Infancy is characterized by rapid neurological transformations leading to consolidation of lifelong function capabilities. Studying the infant brain is crucial for understanding how these mechanisms develop during this sensitive period. We review the neuroimaging modalities used with infants in stimulus-induced activity paradigms specifically, for the unique opportunity the latter provide for assessment of brain function.

Instructed fear, which denotes fearful emotions learned from others' verbal instructions, is an important form of fear acquisition in humans. Maladaptive instructed fear produces detrimental effects on health, but little is known about performing an efficient regulation of instructed fear and its underlying neural substrates. To address this question, 26 subjects performed an instructed fear task where emotional experiences and functional neuroimages were recorded during watching, explicit regulation (calmness imagination), and implicit regulation (calmness priming) conditions. Results indicated that implicit regulation decreased activity in the left amygdala and left insula for instructed fear; however, these effects were absent in explicit regulation. The implementation of implicit regulation did not increase activity in the frontoparietal control regions, while explicit regulation increased dorsolateral prefrontal cortex activity. Furthermore, implicit regulation increased functional connectivity between the right amygdala and right fusiform gyrus, and decreased functional connectivity between the right medial temporal gyrus and left inferior frontal gyrus, which are key nodes of memory retrieval and cognitive control networks, respectively. These findings suggest a favourable effect of implicit regulation on instructed fear, which is subserved by less involvement of control-related brain mechanisms.

Brand love is a critical concept for building a relationship between brands and consumers because falling in love with a brand can lead to strong brand loyalty. Despite the importance of marketing strategies, however, the underlying neural mechanisms of brand love remain unclear. The present study used an activation likelihood estimation meta-analysis method to investigate the neural correlates of brand love and compared it with those of maternal and romantic love. In total, 47 experiments investigating brand, maternal, and romantic love were examined, and the neural systems involved for the three loves were compared and contrasted. Results revealed that the putamen and insula were commonly activated in the three loves. Moreover, activated brain regions in brand love were detected in the dorsal striatum. Activated regions for maternal love were detected in the cortical area and globus pallidus and were associated with pair bonds, empathy, and altruism. Finally, those for romantic love were detected in the hedonic, strong passionate, and intimate-related regions, such as the nucleus accumbens and ventral tegmental area. Thus, the common regions of brain activation between brand and romantic love were in the dorsal striatum. Meanwhile, no common activated regions were observed between brand and maternal love except for the regions shared among the three love types. Although brand love shared little with the two interpersonal (maternal and romantic) loves and relatively resembled aspects of romantic rather than maternal love, our results demonstrated that brand love may have intrinsically different dispositions from the two interpersonal loves.

Previous studies have identified altered brain changes in chronic pain patients, however, it remains unclear whether these changes are reversible. We summarized the neural and molecular changes in patients with chronic pain and employed a meta-analysis approach to quantify the changes. We included 75 studies and 11 of these 75 studies were included in the activation likelihood estimation (ALE) analysis. In the 62 functional magnetic resonance imaging (fMRI) studies, the primary somatosensory and motor cortex (SI and MI), thalamus, insula, and anterior cingulate cortex (ACC) showed significantly decreased activity after the treatments compared to baseline. In the 13 positron emission tomography (PET) studies, the SI, MI, thalamus, and insula showed significantly increased glucose uptake, blood flow, and opioid-receptor binding potentials after the treatments compared to baseline. A meta-analysis of fMRI studies in patients with chronic pain, during pain-related tasks, showed a significant deactivation likelihood cluster in the left medial posterior thalamus. Further studies are warranted to understand brain reorganization in patients with chronic pain compared to the normal state, in terms of its relationship with symptom reduction and baseline conditions.

In recent years, functional magnetic resonance imaging (fMRI) has been widely used in studies that explored the personality-brain association. Researches on personality neuroscience have the potential to provide personality psychology with explanatory models-that is, why people differ from each other rather than how they differ from each other (DeYoung and Gray, 2009). As one of the most important dimensions of personality traits, extraversion is the most stable core and a universal component in personality theory. The aim of the present study was to employ a fully data-driven approach to study the brain mechanism of extraversion in a sample of 111 healthy adults. The Eysenck Personality Questionnaire (EPQ) was used to measure the personality characteristics of all the subjects. We investigated whether the subjects can be grouped into highly homogeneous communities according to the characteristics of their intrinsic connectivity networks (ICNs). The resultant subjects communities and the representative characteristics of ICNs were then associated to personality concepts. Finally, we found one ICN (salience network) whose subject community profiles exhibited significant associations with Extraversion trait.

The application of deep learning (DL) models to neuroimaging data poses several challenges, due to the high dimensionality, low sample size, and complex temporo-spatial dependency structure of these data. Even further, DL models often act as black boxes, impeding insight into the association of cognitive state and brain activity. To approach these challenges, we introduce the DeepLight framework, which utilizes long short-term memory (LSTM) based DL models to analyze whole-brain functional Magnetic Resonance Imaging (fMRI) data. To decode a cognitive state (e.g., seeing the image of a house), DeepLight separates an fMRI volume into a sequence of axial brain slices, which is then sequentially processed by an LSTM. To maintain interpretability, DeepLight adapts the layer-wise relevance propagation (LRP) technique. Thereby, decomposing its decoding decision into the contributions of the single input voxels to this decision. Importantly, the decomposition is performed on the level of single fMRI volumes, enabling DeepLight to study the associations between cognitive state and brain activity on several levels of data granularity, from the level of the group down to the level of single time points. To demonstrate the versatility of DeepLight, we apply it to a large fMRI dataset of the Human Connectome Project. We show that DeepLight outperforms conventional approaches of uni- and multivariate fMRI analysis in decoding the cognitive states and in identifying the physiologically appropriate brain regions associated with these states. We further demonstrate DeepLight's ability to study the fine-grained temporo-spatial variability of brain activity over sequences of single fMRI samples.

Objectives: This study was conducted in order to investigate the study design and main outcomes of acupuncture neuroimaging studies on low back pain (LBP). Methods: Neuroimaging studies of acupuncture on LBP were collected from three English databases such as PubMed and four Chinese databases such as China National Knowledge Infrastructure (CNKI) from inception to December 31, 2020. Study selection, data extraction, and assessment of risk of bias were performed independently by two investigators. The quality of studies was appraised with the Cochrane's risk of bias tools. Information on basic information, methodology, and brain imaging data were extracted. Results: The literature search returned 310 potentially eligible studies and 19 articles met inclusion criteria; 78.9% of studies chose manual acupuncture as the intervention, 89.5% of studies evaluated functional changes elicited by acupuncture, and 68.4% of studies used resting-state fMRI as imaging condition. The most frequently reported acupuncture-induced brain alterations of LBP patients were in the prefrontal cortex, insula, cerebellum, primary somatosensory cortex, and anterior cingulate cortex. There was a significant correlation between improved clinical outcomes and changes in the brain. Conclusions: The results suggested that improving abnormal structure and functional activities in the brain of the LBP patient is an important mechanism of acupuncture treatment for LBP. The brain regions involved in acupuncture analgesia for LBP were mainly located in the pain matrix, default mode network (DMN), salience network (SN), and descending pain modulatory system (DPMS). However, it was difficult to draw a generalized conclusion due to the heterogeneity of study designs. Further well-designed multimodal neuroimaging studies investigating the mechanism of acupuncture on LBP are warranted.

DNA hypermethylation has been widely observed in temporal lobe epilepsy (TLE), in which NR4A1 knockdown has been reported to be able to alleviate seizure severity in mouse model, while the underlying methylation-imaging pathway modulated by aberrant methylation levels of NR4A1 remains to be clarified in patients with TLE. Here, using multi-site canonical correlation analysis with reference, methylation levels of NR4A1 in blood were used as priori to guide fusion of three MRI features: functional connectivity (FC), fractional anisotropy (FA), and gray matter volume (GMV) for 56 TLE patients and 65 healthy controls. Post-hoc correlations were further evaluated between the identified NR4A1-associated brain components and disease onset. Results suggested that higher NR4A1 methylation levels in TLE were related with impaired temporal-cerebellar and occipital-cerebellar FC strength, lower FA in cingulum (hippocampus), and reduced GMV in putamen, temporal pole, and cerebellum. Moreover, findings were also replicated well in both patient subsets with either right TLE or left TLE only. Particularly, right TLE patients showed poorer cognitive abilities and more severe brain impairment than left TLE patients, especially more reduced GMV in thalamus. In summary, this work revealed a potential imaging-methylation pathway modulated by higher NR4A1 methylation in TLE via data mining, which may impact the above-mentioned multimodal brain circuits and was also associated with earlier disease onset and more cognitive deficits.

Introduction: Fatigue and cognitive dysfunction commonly co-occur in breast cancer patients and survivors. However, the underlying neural mechanism is not clear. We performed a systematic review of studies that used neuroimaging methods to investigate structural and functional changes in the brain associated with fatigue in breast cancer patients and survivors. Methods: We searched PubMed, Scopus, EmBase, and Cochrane CENTRAL from January 2009 to May 2021 for studies that reported brain neuroimaging findings in relationship to fatigue in breast cancer patients or survivors. Neuroimaging methods included magnetic resonance imaging (MRI), positron emission tomography (PET), and electroencephalogram (EEG). We summarized structural and functional neuroimaging changes associated with fatigue. Results: Of the 176 articles retrieved, ten MRI studies reported neuroimaging findings in relationship to fatigue. Together these studies compared 385 breast cancer patients or survivors to 205 controls. Fatigue was associated with reduced white matter integrity and increased glutamate in the insula but changes in gray matter volume were not associated with fatigue score. Nine of the ten studies found significant associations between fatigue and functional changes in the frontoparietal cortex. In response to memory and planning tasks, fatigue was associated with increased activations in several regions of the frontoparietal cortex, however, overall performance on tasks was not reduced. Fatigue was also associated with extensive changes in the connectivity of brain networks that filter endogenous signals (salience network), internal attention (default mode network), and external attention (dorsal attention network). Subcortical regions associated with fatigue included insula (interoception), superior colliculus (sleep regulation), and thalamus (alertness). Functional brain changes before initiation of chemotherapy were a better predictor of post-treatment fatigue than chemotherapy itself. Conclusions: Fatigue in breast cancer is associated with widespread functional changes of brain regions and networks that affect executive function including memory, planning, internal and external attention. Observed changes likely represent a compensatory mechanism through which breast cancer patients and survivors try to maintain adequate executive function. Breast cancer patients scheduled to undergo chemotherapy are at high risk for developing fatigue even before the start of treatment.

Electroencephalographic (EEG) neurofeedback (NFB) is a popular neuromodulation method to help one selectively enhance or inhibit his/her brain activities by means of real-time visual or auditory feedback of EEG signals. Sensory motor rhythm (SMR) NFB protocol has been applied to improve cognitive performance, but a large proportion of participants failed to self-regulate their brain activities and could not benefit from NFB training. Therefore, it is important to identify the neural predictors of SMR up-regulation NFB training performance for a better understanding the mechanisms of individual difference in SMR NFB. Twenty-seven healthy participants (12 males, age: 23.1 ± 2.36) were enrolled to complete three sessions of SMR up-regulation NFB training and collection of multimodal neuroimaging data [resting-state EEG, structural magnetic resonance imaging (MRI), and resting-state functional MRI (fMRI)]. Correlation analyses were performed between within-session NFB learning index and anatomical and functional brain features extracted from multimodal neuroimaging data, in order to identify the neuroanatomical and neurophysiological predictors for NFB learning performance. Lastly, machine learning models were trained to predict NFB learning performance using features from each modality as well as multimodal features. According to our results, most participants were able to successfully increase the SMR power and the NFB learning performance was significantly correlated with a set of neuroimaging features, including resting-state EEG powers, gray/white matter volumes from MRI, regional and functional connectivity (FC) of resting-state fMRI. Importantly, results of prediction analysis indicate that NFB learning index can be better predicted using multimodal features compared with features of single modality. In conclusion, this study highlights the importance of multimodal neuroimaging technique as a tool to explain the individual difference in within-session NFB learning performance, and could provide a theoretical framework for early identification of individuals who cannot benefit from NFB training.

Leveraging a large population-level morphologic, microstructural, and functional neuroimaging dataset, we aimed to elucidate the underlying neurobiology of attention-deficit hyperactivity disorder (ADHD) in children. In addition, we evaluated the applicability of machine learning classifiers to predict ADHD diagnosis based on imaging and clinical information.

Published studies using functional and structural MRI include many errors in the way data are analyzed and conclusions reported. This was observed when working on a comprehensive review of the neural bases of synesthesia, but these errors are probably endemic to neuroimaging studies. All studies reviewed had based their conclusions using Null Hypothesis Significance Tests (NHST). NHST have yet been criticized since their inception because they are more appropriate for taking decisions related to a Null hypothesis (like in manufacturing) than for making inferences about behavioral and neuronal processes. Here I focus on a few key problems of NHST related to brain imaging techniques, and explain why or when we should not rely on "significance" tests. I also observed that, often, the ill-posed logic of NHST was even not correctly applied, and describe what I identified as common mistakes or at least problematic practices in published papers, in light of what could be considered as the very basics of statistical inference. MRI statistics also involve much more complex issues than standard statistical inference. Analysis pipelines vary a lot between studies, even for those using the same software, and there is no consensus which pipeline is the best. I propose a synthetic view of the logic behind the possible methodological choices, and warn against the usage and interpretation of two statistical methods popular in brain imaging studies, the false discovery rate (FDR) procedure and permutation tests. I suggest that current models for the analysis of brain imaging data suffer from serious limitations and call for a revision taking into account the "new statistics" (confidence intervals) logic.

We evaluated and compared the performance of two popular neuroimaging processing platforms: Statistical Parametric Mapping (SPM) and FMRIB Software Library (FSL). We focused on comparing brain segmentations using Kirby21, a magnetic resonance imaging (MRI) replication study with 21 subjects and two scans per subject conducted only a few hours apart. We tested within- and between-platform segmentation reliability both at the whole brain and in 10 regions of interest (ROIs). For a range of fixed probability thresholds we found no differences between-scans within-platform, but large differences between-platforms. We have also found very large differences between- and within-platforms when probability thresholds were changed. A randomized blinded reader study indicated that: (1) SPM and FSL performed well in terms of gray matter segmentation; (2) SPM and FSL performed poorly in terms of white matter segmentation; and (3) FSL slightly outperformed SPM in terms of CSF segmentation. We also found that tissue class probability thresholds can have profound effects on segmentation results. We conclude that the reproducibility of neuroimaging studies depends on the neuroimaging software-processing platform and tissue probability thresholds. Our results suggest that probability thresholds may not be comparable across platforms and consistency of results may be improved by estimating a probability threshold correspondence function between SPM and FSL.

With the rapid development of machine learning techniques, multivariate pattern analysis (MVPA) is becoming increasingly popular in the field of neuroimaging data analysis. Several software packages have been developed to facilitate its application in neuroimaging studies. As most of these software packages are based on command lines, researchers are required to learn how to program, which has greatly limited the use of MVPA for researchers without programming skills. Moreover, lacking a graphical user interface (GUI) also hinders the standardization of the application of MVPA in neuroimaging studies and, consequently, the replication of previous studies or comparisons of results between different studies. Therefore, we developed a GUI-based toolkit for MVPA of neuroimaging data: MVPANI (MVPA for Neuroimaging). Compared with other existing software packages, MVPANI has several advantages. First, MVPANI has a GUI and is, thus, more friendly for non-programmers. Second, MVPANI offers a variety of machine learning algorithms with the flexibility of parameter modification so that researchers can test different algorithms and tune parameters to identify the most suitable algorithms and parameters for their own data. Third, MVPANI also offers the function of data fusion at two levels (feature level or decision level) to utilize complementary information contained in different measures obtained from multimodal neuroimaging techniques. In this paper, we introduce this toolkit and provide four examples to demonstrate its usage, including (1) classification between patients and controls, (2) identification of brain areas containing discriminating information, (3) prediction of clinical scores, and (4) multimodal data fusion.

Despite striking progress in the understanding of the neurobiology of insomnia disorder (ID), about 40% of ID patients do not reach sustained remission with the primary treatments. It is necessary to reveal novel neuroimaging biomarkers for sleep quality in ID. The hypothalamus has a central role in sleep-wake regulation by communicating with different brain regions. However, the functional implications of hypothalamus circuitry with other brain areas remains largely unknown in ID. It may be speculated that dysfunctional circuitry in the hypothalamus is involved in the pathogenesis of ID. Thus, we investigated the different network organizations of the bilateral hypothalamus during the resting-state between 26 ID patients and 28 healthy controls (HC). Correlation analysis has been carried out to link the neuroimaging findings and Pittsburgh sleep quality index (PSQI) scores. Group comparisons reveal that the resting-state functional connectivity (RSFC) between the left hypothalamic region and a few other brain regions, including the medial prefrontal cortex (mPFC) and pallidum, are significantly higher in ID compared with HC. The right inferior temporal cortex showed reduced RSFC with the left hypothalamus. No significantly different RSFC between ID and HC was detected for the right hypothalamus. Positive correlations with PSQI scores were observed for RSFC strength between the left hypothalamus and bilateral mPFC (left: r = 0.2985, p = 0.0393; right: r = 0.3723, p = 0.0056). Similarly, the RSFC strength between the right hypothalamus and bilateral mPFC (left: r = 0.3980, p = 0.0029; right: r = 0.2972, p = 0.0291) also showed significant positive correlations with PSQI scores. In conclusion, we reveal a novel neuroimaging biomarker for sleep quality, i.e., the RSFC strength of the hypothalamus-mPFC pathway. Consistent with the hyperarousal model of ID, our results shed new insights into the implications of the hyper-connection within hypothalamus circuits in the pathology of the ID.

Fusing complementary information from different modalities can lead to the discovery of more accurate diagnostic biomarkers for psychiatric disorders. However, biomarker discovery through data fusion is challenging since it requires extracting interpretable and reproducible patterns from data sets, consisting of shared/unshared patterns and of different orders. For example, multi-channel electroencephalography (EEG) signals from multiple subjects can be represented as a third-order tensor with modes: subject, time, and channel, while functional magnetic resonance imaging (fMRI) data may be in the form of subject by voxel matrices. Traditional data fusion methods rearrange higher-order tensors, such as EEG, as matrices to use matrix factorization-based approaches. In contrast, fusion methods based on coupled matrix and tensor factorizations (CMTF) exploit the potential multi-way structure of higher-order tensors. The CMTF approach has been shown to capture underlying patterns more accurately without imposing strong constraints on the latent neural patterns, i.e., biomarkers. In this paper, EEG, fMRI, and structural MRI (sMRI) data collected during an auditory oddball task (AOD) from a group of subjects consisting of patients with schizophrenia and healthy controls, are arranged as matrices and higher-order tensors coupled along the subject mode, and jointly analyzed using structure-revealing CMTF methods [also known as advanced CMTF (ACMTF)] focusing on unique identification of underlying patterns in the presence of shared/unshared patterns. We demonstrate that joint analysis of the EEG tensor and fMRI matrix using ACMTF reveals significant and biologically meaningful components in terms of differentiating between patients with schizophrenia and healthy controls while also providing spatial patterns with high resolution and improving the clustering performance compared to the analysis of only the EEG tensor. We also show that these patterns are reproducible, and study reproducibility for different model parameters. In comparison to the joint independent component analysis (jICA) data fusion approach, ACMTF provides easier interpretation of EEG data by revealing a single summary map of the topography for each component. Furthermore, fusion of sMRI data with EEG and fMRI through an ACMTF model provides structural patterns; however, we also show that when fusing data sets from multiple modalities, hence of very different nature, preprocessing plays a crucial role.

Neuroimaging research is growing rapidly, providing expansive resources for synthesizing data. However, navigating these dense resources is complicated by the volume of research articles and variety of experimental designs implemented across studies. The advent of machine learning algorithms and text-mining techniques has advanced automated labeling of published articles in biomedical research to alleviate such obstacles. As of yet, a comprehensive examination of document features and classifier techniques for annotating neuroimaging articles has yet to be undertaken. Here, we evaluated which combination of corpus (abstract-only or full-article text), features (bag-of-words or Cognitive Atlas terms), and classifier (Bernoulli naïve Bayes, k-nearest neighbors, logistic regression, or support vector classifier) resulted in the highest predictive performance in annotating a selection of 2,633 manually annotated neuroimaging articles. We found that, when utilizing full article text, data-driven features derived from the text performed the best, whereas if article abstracts were used for annotation, features derived from the Cognitive Atlas performed better. Additionally, we observed that when features were derived from article text, anatomical terms appeared to be the most frequently utilized for classification purposes and that cognitive concepts can be identified based on similar representations of these anatomical terms. Optimizing parameters for the automated classification of neuroimaging articles may result in a larger proportion of the neuroimaging literature being annotated with labels supporting the meta-analysis of psychological constructs.

Familiarity in music has been reported as an important factor modulating emotional and hedonic responses in the brain. Familiarity and repetition may increase the liking of a piece of music, thus inducing positive emotions. Neuroimaging studies have focused on identifying the brain regions involved in the processing of familiar and unfamiliar musical stimuli. However, the use of different modalities and experimental designs has led to discrepant results and it is not clear which areas of the brain are most reliably engaged when listening to familiar and unfamiliar musical excerpts. In the present study, we conducted a systematic review from three databases (Medline, PsychoINFO, and Embase) using the keywords (recognition OR familiar OR familiarity OR exposure effect OR repetition) AND (music OR song) AND (brain OR brains OR neuroimaging OR functional Magnetic Resonance Imaging OR Position Emission Tomography OR Electroencephalography OR Event Related Potential OR Magnetoencephalography). Of the 704 titles identified, 23 neuroimaging studies met our inclusion criteria for the systematic review. After removing studies providing insufficient information or contrasts, 11 studies (involving 212 participants) qualified for the meta-analysis using the activation likelihood estimation (ALE) approach. Our results did not find significant peak activations consistently across included studies. Using a less conservative approach (p < 0.001, uncorrected for multiple comparisons) we found that the left superior frontal gyrus, the ventral lateral (VL) nucleus of the left thalamus, and the left medial surface of the superior frontal gyrus had the highest likelihood of being activated by familiar music. On the other hand, the left insula, and the right anterior cingulate cortex had the highest likelihood of being activated by unfamiliar music. We had expected limbic structures as top clusters when listening to familiar music. But, instead, music familiarity had a motor pattern of activation. This could reflect an audio-motor synchronization to the rhythm which is more engaging for familiar tunes, and/or a sing-along response in one's mind, anticipating melodic, harmonic progressions, rhythms, timbres, and lyric events in the familiar songs. These data provide evidence for the need for larger neuroimaging studies to understand the neural correlates of music familiarity.

The identification of efficient markers of disease progression and response to possibly effective treatments is a key priority for slowly progressive, rare and neurodegenerative diseases, such as Friedreich's ataxia. Various imaging modalities have documented specific abnormalities in Friedreich's ataxia that could be tracked to provide useful indicators of efficacy in clinical trials. Advanced MRI imaging (diffusion tensor imaging, DTI; functional MRI, fMRI; and resting-state fMRI, rs-fMRI) and retinal imaging (optical coherence tomography, OCT) were tested longitudinally in a small group of Friedreich's ataxia patients participating in an open-label clinical trial testing the safety and the efficacy of 6-month treatment with interferon gamma. While the DTI indices documented the slow progression of fractional anisotropy loss, fMRI and rs-fMRI were significantly modified during and after treatment. The fMRI changes significantly correlated with the Scale for the Assessment and Rating of Ataxia, which is used to monitor clinical response. OCT documented the known thickness reduction of the retinal nerve fiber layer thickness, but there was no change over time. This pilot study provides indications for the potential utility of fMRI and rs-fMRI as ancillary measures in clinical trials for Friedreich's ataxia.

In this paper we describe and validate a new coordinate-based method for meta-analysis of neuroimaging data based on an optimized hierarchical clustering algorithm: CluB (Clustering the Brain). The CluB toolbox permits both to extract a set of spatially coherent clusters of activations from a database of stereotactic coordinates, and to explore each single cluster of activation for its composition according to the cognitive dimensions of interest. This last step, called "cluster composition analysis," permits to explore neurocognitive effects by adopting a factorial-design logic and by testing the working hypotheses using either asymptotic tests, or exact tests either in a classic inference, or in a Bayesian-like context. To perform our validation study, we selected the fMRI data from 24 normal controls involved in a reading task. We run a standard random-effects second level group analysis to obtain a "Gold Standard" of reference. In a second step, the subject-specific reading effects (i.e., the linear t-contrast "reading > baseline") were extracted to obtain a coordinates-based database that was used to run a meta-analysis using both CluB and the popular Activation Likelihood Estimation method implemented in the software GingerALE. The results of the two meta-analyses were compared against the "Gold Standard" to compute performance measures, i.e., sensitivity, specificity, and accuracy. The GingerALE method obtained a high level of accuracy (0.967) associated with a high sensitivity (0.728) and specificity (0.971). The CluB method obtained a similar level of accuracy (0.956) and specificity (0.969), notwithstanding a lower level of sensitivity (0.14) due to the lack of prior Gaussian transformation of the data. Finally, the two methods obtained a good-level of concordance (AC1 = 0.93). These results suggested that methods based on hierarchical clustering (and post-hoc statistics) and methods requiring prior Gaussian transformation of the data can be used as complementary tools, with the GingerALE method being optimal for neurofunctional mapping of pooled data according to simpler designs, and the CluB method being preferable to test more specific, and localized, neurocognitive hypotheses according to factorial designs.