Consistency and variability in functional localisers

The anterior fusiform gyrus: The ghost in the cortical face machine

Face-selective regions in the human ventral occipito-temporal cortex (VOTC) have been defined for decades mainly with functional magnetic resonance imaging. This face-selective VOTC network is traditionally divided in a posterior 'core' system thought to subtend face perception, and regions of the anterior temporal lobe as a semantic memory component of an extended general system. In between these two putative systems lies the anterior fusiform gyrus and surrounding sulci, affected by magnetic susceptibility artifacts. Here we suggest that this methodological gap overlaps with and contributes to a conceptual gap between (visual) perception and semantic memory for faces. Filling this gap with intracerebral recordings and direct electrical stimulation reveals robust face-selectivity in the anterior fusiform gyrus and a crucial role of this region, especially in the right hemisphere, in identity recognition for both familiar and unfamiliar faces. Based on these observations, we propose an integrated theoretical framework for human face (identity) recognition according to which face-selective regions in the anterior fusiform gyrus join the dots between posterior and anterior cortical face memories.

Towards an optimization of functional localizers in non-human primate neuroimaging with (fMRI) frequency-tagging

Non-human primate (NHP) neuroimaging can provide essential insights into the neural basis of human cognitive functions. While functional magnetic resonance imaging (fMRI) localizers can play an essential role in reaching this objective (Russ et al., 2021), they often differ substantially across species in terms of paradigms, measured signals, and data analysis, biasing the comparisons. Here we introduce a functional frequency-tagging face localizer for NHP imaging, successfully developed in humans and outperforming standard face localizers (Gao et al., 2018). FMRI recordings were performed in two awake macaques. Within a rapid 6 Hz stream of natural non-face objects images, human or monkey face stimuli were presented in bursts every 9 s. We also included control conditions with phase-scrambled versions of all images. As in humans, face-selective activity was objectively identified and quantified at the peak of the face-stimulation frequency (0.111 Hz) and its second harmonic (0.222 Hz) in the Fourier domain. Focal activations with a high signal-to-noise ratio were observed in regions previously described as face-selective, mainly in the STS (clusters PL, ML, MF; also, AL, AF), both for human and monkey faces. Robust face-selective activations were also found in the prefrontal cortex of one monkey (PVL and PO clusters). Face-selective neural activity was highly reliable and excluded all contributions from low-level visual cues contained in the amplitude spectrum of the stimuli. These observations indicate that fMRI frequency-tagging provides a highly valuable approach to objectively compare human and monkey visual recognition systems within the same framework.

Enhanced hyperalignment via spatial prior information

Functional alignment between subjects is an important assumption of functional magnetic resonance imaging (fMRI) group-level analysis. However, it is often violated in practice, even after alignment to a standard anatomical template. Hyperalignment, based on sequential Procrustes orthogonal transformations, has been proposed as a method of aligning shared functional information into a common high-dimensional space and thereby improving inter-subject analysis. Though successful, current hyperalignment algorithms have a number of shortcomings, including difficulties interpreting the transformations, a lack of uniqueness of the procedure, and difficulties performing whole-brain analysis. To resolve these issues, we propose the ProMises (Procrustes von Mises-Fisher) model. We reformulate functional alignment as a statistical model and impose a prior distribution on the orthogonal parameters (the von Mises-Fisher distribution). This allows for the embedding of anatomical information into the estimation procedure by penalizing the contribution of spatially distant voxels when creating the shared functional high-dimensional space. Importantly, the transformations, aligned images, and related results are all unique. In addition, the proposed method allows for efficient whole-brain functional alignment. In simulations and application to data from four fMRI studies we find that ProMises improves inter-subject classification in terms of between-subject accuracy and interpretability compared to standard hyperalignment algorithms.

Test-retest reliability of a finger-tapping fMRI task in a healthy population

Measuring brain activity during functional MRI (fMRI) tasks is one of the main tools to identify brain biomarkers of disease or neural substrates associated with specific symptoms. However, identifying correct biomarkers relies on reliable measures. Recently, poor reliability was reported for task-based fMRI measures. The present study aimed to demonstrate the reliability of a finger-tapping fMRI task across two sessions in healthy participants. Thirty-one right-handed healthy participants aged 18-60 years took part in two MRI sessions 3 weeks apart during which we acquired finger-tapping task-fMRI. We examined the overlap of activations between sessions using Dice similarity coefficients, assessing their location and extent. Then, we compared amplitudes calculating intraclass correlation coefficients (ICCs) in three sets of regions of interest (ROIs) in the motor network: literature-based ROIs (10-mm-radius spheres centred on peaks of an activation likelihood estimation), anatomical ROIs (regions as defined in an atlas) and ROIs based on conjunction analyses (superthreshold voxels in both sessions). Finger tapping consistently activated expected regions, for example, left primary sensorimotor cortices, premotor area and right cerebellum. We found good-to-excellent overlap of activations for most contrasts (Dice coefficients: .54-.82). Across time, ICCs showed large variability in all ROI sets (.04-.91). However, ICCs in most ROIs indicated fair-to-good reliability (mean = .52). The least specific contrast consistently yielded the best reliability. Overall, the finger-tapping task showed good spatial overlap and fair reliability of amplitudes on group level. Although caution is warranted in interpreting correlations of activations with other variables, identification of activated regions in response to a task and their between-group comparisons are still valid and important modes of analysis in neuroimaging to find population tendencies and differences.

Parallel transmit (pTx) with online pulse design for task-based fMRI at 7 T

PURPOSE

Parallel transmission (pTx) is an approach to improve image uniformity for ultra-high field imaging. In this study, we modified an echo planar imaging (EPI) sequence to design subject-specific pTx pulses online. We compared its performance against EPI with conventional circularly polarised (CP) pulses.

METHODS

We compared the pTx-EPI and CP-EPI sequences in a short EPI acquisition protocol and for two different functional paradigms in six healthy volunteers (2 female, aged 23-36 years, mean age 29.2 years). We chose two paradigms that are typically affected by signal dropout at 7 T: a visual objects localiser to determine face/scene selective brain regions and a semantic-processing task.

RESULTS

Across all subjects, pTx-EPI improved whole-brain mean temporal signal-to-noise ratio (tSNR) by 11.0% compared to CP-EPI. We also compared the ability of pTx-EPI and CP-EPI to detect functional activation for three contrasts over the two paradigms: face > object and scene > object for the visual objects localiser and semantic association > pattern matching for the semantic-processing paradigm. Across all three contrasts, pTx-EPI showed higher median z-scores and detected more active voxels in relevant areas, as determined from previous 3 T studies.

CONCLUSION

We have demonstrated a workflow for EPI acquisitions with online per-subject pulse calculations. We saw improved performance in both tSNR and functional acquisitions from pTx-EPI. Thus, we believe that online calculation pTx-EPI is robust enough for future fMRI studies, especially where activation is expected in brain areas liable to significant signal dropout.

BAYESIAN FUNCTIONAL REGISTRATION OF FMRI ACTIVATION MAPS

Functional magnetic resonance imaging (fMRI) has provided invaluable insight into our understanding of human behavior. However, large inter-individual differences in both brain anatomy and functional localization after anatomical alignment remain a major limitation in conducting group analyses and performing population level inference. This paper addresses this problem by developing and validating a new computational technique for reducing misalignment across individuals in functional brain systems by spatially transforming each subjects functional data to a common reference map. Our proposed Bayesian functional registration approach allows us to assess differences in brain function across subjects and individual differences in activation topology. It combines intensity-based and feature-based information into an integrated framework, and allows inference to be performed on the transformation via the posterior samples. We evaluate the method in a simulation study and apply it to data from a study of thermal pain. We find that the proposed approach provides increased sensitivity for group-level inference.

Measuring the response to visually presented faces in the human lateral prefrontal cortex

Neuroimaging studies identify multiple face-selective areas in the human brain. In the current study, we compared the functional response of the face area in the lateral prefrontal cortex to that of other face-selective areas. In Experiment 1, participants (n = 32) were scanned viewing videos containing faces, bodies, scenes, objects, and scrambled objects. We identified a face-selective area in the right inferior frontal gyrus (rIFG). In Experiment 2, participants (n = 24) viewed the same videos or static images. Results showed that the rIFG, right posterior superior temporal sulcus (rpSTS), and right occipital face area (rOFA) exhibited a greater response to moving than static faces. In Experiment 3, participants (n = 18) viewed face videos in the contralateral and ipsilateral visual fields. Results showed that the rIFG and rpSTS showed no visual field bias, while the rOFA and right fusiform face area (rFFA) showed a contralateral bias. These experiments suggest two conclusions; firstly, in all three experiments, the face area in the IFG was not as reliably identified as face areas in the occipitotemporal cortex. Secondly, the similarity of the response profiles in the IFG and pSTS suggests the areas may perform similar cognitive functions, a conclusion consistent with prior neuroanatomical and functional connectivity evidence.

Twenty years of investigation with the case of prosopagnosia PS to understand human face identity recognition. Part II: Neural basis

Patient PS sustained her dramatic brain injury in 1992, the same year as the first report of a neuroimaging study of human face recognition. The present paper complements the review on the functional nature of PS's prosopagnosia (part I), illustrating how her case study directly, i.e., through neuroimaging investigations of her brain structure and activity, but also indirectly, through neural studies performed on other clinical cases and neurotypical individuals, inspired and constrained neural models of human face recognition. In the dominant right hemisphere for face recognition in humans, PS's main lesion concerns (inputs to) the inferior occipital gyrus (IOG), in a region where face-selective activity is typically found in normal individuals ('Occipital Face Area', OFA). Her case study initially supported the criticality of this region for face identity recognition (FIR) and provided the impetus for transcranial magnetic stimulation (TMS), intracerebral electrical stimulation, and cortical surgery studies that have generally supported this view. Despite PS's right IOG lesion, typical face-selectivity is found anteriorly in the middle portion of the fusiform gyrus, a hominoid structure (termed the right 'Fusiform Face Area', FFA) that is widely considered to be the most important region for human face recognition. This finding led to the original proposal of direct anatomico-functional connections from early visual cortices to the FFA, bypassing the IOG/OFA (lulu), a hypothesis supported by further neuroimaging studies of PS, other neurological cases and neuro-typical individuals with original visual stimulation paradigms, data recordings and analyses. The proposal of a lack of sensitivity to face identity in PS's right FFA due to defective reentrant inputs from the IOG/FFA has also been supported by other cases, functional connectivity and cortical surgery studies. Overall, neural studies of, and based on, the case of prosopagnosia PS strongly question the hierarchical organization of the human neural face recognition system, supporting a more flexible and dynamic view of this key social brain function.

Functional Localizers for Motor Areas of the Brain Using fMRI

Neuroimaging researchers increasingly take advantage of the known functional properties of brain regions to localize motor regions in the brain and investigate changes in their activity under various conditions. Using this noninvasive functional MRI (fMRI) method makes it possible to identify and localize brain activation. There are many localizers that can be used to identify brain areas, namely, motor areas such as functional localizer, anatomical localizer, or Atlas mask. Eighteen right-handed participants were recruited for this research to test the reliability of five localizers for primary motor cortex (M1), supplementary motor area (SMA), premotor cortex (PMC), motor cerebellum, and motor thalamus. Motor execution task, namely, hand clenching was used to activate M1, SMA, and motor cerebellum. A combined action observation and motor imagery (AOMI) task was used to functionally activate PMC. Finally, a mask based on Talairach coordinates Atlas was created and used to identify the motor thalamus. Our results show that all localizers were successfully activated in the desired regions of interest. Motor execution successfully activated M1, SMA, and motor cerebellum. A novel localizer based on AOMI was successfully activated in PMC, and the motor thalamus mask obtained from the thalamus mask was successfully implemented on each participant. In conclusion, all five localizers tested in this research were reliable and can be used for rt-fMRI neurofeedback research to define the regions of interest.

Seed-based dual regression: An illustration of the impact of dual regression's inherent filtering of global signal

BACKGROUND

Functional connectivity (FC) maps from brain fMRI data are often derived with seed-based methods that estimate temporal correlations between the time course in a predefined region (seed) and other brain regions (SCA, seed-based correlation analysis). Standard dual regression, which uses a set of spatial regressor maps, can detect FC with entire brain "networks," such as the default mode network, but may not be feasible when detecting FC associated with a single small brain region alone (for example, the amygdala).

NEW METHOD

We explored seed-based dual regression (SDR) from theoretical and practical points of view. SDR is a modified implementation of dual regression where the set of spatial regressors is replaced by a single binary spatial map of the seed region.

RESULTS

SDR allowed detection of FC with small brain regions.

COMPARISON WITH EXISTING METHOD

For both synthetic and natural fMRI data, detection of FC with SDR was identical to that obtained with SCA after removal of global signal from fMRI data with global signal regression (GSR). In the absence of GSR, detection of FC was significantly improved when using SDR compared with SCA.

CONCLUSION

The improved FC detection achieved with SDR was related to a partial filtering of the global signal that occurred during spatial regression, an integral part of dual regression. This filtering can sometimes lead to spurious negative correlations that result in a widespread negative bias in FC derived with any application of dual regression. We provide guidelines for how to identify and correct this potential problem.

Region-of-interest analysis approaches in neuroimaging studies of body ownership: An activation likelihood estimation meta-analysis

How do we feel that we own our body? By manipulating the integration of multisensory signals and creating the illusory experience of owning external body parts and entire bodies, researchers have investigated the neurofunctional correlates of body ownership. Recent attempts to synthesize the neuroimaging literature of body ownership through meta-analysis have shown partly inconsistent results. A large proportion of functional magnetic resonance imaging (fMRI) findings on body ownership include analyses based on regions of interest (ROIs). This approach can produce inflated findings when results are synthesized in meta-analyses. We conducted a systematic search of the fMRI literature of ownership of body parts and entire bodies. Three activation likelihood estimation (ALE) meta-analyses were conducted, testing the impact of including ROI-based findings. When both whole-brain and ROI-based results were included, frontal and posterior parietal multisensory areas were associated with body ownership. When only ROI-based results were included, larger areas of the frontal and posterior parietal cortices and the middle occipital gyrus were associated with body ownership. A whole-brain meta-analysis, excluding ROI-based results, found no significant convergence of activation across the brain. These findings highlight the difficulty of quantitatively synthesizing a neuroimaging field where a large part of the literature is based on findings from ROI-based analyses. We discuss these findings in the light of current practices within this field of research and highlight current problems of meta-analytic approaches of body ownership. We recommend the sharing of unthresholded data as a means to facilitate future meta-analyses of the neuroimaging literature of body ownership.

Identity-mapping cascaded network for fMRI registration

Neuroscience researches based on functional magnetic resonance imaging (fMRI) rely on accurate inter-subject image registration of functional regions. The intersubject alignment of fMRI can improve the statistical power of group analyses. Recent studies have shown the deep learning-based registration methods can be used for registration. In our work, we proposed a 30-Identity-Mapping Cascaded network (30-IMCNet) for rs-fMRI registration. It is a cascaded network that can warp the moving image progressively and finally align to the fixed image. A Combination unit with an identity-mapping path is added to the inputs of each IMCNet to guide the network training. We implemented 30-IMCNet on an rs-fMRI dataset (1000 Functional Connectomes Project dataset) and a task-related fMRI dataset (Eyes Open Eyes Closed fMRI dataset). To evaluate our method, a group-level analysis was implemented in the testing dataset. For rs-fMRI, the criterions such as peakt-value of group-level t-maps, cluster-level evaluation, and intersubject functional network correlation were used to evaluate the quality of the registrations. For task-related fMRI, peakt-value in ALFF paired-t map and peakt-value in ReHo paired-t maps were used. Compared with traditional algorithm FSL, SPM, and deep learning algorithm Kimet al, Zhaoet alour method has improvements of 48.90%, 30.73%, 36.38%, and 16.73% in the peaktvalue of t-maps. Our proposed method can achieve superior functional registration performance and thus gain a significant improvement in functional consistency.

Weight Loss by Low-Calorie Diet Versus Gastric Bypass Surgery in People With Diabetes Results in Divergent Brain Activation Patterns: A Functional MRI Study

OBJECTIVE

Weight loss achieved with very-low-calorie diets (VLCDs) can produce remission of type 2 diabetes (T2D), but weight regain very often occurs with reintroduction of higher calorie intakes. In contrast, bariatric surgery produces clinically significant and durable weight loss, with diabetes remission that translates into reductions in mortality. We hypothesized that in patients living with obesity and prediabetes/T2D, longitudinal changes in brain activity in response to food cues as measured using functional MRI would explain this difference.

RESEARCH DESIGN AND METHODS

Sixteen participants underwent gastric bypass surgery, and 19 matched participants undertook a VLCD (meal replacement) for 4 weeks. Brain responses to food cues and resting-state functional connectivity were assessed with functional MRI pre- and postintervention and compared across groups.

RESULTS

We show that Roux-en-Y gastric bypass surgery (RYGB) results in three divergent brain responses compared with VLCD-induced weight loss: 1) VLCD resulted in increased brain reward center food cue responsiveness, whereas in RYGB, this was reduced; 2) VLCD resulted in higher neural activation of cognitive control regions in response to food cues associated with exercising increased cognitive restraint over eating, whereas RYGB did not; and 3) a homeostatic appetitive system (centered on the hypothalamus) is better engaged following RYGB-induced weight loss than VLCD.

CONCLUSIONS

Taken together, these findings point to divergent brain responses to different methods of weight loss in patients with diabetes, which may explain weight regain after a short-term VLCD in contrast to enduring weight loss after RYGB.

Extracting representations of cognition across neuroimaging studies improves brain decoding

Cognitive brain imaging is accumulating datasets about the neural substrate of many different mental processes. Yet, most studies are based on few subjects and have low statistical power. Analyzing data across studies could bring more statistical power; yet the current brain-imaging analytic framework cannot be used at scale as it requires casting all cognitive tasks in a unified theoretical framework. We introduce a new methodology to analyze brain responses across tasks without a joint model of the psychological processes. The method boosts statistical power in small studies with specific cognitive focus by analyzing them jointly with large studies that probe less focal mental processes. Our approach improves decoding performance for 80% of 35 widely-different functional-imaging studies. It finds commonalities across tasks in a data-driven way, via common brain representations that predict mental processes. These are brain networks tuned to psychological manipulations. They outline interpretable and plausible brain structures. The extracted networks have been made available; they can be readily reused in new neuro-imaging studies. We provide a multi-study decoding tool to adapt to new data.

Reliability of single-subject neural activation patterns in speech production tasks

Speech neuroimaging research targeting individual speakers could help elucidate differences that may be crucial to understanding speech disorders. However, this research necessitates reliable brain activation across multiple speech production sessions. In the present study, we evaluated the reliability of speech-related brain activity measured by functional magnetic resonance imaging data from twenty neuro-typical subjects who participated in two experiments involving reading aloud simple speech stimuli. Using traditional methods like the Dice and intraclass correlation coefficients, we found that most individuals displayed moderate to high reliability. We also found that a novel machine-learning subject classifier could identify these individuals by their speech activation patterns with 97% accuracy from among a dataset of seventy-five subjects. These results suggest that single-subject speech research would yield valid results and that investigations into the reliability of speech activation in people with speech disorders are warranted.

Reliability of neural food cue-reactivity in participants with obesity undergoing bariatric surgery: a 26-week longitudinal fMRI study

Obesity is highly prevalent worldwide and results in a high disease burden. The efforts to monitor and predict treatment outcome in participants with obesity using functional magnetic resonance imaging (fMRI) depends on the reliability of the investigated task-fMRI brain activation. To date, no study has investigated whole-brain reliability of neural food cue-reactivity. To close this gap, we analyzed the longitudinal reliability of an established food cue-reactivity task. Longitudinal reliability of neural food-cue-induced brain activation and subjective food craving ratings over three fMRI sessions (T0: 2 weeks before surgery, T1: 8 weeks and T2: 24 weeks after surgery) were investigated in N = 11 participants with obesity. We computed an array of established reliability estimates, including the intraclass correlation (ICC), the Dice and Jaccard coefficients and similarity of brain activation maps. The data indicated good reliability (ICC > 0.6) of subjective food craving ratings over 26 weeks and excellent reliability (ICC > 0.75) of brain activation signals for the contrast of interest (food > neutral) in the caudate, putamen, thalamus, middle cingulum, inferior, middle and superior occipital gyri, and middle and superior temporal gyri and cunei. Using similarity estimates, it was possible to re-identify individuals based on their neural activation maps (73%) with a fading degree of accuracy, when comparing fMRI sessions further apart. The results show excellent reliability of task-fMRI neural brain activation in several brain regions. Current data suggest that fMRI-based measures might indeed be suitable to monitor and predict treatment outcome in participants with obesity undergoing bariatric surgery.

Deep Representational Similarity Learning for Analyzing Neural Signatures in Task-based fMRI Dataset

Similarity analysis is one of the crucial steps in most fMRI studies. Representational Similarity Analysis (RSA) can measure similarities of neural signatures generated by different cognitive states. This paper develops Deep Representational Similarity Learning (DRSL), a deep extension of RSA that is appropriate for analyzing similarities between various cognitive tasks in fMRI datasets with a large number of subjects, and high-dimensionality - such as whole-brain images. Unlike the previous methods, DRSL is not limited by a linear transformation or a restricted fixed nonlinear kernel function - such as Gaussian kernel. DRSL utilizes a multi-layer neural network for mapping neural responses to linear space, where this network can implement a customized nonlinear transformation for each subject separately. Furthermore, utilizing a gradient-based optimization in DRSL can significantly reduce runtime of analysis on large datasets because it uses a batch of samples in each iteration rather than all neural responses to find an optimal solution. Empirical studies on multi-subject fMRI datasets with various tasks - including visual stimuli, decision making, flavor, and working memory - confirm that the proposed method achieves superior performance to other state-of-the-art RSA algorithms.

Leveraging shared connectivity to aggregate heterogeneous datasets into a common response space

Connectivity hyperalignment can be used to estimate a single shared response space across disjoint datasets. We develop a connectivity-based shared response model that factorizes aggregated fMRI datasets into a single reduced-dimension shared connectivity space and subject-specific topographic transformations. These transformations resolve idiosyncratic functional topographies and can be used to project response time series into shared space. We evaluate this algorithm on a large collection of heterogeneous, naturalistic fMRI datasets acquired while subjects listened to spoken stories. Projecting subject data into shared space dramatically improves between-subject story time-segment classification and increases the dimensionality of shared information across subjects. This improvement generalizes to subjects and stories excluded when estimating the shared space. We demonstrate that estimating a simple semantic encoding model in shared space improves between-subject forward encoding and inverted encoding model performance. The shared space estimated across all datasets is distinct from the shared space derived from any particular constituent dataset; the algorithm leverages shared connectivity to yield a consensus shared space conjoining diverse story stimuli.

Fine-grain atlases of functional modes for fMRI analysis

Population imaging markedly increased the size of functional-imaging datasets, shedding new light on the neural basis of inter-individual differences. Analyzing these large data entails new scalability challenges, computational and statistical. For this reason, brain images are typically summarized in a few signals, for instance reducing voxel-level measures with brain atlases or functional modes. A good choice of the corresponding brain networks is important, as most data analyses start from these reduced signals. We contribute finely-resolved atlases of functional modes, comprising from 64 to 1024 networks. These dictionaries of functional modes (DiFuMo) are trained on millions of fMRI functional brain volumes of total size 2.4 ​TB, spanned over 27 studies and many research groups. We demonstrate the benefits of extracting reduced signals on our fine-grain atlases for many classic functional data analysis pipelines: stimuli decoding from 12,334 brain responses, standard GLM analysis of fMRI across sessions and individuals, extraction of resting-state functional-connectomes biomarkers for 2500 individuals, data compression and meta-analysis over more than 15,000 statistical maps. In each of these analysis scenarii, we compare the performance of our functional atlases with that of other popular references, and to a simple voxel-level analysis. Results highlight the importance of using high-dimensional "soft" functional atlases, to represent and analyze brain activity while capturing its functional gradients. Analyses on high-dimensional modes achieve similar statistical performance as at the voxel level, but with much reduced computational cost and higher interpretability. In addition to making them available, we provide meaningful names for these modes, based on their anatomical location. It will facilitate reporting of results.

Optimizing data processing to improve the reproducibility of single-subject functional magnetic resonance imaging

INTRODUCTION

High reproducibility is critical for ensuring the confidence needed to use functional magnetic resonance imaging (fMRI) activation maps for presurgical planning.

METHODS

In this study, the comparison of different motion correction methods, spatial smoothing methods, regression methods, and thresholding methods was performed to see whether specific data processing methods can be employed to improve the reproducibility of single-subject fMRI activation. Three test-retest metrics were used: the percent difference in activation volume (PDAV), the difference in the center of mass (DCM), and the Dice Similarity Coefficient (DSC).

RESULTS

The PDAV was minimized when using little or no spatial smoothing and AMPLE thresholding. The DCM was minimized when using affine motion correction and little or no spatial smoothing. The DSC was improved when using affine motion correction and generous spatial smoothing. However, it is believed that the overlap metric may be unsuitable for testing fMRI reproducibility.

CONCLUSION

Processing methods to improve fMRI reproducibility were determined. Importantly, the processing methods needed to improve reproducibility were dependent on the fMRI activation metric of interest.

Reliability of BOLD signals in chronic stroke-induced aphasia

Investigating the neurobiology of language impairment and treatment in chronic stroke aphasia using fMRI requires an understanding of measurement variability within and between participants. In this multicenter study, we evaluated the scan-rescan reliability of an auditory and visual (written) story comprehension paradigm in stroke participants with aphasia (N = 65) and healthy controls (N = 22). The multi-modal task was conducted twice (~1 week apart) on separate visits upon study enrolment and twice again at completion three months later. A non-language visuomotor task was studied in the aphasia group only, which was conducted once per time point (3 months apart). While participants were asked to make responses during the comprehension task, these in-scanner responses were not recorded. Reliability was assessed using intraclass correlation coefficient (ICC) at both group and individual participant levels. The visual story comprehension condition had higher reliability than the auditory condition in both groups, with participants with aphasia exhibiting lower reliability than controls in both conditions (stroke ICC = .43, healthy ICC = .81). Differences in reliability within the group of participants with aphasia were found to be partially explained by overall language impairment as well as greater head motion. In the participants with aphasia, the visuomotor paradigm was found to have greater reliability than the story comprehension task at equivalent interscan intervals (visuomotor = 0.50, comprehension = 0.34), and its reliability was not associated with language impairment. This work highlights the importance of considering the reliability of fMRI tasks in aphasia research, provides strategies to improve reliability and has potential implications for the field of clinical neuroimaging in general.

Short-Term Classification Learning Promotes Rapid Global Improvements of Information Processing in Human Brain Functional Connectome

Classification learning is a preeminent human ability within the animal kingdom but the key mechanisms of brain networks regulating learning remain mostly elusive. Recent neuroimaging advancements have depicted human brain as a complex graph machinery where brain regions are nodes and coherent activities among them represent the functional connections. While long-term motor memories have been found to alter functional connectivity in the resting human brain, a graph topological investigation of the short-time effects of learning are still not widely investigated. For instance, classification learning is known to orchestrate rapid modulation of diverse memory systems like short-term and visual working memories but how the brain functional connectome accommodates such modulations is unclear. We used publicly available repositories (openfmri.org) selecting three experiments, two focused on short-term classification learning along two consecutive runs where learning was promoted by trial-by-trial feedback errors, while a further experiment was used as supplementary control. We analyzed the functional connectivity extracted from BOLD fMRI signals, and estimated the graph information processing in the cerebral networks. The information processing capability, characterized by complex network statistics, significantly improved over runs, together with the subject classification accuracy. Instead, null-learning experiments, where feedbacks came with poor consistency, did not provoke any significant change in the functional connectivity over runs. We propose that learning induces fast modifications in the overall brain network dynamics, definitely ameliorating the short-term potential of the brain to process and integrate information, a dynamic consistently orchestrated by modulations of the functional connections among specific brain regions.

Test-Retest Reliability of Functional Magnetic Resonance Imaging Activation for a Vergence Eye Movement Task

Vergence eye movements are the inward and outward rotation of the eyes responsible for binocular coordination. While studies have mapped and investigated the neural substrates of vergence, it is not well understood whether vergence eye movements evoke the blood oxygen level-dependent signal reliably in separate experimental visits. The test-retest reliability of stimulus-induced vergence eye movement tasks during a functional magnetic resonance imaging (fMRI) experiment is important for future randomized clinical trials (RCTs). In this study, we established region of interest (ROI) masks for the vergence neural circuit. Twenty-seven binocularly normal young adults participated in two functional imaging sessions measured on different days on the same 3T Siemens scanner. The fMRI experiments used a block design of sustained visual fixation and rest blocks interleaved between task blocks that stimulated eight or four vergence eye movements. The test-retest reliability of task-activation was assessed using the intraclass correlation coefficient (ICC), and that of spatial extent was assessed using the Dice coefficient. Functional activation during the vergence eye movement task of eight movements compared to rest was repeatable within the primary visual cortex (ICC = 0.8), parietal eye fields (ICC = 0.6), supplementary eye field (ICC = 0.5), frontal eye fields (ICC = 0.5), and oculomotor vermis (ICC = 0.6). The results demonstrate significant test-retest reliability in the ROIs of the vergence neural substrates for functional activation magnitude and spatial extent using the stimulus protocol of a task block stimulating eight vergence eye movements compared to sustained fixation. These ROIs can be used in future longitudinal RCTs to study patient populations with vergence dysfunctions.

Successful Reorganization of Category-Selective Visual Cortex following Occipito-temporal Lobectomy in Childhood

Investigations of functional (re)organization in children who have undergone large cortical resections offer a unique opportunity to elucidate the nature and extent of cortical plasticity. We report findings from a 3-year investigation of a child, U.D., who underwent surgical removal of the right occipital and posterior temporal lobes at age 6 years 9 months. Relative to controls, post-surgically, U.D. showed age-appropriate intellectual performance and visuoperceptual face and object recognition skills. Using fMRI at five different time points, we observed a persistent hemianopia and no visual field remapping. In category-selective visual cortices, however, object- and scene-selective regions in the intact left hemisphere were stable early on, but regions subserving face and word recognition emerged later and evinced competition for cortical representation. These findings reveal alterations in the selectivity and topography of category-selective regions when confined to a single hemisphere and provide insights into dynamic functional changes in extrastriate cortical architecture.

Omission of temporal nuisance regressors from dual regression can improve accuracy of fMRI functional connectivity maps

Functional connectivity (FC) maps from brain fMRI data can be derived with dual regression, a proposed alternative to traditional seed-based FC (SFC) methods that detect temporal correlation between a predefined region (seed) and other regions in the brain. As with SFC, incorporating nuisance regressors (NR) into the dual regression must be done carefully, to prevent potential bias and insensitivity of FC estimates. Here, we explore the potentially untoward effects on dual regression that may occur when NR correlate highly with the signal of interest, using both synthetic and real fMRI data to elucidate mechanisms responsible for loss of accuracy in FC maps. Our tests suggest significantly improved accuracy in FC maps derived with dual regression when highly correlated temporal NR were omitted. Single-map dual regression, a simplified form of dual regression that uses neither spatial nor temporal NR, offers a viable alternative whose FC maps may be more easily interpreted, and in some cases be more accurate than those derived with standard dual regression.

Top-down activation of the visuo-orthographic system during spoken sentence processing

The left ventral occipitotemporal cortex (vOT) is considered the key area of the visuo-orthographic system. However, some studies reported that the area is also involved in speech processing tasks, especially those that require activation of orthographic knowledge. These findings suggest the existence of a top-down activation mechanism allowing such cross-modal activation. Yet, little is known about the involvement of the vOT in more natural speech processing situations like spoken sentence processing. Here, we addressed this issue in a functional Magnetic Resonance Imaging (fMRI) study while manipulating the impacts of two factors, i.e., task demands (semantic vs. low-level perceptual task) and the quality of speech signals (sentences presented against clear vs. noisy background). Analyses were performed at the levels of whole brain and region-of-interest (ROI) focusing on the vOT voxels individually identified through a reading task. Whole brain analysis showed that processing spoken sentences induced activity in a large network including the regions typically involved in phonological, articulatory, semantic and orthographic processing. ROI analysis further specified that a significant part of the vOT voxels that responded to written words also responded to spoken sentences, thus, suggesting that the same area within the left occipitotemporal pathway contributes to both reading and speech processing. Interestingly, both analyses provided converging evidence that vOT responses to speech were sensitive to both task demands and quality of speech signals: Compared to the low-level perceptual task, activity of the area increased when efforts on comprehension were required. The impact of background noise depended on task demands. It led to a decrease of vOT activity in the semantic task but not in the low-level perceptual task. Our results provide new insights into the function of this key area of the reading network, notably by showing that its speech-induced top-down activation also generalizes to ecological speech processing situations.

Multi-Objective Cognitive Model: a Supervised Approach for Multi-subject fMRI Analysis

In order to decode human brain, Multivariate Pattern (MVP) classification generates cognitive models by using functional Magnetic Resonance Imaging (fMRI) datasets. As a standard pipeline in the MVP analysis, brain patterns in multi-subject fMRI dataset must be mapped to a shared space and then a classification model is generated by employing the mapped patterns. However, the MVP models may not provide stable performance on a new fMRI dataset because the standard pipeline uses disjoint steps for generating these models. Indeed, each step in the pipeline includes an objective function with independent optimization approach, where the best solution of each step may not be optimum for the next steps. For tackling the mentioned issue, this paper introduces Multi-Objective Cognitive Model (MOCM) that utilizes an integrated objective function for MVP analysis rather than just using those disjoint steps. For solving the integrated problem, we proposed a customized multi-objective optimization approach, where all possible solutions are firstly generated, and then our method ranks and selects the robust solutions as the final results. Empirical studies confirm that the proposed method can generate superior performance in comparison with other techniques.

Improving the Assessment of Breath-Holding Induced Cerebral Vascular Reactivity Using a Multiband Multi-echo ASL/BOLD Sequence

Breath holding (BH) is a viable vasodilatory stimulus for calculating functional MRI-derived cerebral vascular reactivity (CVR). The BH technique suffers from reduced repeatability compared with gas inhalation techniques; however, extra equipment is needed to perform gas inhalation techniques, and this equipment is not available at all institutions. This study aimed to determine the sensitivity and repeatability of BH activation and CVR using a multiband multi-echo simultaneous arterial spin labelling/blood oxygenation level dependent (ASL/BOLD) sequence. Whole-brain images were acquired in 14 volunteers. Ten subjects returned for repeat imaging. Each subject performed four cycles of 16 s BH on expiration interleaved with paced breathing. Following standard preprocessing, the echoes were combined using a T2*-weighted approach. BOLD and ASL BH activation was computed, and CVR was then determined as the percent signal change related to the activation. The "M" parameter from the Davis Model was also computed by incorporating the ASL signal. Our results showed higher BH activation strength, volume, and repeatability for the combined multi-echo (MEC) data compared with the single-echo data. MEC CVR also had higher repeatability, sensitivity, specificity, and reliability compared with the single-echo BOLD data. These data support the usefulness of an MBME ASL/BOLD acquisition for BH CVR and M measurements.

Diverse pathophysiological processes converge on network disruption in mania

BACKGROUND

Neuroimaging of psychiatric disease is challenged by the difficulty of establishing the causal role of neuroimaging abnormalities. Lesions that cause mania present a unique opportunity to understand how brain network disruption may cause mania in both lesions and in bipolar disorder.

METHODS

A literature search revealed 23 case reports with imaged lesions that caused mania in patients without history of bipolar disorder. We traced these lesions and examined resting-state functional Magnetic Resonance Imaging (rsfMRI) connectivity to these lesions and control lesions to find networks that would be disrupted specifically by mania-causing lesions. The results were then used as regions-of-interest to examine rsfMRI connectivity in patients with bipolar disorder (n = 16) who underwent imaging longitudinally across states of both mania and euthymia alongside a cohort of healthy participants scanned longitudinally. We then sought to replicate these results in independent cohorts of manic (n = 26) and euthymic (n = 21) participants with bipolar disorder.

RESULTS

Mania-inducing lesions overlap significantly in network connectivity. Mania-causing lesions selectively disrupt networks that include orbitofrontal cortex, dorsolateral prefrontal cortex, and temporal lobes. In bipolar disorder, the manic state was reflected in strong, significant, and specific disruption in network communication between these regions and regions implicated in bipolar pathophysiology: the amygdala and ventro-lateral prefrontal cortex.

LIMITATIONS

There was heterogeneity in the clinical characterization of mania causing lesions.

CONCLUSIONS

Lesions causing mania demonstrate shared and specific network disruptions. These disruptions are also observed in bipolar mania and suggest a convergence of multiple disorders on shared circuit dysfunction to cause mania.

fMRIPrep: a robust preprocessing pipeline for functional MRI

Preprocessing of functional MRI (fMRI) involves numerous steps to clean and standardize data before statistical analysis. Generally, researchers create ad-hoc preprocessing workflows for each new dataset, building upon a large inventory of tools available. The complexity of these workflows has snowballed with rapid advances in acquisition and processing. We introduce fMRIPrep, an analysis-agnostic tool that addresses the challenge of robust and reproducible preprocessing for fMRI data. FMRIPrep automatically adapts a best-in-breed workflow to the idiosyncrasies of virtually any dataset, ensuring high-quality preprocessing with no manual intervention. By introducing visual assessment checkpoints into an iterative integration framework for software-testing, we show that fMRIPrep robustly produces high-quality results on a diverse fMRI data collection. Additionally, fMRIPrep introduces less uncontrolled spatial smoothness than commonly used preprocessing tools. FMRIPrep equips neuroscientists with a high-quality, robust, easy-to-use and transparent preprocessing workflow, which can help ensure the validity of inference and the interpretability of their results.

Reliable individual differences in fine-grained cortical functional architecture

Fine-grained functional organization of cortex is not well-conserved across individuals. As a result, individual differences in cortical functional architecture are confounded by topographic idiosyncrasies-i.e., differences in functional-anatomical correspondence. In this study, we used hyperalignment to align information encoded in topographically variable patterns to study individual differences in fine-grained cortical functional architecture in a common representational space. We characterized the structure of individual differences using three common functional indices, and assessed the reliability of this structure across independent samples of data in a natural vision paradigm. Hyperalignment markedly improved the reliability of individual differences across all three indices by resolving topographic idiosyncrasies and accommodating information encoded in spatially fine-grained response patterns. Our results demonstrate that substantial individual differences in cortical functional architecture exist at fine spatial scales, but are inaccessible with anatomical normalization alone.

Evidencing a place for the hippocampus within the core scene processing network

Functional neuroimaging studies have identified several “core” brain regions that are preferentially activated by scene stimuli, namely posterior parahippocampal gyrus (PHG), retrosplenial cortex (RSC), and transverse occipital sulcus (TOS). The hippocampus (HC), too, is thought to play a key role in scene processing, although no study has yet investigated scene‐sensitivity in the HC relative to these other “core” regions. Here, we characterised the frequency and consistency of individual scene‐preferential responses within these regions by analysing a large dataset (n = 51) in which participants performed a one‐back working memory task for scenes, objects, and scrambled objects. An unbiased approach was adopted by applying independently‐defined anatomical ROIs to individual‐level functional data across different voxel‐wise thresholds and spatial filters. It was found that the majority of subjects had preferential scene clusters in PHG (max = 100% of participants), RSC (max = 76%), and TOS (max = 94%). A comparable number of individuals also possessed significant scene‐related clusters within their individually defined HC ROIs (max = 88%), evidencing a HC contribution to scene processing. While probabilistic overlap maps of individual clusters showed that overlap “peaks” were close to those identified in group‐level analyses (particularly for TOS and HC), inter‐individual consistency varied across regions and statistical thresholds. The inter‐regional and inter‐individual variability revealed by these analyses has implications for how scene‐sensitive cortex is localised and interrogated in functional neuroimaging studies, particularly in medial temporal lobe regions, such as the HC. Hum Brain Mapp 37:3779–3794, 2016. © 2016 Wiley Periodicals, Inc.

Reproducibility of R-fMRI metrics on the impact of different strategies for multiple comparison correction and sample sizes

Concerns regarding reproducibility of resting-state functional magnetic resonance imaging (R-fMRI) findings have been raised. Little is known about how to operationally define R-fMRI reproducibility and to what extent it is affected by multiple comparison correction strategies and sample size. We comprehensively assessed two aspects of reproducibility, test-retest reliability and replicability, on widely used R-fMRI metrics in both between-subject contrasts of sex differences and within-subject comparisons of eyes-open and eyes-closed (EOEC) conditions. We noted permutation test with Threshold-Free Cluster Enhancement (TFCE), a strict multiple comparison correction strategy, reached the best balance between family-wise error rate (under 5%) and test-retest reliability/replicability (e.g., 0.68 for test-retest reliability and 0.25 for replicability of amplitude of low-frequency fluctuations (ALFF) for between-subject sex differences, 0.49 for replicability of ALFF for within-subject EOEC differences). Although R-fMRI indices attained moderate reliabilities, they replicated poorly in distinct datasets (replicability < 0.3 for between-subject sex differences, < 0.5 for within-subject EOEC differences). By randomly drawing different sample sizes from a single site, we found reliability, sensitivity and positive predictive value (PPV) rose as sample size increased. Small sample sizes (e.g., < 80 [40 per group]) not only minimized power (sensitivity < 2%), but also decreased the likelihood that significant results reflect "true" effects (PPV < 0.26) in sex differences. Our findings have implications for how to select multiple comparison correction strategies and highlight the importance of sufficiently large sample sizes in R-fMRI studies to enhance reproducibility. Hum Brain Mapp 39:300-318, 2018. © 2017 Wiley Periodicals, Inc.

FReM - Scalable and stable decoding with fast regularized ensemble of models

Brain decoding relates behavior to brain activity through predictive models. These are also used to identify brain regions involved in the cognitive operations related to the observed behavior. Training such multivariate models is a high-dimensional statistical problem that calls for suitable priors. State of the art priors -eg small total-variation- enforce spatial structure on the maps to stabilize them and improve prediction. However, they come with a hefty computational cost. We build upon very fast dimension reduction with spatial structure and model ensembling to achieve decoders that are fast on large datasets and increase the stability of the predictions and the maps. Our approach, fast regularized ensemble of models (FReM), includes an implicit spatial regularization by using a voxel grouping with a fast clustering algorithm. In addition, it aggregates different estimators obtained across splits of a cross-validation loop, each time keeping the best possible model. Experiments on a large number of brain imaging datasets show that our combination of voxel clustering and model ensembling improves decoding maps stability and reduces the variance of prediction accuracy. Importantly, our method requires less samples than state-of-the-art methods to achieve a given level of prediction accuracy. Finally, FreM is much faster than other spatially-regularized methods and, in addition, it can better exploit parallel computing resources.

Abnormalities in the effective connectivity of visuothalamic circuitry in schizophrenia

BACKGROUND

Sensory-processing deficits appear crucial to the clinical expression of symptoms of schizophrenia. The visual cortex displays both dysconnectivity and aberrant spontaneous activity in patients with persistent symptoms and cognitive deficits. In this paper, we examine visual cortex in the context of the remerging notion of thalamic dysfunction in schizophrenia. We examined specific regional and longer-range abnormalities in sensory and thalamic circuits in schizophrenia, and whether these patterns are strong enough to discriminate symptomatic patients from controls.

METHOD

Using publicly available resting fMRI data of 71 controls and 62 schizophrenia patients, we derived conjunction maps of regional homogeneity (ReHo) and fractional amplitude of low-frequency fluctuations (fALFF) to inform further seed-based Granger causality analysis (GCA) to study effective connectivity patterns. ReHo, fALFF and GCA maps were entered into a multiple kernel learning classifier, to determine whether patterns of local and effective connectivity can differentiate controls from patients.

RESULTS

Visual cortex shows both ReHo and fALFF reductions in patients. Visuothalamic effective connectivity in patients was significantly reduced. Local connectivity (ReHo) patterns discriminated patients from controls with the highest level of accuracy of 80.32%.

CONCLUSIONS

Both the inflow and outflow of Granger causal information between visual cortex and thalamus is affected in schizophrenia; this occurs in conjunction with highly discriminatory but localized dysconnectivity and reduced neural activity within the visual cortex. This may explain the visual-processing deficits that are present despite symptomatic remission in schizophrenia.

Progress toward openness, transparency, and reproducibility in cognitive neuroscience

Accumulating evidence suggests that many findings in psychological science and cognitive neuroscience may prove difficult to reproduce; statistical power in brain imaging studies is low and has not improved recently; software errors in analysis tools are common and can go undetected for many years; and, a few large-scale studies notwithstanding, open sharing of data, code, and materials remain the rare exception. At the same time, there is a renewed focus on reproducibility, transparency, and openness as essential core values in cognitive neuroscience. The emergence and rapid growth of data archives, meta-analytic tools, software pipelines, and research groups devoted to improved methodology reflect this new sensibility. We review evidence that the field has begun to embrace new open research practices and illustrate how these can begin to address problems of reproducibility, statistical power, and transparency in ways that will ultimately accelerate discovery.

Introducing Alternative-Based Thresholding for Defining Functional Regions of Interest in fMRI

In fMRI research, one often aims to examine activation in specific functional regions of interest (fROIs). Current statistical methods tend to localize fROIs inconsistently, focusing on avoiding detection of false activation. Not missing true activation is however equally important in this context. In this study, we explored the potential of an alternative-based thresholding (ABT) procedure, where evidence against the null hypothesis of no effect and evidence against a prespecified alternative hypothesis is measured to control both false positives and false negatives directly. The procedure was validated in the context of localizer tasks on simulated brain images and using a real data set of 100 runs per subject. Voxels categorized as active with ABT can be confidently included in the definition of the fROI, while inactive voxels can be confidently excluded. Additionally, the ABT method complements classic null hypothesis significance testing with valuable information by making a distinction between voxels that show evidence against both the null and alternative and voxels for which the alternative hypothesis cannot be rejected despite lack of evidence against the null.

Assessing and tuning brain decoders: Cross-validation, caveats, and guidelines

Decoding, i.e. prediction from brain images or signals, calls for empirical evaluation of its predictive power. Such evaluation is achieved via cross-validation, a method also used to tune decoders' hyper-parameters. This paper is a review on cross-validation procedures for decoding in neuroimaging. It includes a didactic overview of the relevant theoretical considerations. Practical aspects are highlighted with an extensive empirical study of the common decoders in within- and across-subject predictions, on multiple datasets -anatomical and functional MRI and MEG- and simulations. Theory and experiments outline that the popular "leave-one-out" strategy leads to unstable and biased estimates, and a repeated random splits method should be preferred. Experiments outline the large error bars of cross-validation in neuroimaging settings: typical confidence intervals of 10%. Nested cross-validation can tune decoders' parameters while avoiding circularity bias. However we find that it can be favorable to use sane defaults, in particular for non-sparse decoders.

Neural correlates of text-based emoticons: a preliminary fMRI study

INTRODUCTION

Like nonverbal cues in oral interactions, text-based emoticons, which are textual portrayals of a writer's facial expressions, are commonly used in electronic device-mediated communication. Little is known, however, about how text-based emoticons are processed in the human brain. With this study, we investigated whether the text-based emoticons are processed as face expressions using fMRI.

METHODS

During fMRI scan, subjects were asked to respond by pressing a button, indicating whether text-based emoticons represented positive or negative emotions. Voxel-wise analyses were performed to compare the responses and contrasted with emotional versus scrambled emoticons and among emoticons with different emotions. To explore processing strategies for text-based emoticons, brain activity in the bilateral occipital and fusiform face areas were compared.

RESULTS

In the voxel-wise analysis, both emotional and scrambled emoticons were processed mainly in the bilateral fusiform gyri, inferior division of lateral occipital cortex, inferior frontal gyri, dorsolateral prefrontal cortex (DLPFC), dorsal anterior cingulate cortex (dACC), and parietal cortex. In a percent signal change analysis, the right occipital and fusiform face areas showed significantly higher activation than left ones. In comparisons among emoticons, sad one showed significant BOLD signal decrease in the dACC, the left AIC, the bilateral thalamus, and the precuneus as compared with other conditions.

CONCLUSION

The results of this study imply that people recognize text-based emoticons as pictures representing face expressions. Even though text-based emoticons contain emotional meaning, they are not associated with the amygdala while previous studies using emotional stimuli documented amygdala activation.

Cluster failure: Why fMRI inferences for spatial extent have inflated false-positive rates

The most widely used task functional magnetic resonance imaging (fMRI) analyses use parametric statistical methods that depend on a variety of assumptions. In this work, we use real resting-state data and a total of 3 million random task group analyses to compute empirical familywise error rates for the fMRI software packages SPM, FSL, and AFNI, as well as a nonparametric permutation method. For a nominal familywise error rate of 5%, the parametric statistical methods are shown to be conservative for voxelwise inference and invalid for clusterwise inference. Our results suggest that the principal cause of the invalid cluster inferences is spatial autocorrelation functions that do not follow the assumed Gaussian shape. By comparison, the nonparametric permutation test is found to produce nominal results for voxelwise as well as clusterwise inference. These findings speak to the need of validating the statistical methods being used in the field of neuroimaging.

Massive cortical reorganization in sighted Braille readers

The brain is capable of large-scale reorganization in blindness or after massive injury. Such reorganization crosses the division into separate sensory cortices (visual, somatosensory...). As its result, the visual cortex of the blind becomes active during tactile Braille reading. Although the possibility of such reorganization in the normal, adult brain has been raised, definitive evidence has been lacking. Here, we demonstrate such extensive reorganization in normal, sighted adults who learned Braille while their brain activity was investigated with fMRI and transcranial magnetic stimulation (TMS). Subjects showed enhanced activity for tactile reading in the visual cortex, including the visual word form area (VWFA) that was modulated by their Braille reading speed and strengthened resting-state connectivity between visual and somatosensory cortices. Moreover, TMS disruption of VWFA activity decreased their tactile reading accuracy. Our results indicate that large-scale reorganization is a viable mechanism recruited when learning complex skills.

DOI:http://dx.doi.org/10.7554/eLife.10762.001

According to most textbooks, our brain is divided into separate areas that are dedicated to specific senses. We have a visual cortex for vision, a tactile cortex for touch, and so on. However, researchers suspect that this division might not be as fixed as the textbooks say. For example, blind people can switch their 'leftover' visual cortex to non-visual purposes, such as reading Braille – a tactile alphabet.

Can this switch in functional organization also happen in healthy people with normal vision? To investigate this, Siuda-Krzywicka, Bola et al. taught a group of healthy, sighted people to read Braille by touch, and monitored the changes in brain activity that this caused using a technique called functional magnetic resonance imaging. According to textbooks, tactile reading should engage the tactile cortex. Yet, the experiment revealed that the brain activity critical for reading Braille by touch did not occur in the volunteers’ tactile cortex, but in their visual cortex.

Further experiments used a technique called transcranial magnetic stimulation to suppress the activity of the visual cortex of the volunteers. This impaired their ability to read Braille by touch. This is a clear-cut proof that sighted adults can re-program their visual cortex for non-visual, tactile purposes.

These results show that intensive training in a complex task can overcome the sensory division-of-labor of our brain. This indicates that our brain is much more flexible than previously thought, and that such flexibility might occur when we learn everyday, complex skills such as driving a car or playing a musical instrument.

The next question that follows from this work is: what enables the brain’s activity to change after learning to read Braille? To understand this, Siuda-Krzywicka, Bola et al. are currently exploring how the physical structure of the brain changes as a result of a person acquiring the ability to read Braille by touch.

DOI:http://dx.doi.org/10.7554/eLife.10762.002

Efficacy of identifying neural components in the face and emotion processing system in schizophrenia using a dynamic functional localizer

Schizophrenia is associated with deficits in face perception and emotion recognition. Despite consistent behavioural results, the neural mechanisms underlying these cognitive abilities have been difficult to isolate, in part due to differences in neuroimaging methods used between studies for identifying regions in the face processing system. Given this problem, we aimed to validate a recently developed fMRI-based dynamic functional localizer task for use in studies of psychiatric populations and specifically schizophrenia. Previously, this functional localizer successfully identified each of the core face processing regions (i.e. fusiform face area, occipital face area, superior temporal sulcus), and regions within an extended system (e.g. amygdala) in healthy individuals. In this study, we tested the functional localizer success rate in 27 schizophrenia patients and in 24 community controls. Overall, the core face processing regions were localized equally between both the schizophrenia and control group. Additionally, the amygdala, a candidate brain region from the extended system, was identified in nearly half the participants from both groups. These results indicate the effectiveness of a dynamic functional localizer at identifying regions of interest associated with face perception and emotion recognition in schizophrenia. The use of dynamic functional localizers may help standardize the investigation of the facial and emotion processing system in this and other clinical populations.

Visualising inter-subject variability in fMRI using threshold-weighted overlap maps

Functional neuroimaging studies are revealing the neural systems sustaining many sensory, motor and cognitive abilities. A proper understanding of these systems requires an appreciation of the degree to which they vary across subjects. Some sources of inter-subject variability might be easy to measure (demographics, behavioural scores, or experimental factors), while others are more difficult (cognitive strategies, learning effects, and other hidden sources). Here, we introduce a simple way of visualising whole-brain consistency and variability in brain responses across subjects using threshold-weighted voxel-based overlap maps. The output quantifies the proportion of subjects activating a particular voxel or region over a wide range of statistical thresholds. The sensitivity of our approach was assessed in 30 healthy adults performing a matching task with their dominant hand. We show how overlap maps revealed many effects that were only present in a subsample of our group; we discuss how overlap maps can provide information that may be missed or misrepresented by standard group analysis, and how this information can help users to understand their data. In particular, we emphasize that functional overlap maps can be particularly useful when it comes to explaining typical (or atypical) compensatory mechanisms used by patients following brain damage.