MRI. To detect supramodal information, we implemented a set of cross-modality pattern classification analyses, which demonstrated that the left caudate nucleus encodes category-level information but not stimulus-specific information (such as spatial directions and stimulus modalities), while the right inferior frontal gyrus, showing the opposite pattern, encodes stimulus-specific information but not category-level information. Given our paradigm, these results reveal abstract representations in the brain that are independent of motor, semantic, and sensory-specific processing, instead reflecting supramodal, categorical information, which points to the caudate nucleus as a locus of cognitive processes involved in complex behavior.

Functional Connectivity in Multiple Sclerosis: Recent Findings and Future Directions.

Multiple sclerosis is a debilitating disorder resulting from scattered lesions in the central nervous system. Because of the high variability of the lesion patterns between patients, it is difficult to relate existing biomarkers to symptoms and their progression. The scattered nature of lesions in multiple sclerosis offers itself to be studied through the lens of network analyses. Recent research into multiple sclerosis has taken such a network approach by making use of functional connectivity. In this review, we briefly introduce measures of functional connectivity and how to compute them. We then identify several common observations resulting from this approach: (a) high likelihood of altered connectivity in deep-gray matter regions, (b) decrease of brain modularity, (c) hemispheric asymmetries in connectivity alterations, and (d) correspondence of behavioral symptoms with task-related and task-unrelated networks. We propose incorporating such connectivity analyses into longitudinal studies in order to improve our understanding of the underlying mechanisms affected by multiple sclerosis, which can consequently offer a promising route to individualizing imaging-related biomarkers for multiple sclerosis.

The neural representation of an individualized relational affective space.

Humans experience emotions every day. Traditionally, psychology has described emotions through discrete labels (e.g. happy, afraid) or standardized affective dimensions (e.g. valence, arousal), and neuroscience has more recently sought the neurobiological basis of emotions via functional neuroimaging. However, by treating emotions similarly among everyone, we neglect that emotions are individualized; thus the overall relational structure of an individual's emotion information may be vital in understanding how the brain represents emotions. Combining behavioral and functional MRI experiments with similarity analyses, we demonstrate that neural activity patterns in the left insula correspond to the multi-dimensional arrangement of individuals' affective spaces, despite interindividual differences, better than to a group-averaged model of affective space, a standardized valence-arousal space, a semantic category space, and a visual similarity space. This finding suggests that the insula may underlie individual-level affective information processing that is specific to one's own affective states, which offers new opportunities for functional neuroimaging to inform clinical approaches of disorders involving emotion dysregulation.

Relating experimentally-induced fear to pre-existing phobic fear in the human brain.

While prior work has demonstrated that fear-conditioning changes the neural representation of previously neutral stimuli, it remains unknown to what extent this new representation abstracts away from specific fears and which brain areas are involved therein. To investigate this question, we sought commonalities between experimentally-induced fear via electric shocks and pre-existing phobia. Using functional MRI, we tested the effect of fear-conditioning pictures of dogs in 21 spider-fearful participants across three phases: baseline, post-conditioning, and extinction. Considering phobic stimuli as a reference point for the state of fear allowed us to examine whether fear-conditioning renders information patterns of previously neutral stimuli more similar to those of phobic stimuli. We trained a classification algorithm to discriminate information patterns of neutral stimuli (rats) and phobic stimuli and then tested the algorithm on information patterns from the conditioned stimuli (dogs). Performing this cross-decoding analysis at each experimental phase revealed brain regions in which dogs were classified as rats during baseline, as spiders following conditioning, and again as rats after extinction. A follow-up analysis showed that changes in visual perception information cannot explain the changing classification performance. These results demonstrate a common neural representation for processing fear-eliciting information, either pre-existing or acquired by classical conditioning.

Quantifying the spatial resolution of the gradient echo and spin echo BOLD response at 3 Tesla.

The blood oxygen level dependent (BOLD) response, as measured with fMRI, offers good spatial resolution compared to other non-invasive neuroimaging methods. The use of a spin echo technique rather than the conventional gradient echo technique may further improve the resolution by refocusing static dephasing effects around the larger vessels, so sensitizing the signal to the microvasculature. In this work the width of the point spread function (PSF) of the BOLD response at a field strength of 3 Tesla is compared for these two approaches. A double echo EPI pulse sequence with simultaneous collection of gradient echo and spin echo signal allows a direct comparison of the techniques. Rotating multiple-wedge stimuli of different spatial frequencies are used to estimate the width of the BOLD response. Waves of activation are created on the surface of the visual cortex, which begin to overlap as the wedge separation decreases. The modulation of the BOLD response decreases with increasing spatial frequency in a manner dependent on its width. The spin echo response shows a 13% reduction in the width of the PSF, but at a cost of at least 3-fold reduction in contrast to noise ratio.