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Brain atlases and templates are at the heart of neuroimaging analyses, for which they facilitate multimodal registration, enable group comparisons and provide anatomical reference. However, as atlas-based approaches rely on correspondence mapping between images they perform poorly in the presence of structural pathology. Whilst several strategies exist to overcome this problem, their performance is often dependent on the type, size and homogeneity of any lesions present. We therefore propose a new solution, referred to as Virtual Brain Grafting (VBG), which is a fully-automated, open-source workflow to reliably parcellate magnetic resonance imaging (MRI) datasets in the presence of a broad spectrum of focal brain pathologies, including large, bilateral, intra- and extra-axial, heterogeneous lesions with and without mass effect. The core of the VBG approach is the generation of a lesion-free T1-weighted image, which enables further image processing operations that would otherwise fail. Here we validated our solution based on Freesurfer recon-all parcellation in a group of 10 patients with heterogeneous gliomatous lesions, and a realistic synthetic cohort of glioma patients (n = 100) derived from healthy control data and patient data. We demonstrate that VBG outperforms a non-VBG approach assessed qualitatively by expert neuroradiologists and Mann-Whitney U tests to compare corresponding parcellations (real patients U(6,6) = 33, z = 2.738, P < .010, synthetic-patients U(48,48) = 2076, z = 7.336, P < .001). Results were also quantitatively evaluated by comparing mean dice scores from the synthetic-patients using one-way ANOVA (unilateral VBG = 0.894, bilateral VBG = 0.903, and non-VBG = 0.617, P < .001). Additionally, we used linear regression to show the influence of lesion volume, lesion overlap with, and distance from the Freesurfer volumes of interest, on labeling accuracy. VBG may benefit the neuroimaging community by enabling automated state-of-the-art MRI analyses in clinical populations using methods such as FreeSurfer, CAT12, SPM, Connectome Workbench, as well as structural and functional connectomics. To fully maximize its availability, VBG is provided as open software under a Mozilla 2.0 license (https://github.com/KUL-Radneuron/KUL_VBG). [Display omitted] (A) shows T1 images from two patients with gliomatous lesions. VBG is a lesion replacement/filling workflow with one approach for unilateral lesions (uVBG) and one for bilateral lesion (bVBG). (B) shows the lesion filling and recon-all combination selected, (C) & (D) show the output, tissue segmentations (C) and whole brain parcellations (D). If VBG is not used (non-VBG) recon-all may quit without generating a parcellation (hard failure) shown on the lower left, or finish with some errors (soft failures) in the parcellations shown on the lower right. However, using either VBG method allows recon-all to complete where it had previously failed and also improves parcellation quality. (PAT = patient, VBG = virtual brain grafting, uVBG = unilateral VBG, bVBG = bilateral VBG)

Virtual dissection of white matter (WM) using diffusion MRI tractography is confounded by its poor reproducibility. Despite the increased adoption of advanced reconstruction models, early region-of-interest driven protocols based on diffusion tensor imaging (DTI) remain the dominant reference for virtual dissection protocols. Here we bridge this gap by providing a comprehensive description of typical WM anatomy reconstructed using a reproducible automated subject-specific parcellation-based approach based on probabilistic constrained-spherical deconvolution (CSD) tractography. We complement this with a WM template in MNI space comprising 68 bundles, including all associated anatomical tract selection labels and associated automated workflows. Additionally, we demonstrate bundle inter- and intra-subject variability using 40 (20 test-retest) datasets from the human connectome project (HCP) and 5 sessions with varying b-values and number of b-shells from the single-subject Multiple Acquisitions for Standardization of Structural Imaging Validation and Evaluation (MASSIVE) dataset. The most reliably reconstructed bundles were the whole pyramidal tracts, primary corticospinal tracts, whole superior longitudinal fasciculi, frontal, parietal and occipital segments of the corpus callosum and middle cerebellar peduncles. More variability was found in less dense bundles, e.g., the fornix, dentato-rubro-thalamic tract (DRTT), and premotor pyramidal tract. Using the DRTT as an example, we show that this variability can be reduced by using a higher number of seeding attempts. Overall inter-session similarity was high for HCP test-retest data (median weighted-dice = 0.963, stdev = 0.201 and IQR = 0.099). Compared to the HCP-template bundles there was a high level of agreement for the HCP test-retest data (median weighted-dice = 0.747, stdev = 0.220 and IQR = 0.277) and for the MASSIVE data (median weighted-dice = 0.767, stdev = 0.255 and IQR = 0.338). In summary, this WM atlas provides an overview of the capabilities and limitations of automated subject-specific probabilistic CSD tractography for mapping white matter fasciculi in healthy adults. It will be most useful in applications requiring a reproducible parcellation-based dissection protocol, and as an educational resource for applied neuroimaging and clinical professionals. [Display omitted] (Top) shows the FWT pipeline for both CSTs, AF, and motor CC bundles. (Left to right) show the required input structural parcellation maps and a priori atlases for FWT and the resulting virtual dissection include/exclude VOIs. FWT provides two approaches to virtual dissection: (1) is a bundle-specific approach where streamlines are only seeded for the bundle of interest, (2) is a whole brain tractography followed by streamlines segmentation, (top right) shows output tractograms. (Middle) Group-averaged T1 and fODF images are generated from the HCP test-retest data, and FWT is applied to generate the HCP- atlas using the bundle-specific approach (1*). FWT's whole brain tracking and segmentation approach (2*) was applied to the HCP and MASSIVE dataset (right and left) and conducted model-based, and pair-wise similarity analyses and generated voxel-wise cumulative maps per bundle. FWT = Fun With Tracts, FS = FreeSurfer, MSBP = MultiScaleBrainParcellator, PD25 = NIST Parkinson's histological, JHU = John's Hopkins university, Juelich = Juelich university histological atlas, AC/PC = anterior commissure/posterior commissure) UKBB = UK Biobank, SUIT (spatially unbiased cerebellar atlas template), dMRI = diffusion magnetic resonance imaging, CSD = constrained spherical deconvolution, fODF = fiber orientation distribution function, CST = corticospinal tract, AF = arcuate fasciculus, CC = corpus callosum, HCP = human connectome project, MASSIVE = Multiple acquisitions for standardization of structural imaging validation and evaluation.

Structural brain network topology can be altered in case of a brain tumor, due to both the tumor itself and its treatment. In this study, we explored the role of structural whole-brain and nodal network metrics and their association with cognitive functioning. Fifty WHO grade 2–3 adult glioma survivors (> 1-year post-therapy) and 50 matched healthy controls underwent a cognitive assessment, covering six cognitive domains. Raw cognitive assessment scores were transformed into w-scores, corrected for age and education. Furthermore, based on multi-shell diffusion-weighted MRI, whole-brain tractography was performed to create weighted graphs and to estimate whole-brain and nodal graph metrics. Hubs were defined based on nodal strength, betweenness centrality, clustering coefficient and shortest path length in healthy controls. Significant differences in these metrics between patients and controls were tested for the hub nodes (i.e. n = 12) and non-hub nodes (i.e. n = 30) in two mixed-design ANOVAs. Group differences in whole-brain graph measures were explored using Mann–Whitney U tests. Graph metrics that significantly differed were ultimately correlated with the cognitive domain-specific w-scores. Bonferroni correction was applied to correct for multiple testing. In survivors, the bilateral putamen were significantly less frequently observed as a hub ( p bonf  < 0.001). These nodes’ assortativity values were positively correlated with attention ( r (90) > 0.573, p bonf  < 0.001), and proxy IQ ( r (90) > 0.794, p bonf  < 0.001). Attention and proxy IQ were significantly more often correlated with assortativity of hubs compared to non-hubs ( p bonf  < 0.001). Finally, the whole-brain graph measures of clustering coefficient ( r  = 0.685), global ( r  = 0.570) and local efficiency ( r  = 0.500) only correlated with proxy IQ ( p bonf  < 0.001). This study demonstrated potential reorganization of hubs in glioma survivors. Assortativity of these hubs was specifically associated with cognitive functioning, which could be important to consider in future modeling of cognitive outcomes and risk classification in glioma survivors.

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

EEG-correlated fMRI analysis is widely used to detect regional BOLD fluctuations that are synchronized to interictal epileptic discharges, which can provide evidence for localizing the ictal onset zone. However, the typical, asymmetrical and mass-univariate approach cannot capture the inherent, higher order structure in the EEG data, nor multivariate relations in the fMRI data, and it is nontrivial to accurately handle varying neurovascular coupling over patients and brain regions. We aim to overcome these drawbacks in a data-driven manner by means of a novel structured matrix-tensor factorization: the single-subject EEG data (represented as a third-order spectrogram tensor) and fMRI data (represented as a spatiotemporal BOLD signal matrix) are jointly decomposed into a superposition of several sources, characterized by space-time-frequency profiles. In the shared temporal mode, Toeplitz-structured factors account for a spatially specific, neurovascular ‘bridge’ between the EEG and fMRI temporal fluctuations, capturing the hemodynamic response’s variability over brain regions. By analyzing interictal data from twelve patients, we show that the extracted source signatures provide a sensitive localization of the ictal onset zone (10/12). Moreover, complementary parts of the IOZ can be uncovered by inspecting those regions with the most deviant neurovascular coupling, as quantified by two entropy-like metrics of the hemodynamic response function waveforms (9/12). Hence, this multivariate, multimodal factorization provides two useful sets of EEG-fMRI biomarkers, which can assist the presurgical evaluation of epilepsy. We make all code required to perform the computations available at https://github.com/svaneynd/structured-cmtf.

•No significant effect of acute exercise on mnemonic discrimination in healthy youth.•Decreased mnemonic discrimination for positive images.•Exercise did not influence hippocampal or amygdala activation.•No correlation between mnemonic discrimination and neural activity. Acute exercise has been associated with cognitive improvements, particularly in memory processes linked to the hippocampus, such as the ability to discriminate between similar stimuli, called hippocampal pattern separation. This can be assessed behaviorally with a mnemonic discrimination task and neurally with functional magnetic resonance imaging (fMRI). Additionally, previous research has shown an emotional modulatory effect on pattern separation, involving the amygdala. In this randomized between-subject study, we investigated whether a 10-minute bout of moderate-intensity exercise, compared to rest, could enhance pattern separation of neutral and emotional images in a group of healthy adolescents and young adults (n=53). Our results showed no significant effects of exercise on either mnemonic discrimination performance or neural activity in the hippocampus and amygdala. Additionally, arterial spin labeling (ASL) confirmed that there were no significant differences in cerebral blood flow between exercise and rest. We did observe worse discrimination for images with a higher similarity level and worse discrimination for highly similar positive images compared to negative and neutral images. However, there were no significant correlations between behavioral outcomes and neural activity. Exploratory analysis revealed a neural signal consistent with pattern completion in the bilateral CA1 and left CA3, but no evidence of pattern separation. Future studies should optimize the exercise characteristics necessary to robustly enhance pattern separation.

•No effect of caloric labels on liking or neural responses to erythritol vs. sucrose.•Erythritol elicited lower liking ratings than sucrose.•No neural differences between sweeteners.•Brain activity differed between sucrose and water, less so for erythritol vs. water.•Sucrose triggers a stronger craving signature response than erythritol. The beneficial effects of substituting sugar with non-caloric sweeteners (NCSs) remain uncertain due to the mismatch between their rewarding sweet taste and lack of energy content. Functional magnetic resonance imaging (fMRI) studies indicate an influence of cognitive processes (e.g., beliefs, expectations) on reward system responses to NCSs, thereby changing their rewarding properties. We measured the impact of cognitive influences about the caloric content on brain responses and liking ratings to erythritol, a natural NCS with satiating properties, versus sugar (i.e., sucrose). We performed a within-subject, single-blind, counterbalanced fMRI study in 30 healthy males (mean ± SD: age 23 ± 0.6 years, BMI 22.5 ± 0.3 kg/m²). Concentrations of erythritol were individually titrated to match the perceived sweetness intensity of a 16 % sucrose solution. During the scan, sucrose and equisweet erythritol solutions were delivered as 1 mL sips with either correct or purposefully incorrect \"low-calorie\" or \"high-calorie\" labels. After each sip, participants rated sweetness liking. Water with a \"water\" label was used as the control condition. A 2 × 2 ANOVA revealed lower liking ratings for erythritol than sucrose (p < 0.0001), but no main effect of the label, nor label-by-sweetener interaction. General Linear Model (GLM) analysis of brain responses at FDR q < 0.05 showed no main effect of sweetener nor label, nor a label-by-sweetener interaction. However, several patterns of brain activity mediated the differences in subjective liking ratings between the sweeteners. Moreover, different neural responses were found for sucrose vs. water in parcel-wise, SVM, and ROI-based analyses, whereas for erythritol vs. water, only the latter two showed differences. Lastly, sucrose induced a stronger craving signature response compared to erythritol, driven by the pattern specific to drug craving. Liking ratings were lower for erythritol than sucrose, and they were unaffected by the caloric label. There were no differences in neural responses between the sweeteners and labels, except in comparisons with water.

•GABAAR availability was higher in older as compared to young adults.•GABAAR activity and GABA+ levels were similar across age groups.•GABAAR availability, GABAAR activity and GABA+ levels were not correlated. Healthy aging is associated with mechanistic changes in gamma-aminobutyric acid (GABA), the most abundant inhibitory neurotransmitter in the human brain. While previous work mainly focused on magnetic resonance spectroscopy (MRS)-based GABA+ levels and transcranial magnetic stimulation (TMS)-based GABAA receptor (GABAAR) activity in the primary sensorimotor (SM1) cortex, the aim of the current study was to identify age-related differences in positron emission tomography (PET)-based GABAAR availability and its relationship with GABA+ levels (i.e. GABA with the contribution of macromolecules) and GABAAR activity. For this purpose, fifteen young (aged 20–28 years) and fifteen older (aged 65–80 years) participants were recruited. PET and MRS images were acquired using simultaneous time-of-flight PET/MR to evaluate age-related differences in GABAAR availability (distribution volume ratio with pons as reference region) and GABA+ levels. TMS was applied to identify age-related differences in GABAAR activity by measuring short-interval intracortical inhibition (SICI). Whereas GABAAR availability was significantly higher in the SM cortex of older as compared to young adults (18.5%), there were neither age-related differences in GABA+ levels nor SICI. A correlation analysis revealed no significant associations between GABAAR availability, GABAAR activity and GABA+ levels. Although the exact mechanisms need to be further elucidated, it is possible that a higher GABAAR availability in older adults is a compensatory mechanism to ensure optimal inhibitory functionality during the aging process.

The examination of semantic cognition has traditionally identified word concreteness as well as valence as two of the principal dimensions in the representation of conceptual knowledge. More recently, corpus-based vector space models as well as graph-theoretical analysis of large-scale task-related behavioural responses have revolutionized our insight into how the meaning of words is structured. In this fMRI study, we apply representational similarity analysis to investigate the conceptual representation of abstract words. Brain activity patterns were related to a cued-association based graph as well as to a vector-based co-occurrence model of word meaning. Twenty-six subjects (19 females and 7 males) performed an overt repetition task during fMRI. First, we performed a searchlight classification procedure to identify regions where activity is discriminable between abstract and concrete words. These regions were left inferior frontal gyrus, the upper and lower bank of the superior temporal sulcus bilaterally, posterior middle temporal gyrus and left fusiform gyrus. Representational Similarity Analysis demonstrated that for abstract words, the similarity of activity patterns in the cortex surrounding the superior temporal sulcus bilaterally and in the left anterior superior temporal gyrus reflects the similarity in word meaning. These effects were strongest for semantic similarity derived from the cued association-based graph and for affective similarity derived from either of the two models. The latter effect was mainly driven by positive valence words. This research highlights the close neurobiological link between the information structure of abstract and affective word content and the similarity in activity pattern in the lateral and anterior temporal language system. •26 subjects performed an overt repetition task on visual and auditory words.•SVM classification discriminated between concrete vs. abstract words in IFG, fusiform gyrus, superior temporal cortex.•Neural similarities in the superior temporal gyrus and semantic similarities correlated significantly for abstract words.•We highlight the neurobiological link between abstract and affective word content and the temporal language system.

Different pain types may be encoded in different brain circuits. Here, we examine similarities and differences in brain processing of visceral and somatic pain. We analyze data from seven fMRI studies ( N  = 165) and five types of pain and discomfort (esophageal, gastric, and rectal distension, cutaneous thermal stimulation, and vulvar pressure) to establish and validate generalizable pain representations. We first evaluate an established multivariate brain measure, the Neurologic Pain Signature (NPS), as a common nociceptive pain system across pain types. Then, we develop a multivariate classifier to distinguish visceral from somatic pain. The NPS responds robustly in 98% of participants across pain types, correlates with perceived intensity of visceral pain and discomfort, and shows specificity to pain when compared with cognitive and affective conditions from twelve additional studies ( N  = 180). Pre-defined signatures for non-pain negative affect do not respond to visceral pain. The visceral versus the somatic classifier reliably distinguishes somatic (thermal) from visceral (rectal) stimulation in both cross-validation and independent cohorts. Other pain types reflect mixtures of somatic and visceral patterns. These results validate the NPS as measuring a common core nociceptive pain system across pain types, and provide a new classifier for visceral versus somatic pain. Whether the brain processes different types of pain similarly or differently remains unknown. The authors show that an established neurologic pain signature responds to five different types of visceral and somatic pain; they also develop a new classifier that reliably discriminates between both pain modalities.