Semantic verbal fluency brain network: delineating a physiological basis for the functional hubs using dual-echo ASL and graph theory approach.

Objective. Semantic verbal fluency (SFV) is a cognitive process that engages and modulates specific brain areas related to language comprehension and production, decision making, response inhibition, and memory retrieval. The impairment of the brain network responsible for these functions is related to various neurological conditions, and different strategies have been proposed to assess SVF-related deficits in such diseases. In the present study, the concomitant changes of brain perfusion and functional connectivity were investigated during the resting state and SVF task performance.Approach. Arterial spin labeling (ASL), a perfusion-based magnetic resonance imaging (MRI) method, was used with a pseudocontinuous labeling approach and dual-echo readout in 28 healthy right-handed Brazilian Portuguese speakers. The acquisition was performed in a resting state condition and during the performance of a SVF task.Main results. During task performance, a significant increase in cerebral blood flow (CBF) was observed in language-related regions of the frontal lobe, including Brodmann's areas 6, 9, 45, and 47, associated with semantic processing, word retrieval, and speech motor programming. Such regions, along with the posterior cingulate, showed a crucial role in the SVF functional network, assessed by seed-to-voxel and graph analysis. Our approach successfully overcame the generalization problem regarding functional MRI (fMRI) graph analysis with cognitive, task-based paradigms. Moreover, the CBF maps enabled the functional assessment of orbital frontal and temporal regions commonly affected by magnetic susceptibility artifacts in conventional T2*-weighted fMRI approaches.Significance. Our results demonstrated the capability of ASL to evaluate perfusion alterations and functional patterns simultaneously regarding the SVF network providing a quantitative physiological basis to functional hubs in this network, which may support future clinical studies.

Epilepsy under the scope of ultra-high field MRI.

Ultra-high field magnetic resonance imaging (UHF-MRI) is capable of unraveling anatomical structures in a submillimeter range. In addition, its high resonance regime allows the quantification of constitutive molecules in a spatially sensitive manner, a crucial capability for determining the extent and localization of a probable epileptogenic region or the severity of the epilepsy. The main technical challenges for data acquisition under UHF are to produce a strong, homogeneous transverse field, while keeping the tissue power deposition within the safe regulatory guidelines. The nonuniformities caused by destructive and constructive interferences at UHFs required new technologies to accelerate and increase yield regarding time spent and quality achieved. Image quality is the paramount contribution of UHF high-resolution imaging, which is capable to disclose fine details of the hippocampal formation and its surroundings and their changes in the course of epilepsy. Other sequences like diffusion tensor imaging (DTI) and multiecho susceptibility imaging at 7 T in vivo can assist the creation of normative atlases of the hippocampal subfields or the reconstruction of the highly arborized cerebral blood vessels. In our review, we specify the impact of these advanced relevant techniques onto the study of epilepsy. In this context, we focused onto high field high-resolution scanners and clinically-enriched decision-making. Studies on focal dysplasias correlating ex vivo high-resolution imaging with specific histological and ultrastructural patterns showed that white matter hyperintensities were related to a demyelination process and other alterations. Preliminary results correlating thick serial sections through bioptic epileptogenic tissue could extend the strategy to localize degenerated tissue sectors, correlate nature and extent of tissue loss with preoperative diagnosis and postoperative outcome. Finally, this protocol will provide the neurosurgeon with a detailed depiction of the removed pathologic tissue and possible adverse effects by the pathologic tissue left in situ. This article is part of the special issue "NEWroscience 2018".

Non-classical behavior of the default mode network regions during an information processing task.

The default mode network (DMN) efficient deactivation and suppressed functional connectivity (FC) during goal-directed tasks, which require attentional resources, have been considered essential to healthy brain cognition. However, recent studies have shown that DMN regions do not always show the expected behavior. Then, we aimed to investigate the functional activation and connectivity of DMN nodes in young, healthy controls during a goal-directed task. We used an adaptation of the symbol digit modalities test (SDMT) to evaluate the information processing speed (IPS). Twenty-four subjects (10 women, age: 29 ± 7 years) underwent two functional Magnetic Resonance Imaging experiments: one during resting-state and one during a block-designed SDMT paradigm. We superimposed the templates of the DMN on the group activation map and observed the reorganization of the network. For the posterior cingulate cortex (PCC) node of the DMN, which is spatially extensive, comprising the precuneus (dorsal portion) and the posterior cingulate gyrus (PCG, ventral portion), the extent of each region was different between conditions, suggesting different functional roles for them. Therefore, for the functional connectivity (FC) analysis, we split the DMN-PCC region into two regions: left precuneus (BA 7) and PCG. The left precuneus (BA 7) was positively correlated with the left lingual gyrus (BA 17), a task-positive region, and negatively associated with the DMN nodes when comparing task performance with the resting-state condition. The other DMN regions presented the classical antagonistic role during the attentional task. In conclusion, we found that the activation and functional connectivity of the DMN is, in general, suppressed during the information processing. However, the left precuneus BA 7 presented a context-dependent modulatory behavior, working as a transient in-between hub connecting the DMN to task-positive areas. Such findings support studies that show increased activation and excitatory functional connectivity of DMN portions during goal-directed tasks. Moreover, our results may contribute to defining more precise functional correlates of IPS deficits in a wide range of clinical and neurological diseases.

Brain Structural-Functional Connectivity Relationship Underlying the Information Processing Speed.

Human cognition and behavior emerge from neuronal interactions on a brain structural architecture. The convergence (or divergence) between functional dynamics and structural connectivity (SC) and their relationship with cognition are still a pivotal question about the brain. We focused on the information processing speed (IPS), assessed by the Symbol Digit Modalities Test (SDMT), once delayed IPS underlies attention deficits in various clinical conditions. We hypothesize that the SC constrains but does not determine functional connectivity, and such a relationship is related to the cognitive performance. Blood oxygenation level-dependent and diffusion tensor images of healthy young volunteers were acquired in a 3T magnetic resonance imaging machine. Activation maps included the left and right middle frontal gyri, left superior parietal lobule, left precuneus, left inferior frontal gyrus (IFG), right cuneus, left lingual gyrus, and left declive. A network involving such regions and signal propagation from visual, through cognitive, up to motor regions was proposed. Random effects Bayesian model selection showed that the top-down connections have the highest expected and exceedance probabilities. Moreover, all pairs of task-related regions were connected by at least one tract, except for the left declive with the left IFG. The interactions between the right cuneus with left declive were related to the interindividual variability in SDMT performance. Altogether, our findings suggest that the IPS functional network is related to the highest SDMT scores when its effective endogenous connections are suppressed to the detriment of modulation caused by the experimental conditions, with the underlying structure providing low diffusion environments.

Evidence of regional associations between age-related inter-individual differences in resting-state functional connectivity and cortical thinning revealed through a multi-level analysis.

Normal aging incurs functional and anatomical alterations in the brain. Cortical thinning, age-related alterations in resting-state functional connectivity (RSFC) and reductions in fractional amplitude of low frequency fluctuations (fALFF) are key components of brain aging that can be studied by neuroimaging. However, the level of association between these processes has not been fully established. We performed an analysis at multiple-levels, i.e. region or connection and modality, to investigate whether the evidence for the effect of aging on fALFF, RSFC and cortical thickness are associated in a large cohort. Our results show that there is a positive association between the level of evidence of age-related effects in all three in the brain. We also demonstrate that on a regional basis the association between RSFC alterations and cortical atrophy may be either positive or negative, which may relate to compensatory mechanisms predicted by the Scaffolding Theory of Aging and Cognition (STAC).

Prospective motion correction of fMRI: Improving the quality of resting state data affected by large head motion.

The quality of functional MRI (fMRI) data is affected by head motion. It has been shown that fMRI data quality can be improved by prospectively updating the gradients and radio-frequency pulses in response to head motion during image acquisition by using an MR-compatible optical tracking system (prospective motion correction, or PMC). Recent studies showed that PMC improves the temporal Signal to Noise Ratio (tSNR) of resting state fMRI data (rs-fMRI) acquired from subjects not moving intentionally. Besides that, the time courses of Independent Components (ICs), resulting from Independent Component Analysis (ICA), were found to present significant temporal correlation with the motion parameters recorded by the camera. However, the benefits of applying PMC for improving the quality of rs-fMRI acquired under large head movements and its effects on resting state networks (RSN) and connectivity matrices are still unknown. In this study, subjects were instructed to cross their legs at will while rs-fMRI data with and without PMC were acquired, which generated head motion velocities ranging from 4 to 30 â€‹mm/s. We also acquired fMRI data without intentional motion. Independent component analysis of rs-fMRI was performed to evaluate IC maps and time courses of RSNs. We also calculated the temporal correlation among different brain regions and generated connectivity matrices for the different motion and PMC conditions. In our results we verified that the crossing leg movements reduced the tSNR of sessions without and with PMC by 45 and 20%, respectively, when compared to sessions without intentional movements. We have verified an interaction between head motion speed and PMC status, showing stronger attenuation of tSNR for acquisitions without PMC than for those with PMC. Additionally, the spatial definition of major RSNs, such as default mode, visual, left and right central executive networks, was improved when PMC was enabled. Furthermore, motion altered IC-time courses by decreasing power at low frequencies and increasing power at higher frequencies (typically associated with artefacts). PMC partially reversed these alterations of the power spectra. Finally, we showed that PMC provides temporal correlation matrices for data acquired under motion conditions more comparable to those obtained by fMRI sessions where subjects were instructed not to move.

Computer-assisted craniometric evaluation for diagnosis and follow-up of craniofacial asymmetries: SymMetric v. 1.0.

PURPOSE: The current assessment of patients with craniofacial asymmetries is accomplished by physical examination, anamnesis and radiological imaging. We propose a semi-automated, computer-assisted craniofacial evaluation (SymMetric v 1.0) based on orthogonal photography of the patient's head in 3 positions. The system is simple, low-cost, no-radiation or special resources needed. Although it does not substitute CT in cases of doubt between craniosynostosis and positional plagiocephaly, multiple numeric evaluations indicate regional deformities and severity of the asymmetry, which can help in the clinical decision of indicating or not the orthosis in positional deformities, determining treatment duration or evaluating surgical outcomes after correction. METHODS: A Matlab-based tool was developed for digital processing of photographs taken in 3 positions (anterior, superior and lateral). The software guides the user to select visible and reproducible landmarks in each photograph acquisition and calculates multiple indexes and metrics, generating a set of comprehensive plots to offer the user an overview of head and facial symmetry across the orthogonal views. For purposes of demonstration, we evaluated 2 patients (one control and one with non-sinostotic deformity). RESULTS: The results show a clear differentiation of the control and plagiocephalic patient metrics mainly in the superior view, showing potential for diagnosis of the condition, and also detected the clinical improvement during helmet treatment in the follow-up, 3 and 5 months after orthosis' use. CONCLUSION: We presented a proof-of-concept for a low cost, no radiation evaluation system for craniofacial asymmetries, that can be useful in a clinical context for diagnosis and follow-up of patients.

Fractional Anisotropy of Thalamic Nuclei Is Associated With Verticality Misperception After Extra-Thalamic Stroke.

Verticality misperception after stroke is a frequent neurological deficit that leads to postural imbalance and a higher risk of falls. The posterior thalamic nuclei are described to be involved with verticality perception, but it is unknown if extra-thalamic lesions can have the same effect via diaschisis and degeneration of thalamic nuclei. We investigated the relationship between thalamic fractional anisotropy (FA, a proxy of structural integrity), and verticality perception, in patients after stroke with diverse encephalic extra-thalamic lesions. We included 11 first time post-stroke patients with extra-thalamic primary lesions, and compared their region-based FA to a group of 25 age-matched healthy controls. For the patient sample, correlation and regression analyses evaluated the relationship between thalamic nuclei FA and error of postural vertical (PV) and haptic vertical (HV) in the roll (PVroll/HVroll) and pitch planes (PVpitch/HVpitch). Relative to controls, patients showed decreased FA of anterior, ventral anterior, ventral posterior lateral, dorsal, and pulvinar thalamic nuclei, despite the primary lesions being extra-thalamic. We found a significant correlation between HVroll, and FA in the anterior and dorsal nuclei, and PVroll with FA in the anterior nucleus. FA in the anterior, ventral anterior, ventral posterior lateral, dorsal and pulvinar nuclei predicted PV, and FA in the ventral anterior, ventral posterior lateral and dorsal nuclei predicted HV. While prior studies indicate that primary lesions of the thalamus can result in verticality misperception, here we present evidence supporting that secondary degeneration of thalamic nuclei via diaschisis can also be associated with verticality misperception after stroke.

Patient-specific neurosurgical phantom: assessment of visual quality, accuracy, and scaling effects.

BACKGROUND: Training in medical education depends on the availability of standardized materials that can reliably mimic the human anatomy and physiology. One alternative to using cadavers or animal bodies is to employ phantoms or mimicking devices. Styrene-ethylene/butylene-styrene (SEBS) gels are biologically inert and present tunable properties, including mechanical properties that resemble the soft tissue. Therefore, SEBS is an alternative to develop a patient-specific phantom, that provides real visual and morphological experience during simulation-based neurosurgical training. RESULTS: A 3D model was reconstructed and printed based on patient-specific magnetic resonance images. The fused deposition of polyactic acid (PLA) filament and selective laser sintering of polyamid were used for 3D printing. Silicone and SEBS materials were employed to mimic soft tissues. A neuronavigation protocol was performed on the 3D-printed models scaled to three different sizes, 100%, 50%, and 25% of the original dimensions. A neurosurgery team (17 individuals) evaluated the phantom realism as "very good" and "perfect" in 49% and 31% of the cases, respectively, and rated phantom utility as "very good" and "perfect" in 61% and 32% of the cases, respectively. Models in original size (100%) and scaled to 50% provided a quantitative and realistic visual analysis of the patient's cortical anatomy without distortion. However, reduction to one quarter of the original size (25%) hindered visualization of surface details and identification of anatomical landmarks. CONCLUSIONS: A patient-specific phantom was developed with anatomically and spatially accurate shapes, that can be used as an alternative for surgical planning. Printed models scaled to sizes that avoided quality loss might save time and reduce medical training costs.

Neuro-degeneration profile of Alzheimer's patients: A brain morphometry study.

INTRODUCTION: Alzheimer's disease (AD) is a primary and progressive neurodegenerative disorder, which is marked by cognitive deterioration and memory impairment. Atrophy of hippocampus and other basal brain regions is one of the most predominant structural imaging findings related to AD. Most studies have evaluated the pre-clinical and initial stages of AD through clinical trials using Magnetic Resonance Imaging. Structural biomarkers for advanced AD stages have not been evaluated yet, being considered only hypothetically. OBJECTIVE: To evaluate the brain morphometry of AD patients at all disease stages, identifying the structural neuro-degeneration profile associated with AD severity. MATERIAL AND METHODS: AD patients aged 60 years or over at different AD stages were recruited and grouped into three groups following the Clinical Dementia Rating (CDR) score: CDR1 (n = 16), CDR2 (n = 15), CDR3 (n = 13). Age paired healthy volunteers (n = 16) were also recruited (control group). Brain images were acquired on a 3T magnetic resonance scanner using a conventional Gradient eco 3D T1-w sequence without contrast injection. Volumetric quantitative data and cortical thickness were obtained by automatic segmentation using the Freesurfer software. Volume of each brain region was normalized by the whole brain volume in order to minimize age and body size effects. Volume and cortical thickness variations among groups were compared. RESULTS: Atrophy was observed in the hippocampus, amygdala, entorhinal cortex, parahippocampal region, temporal pole and temporal lobe of patients suffering from AD at any stage. Cortical thickness was reduced only in the parahippocampal gyrus at all disease stages. Volume and cortical thickness were correlated with the Mini Mental State Examination (MMSE) score in all studied regions, as well as with CDR and disease duration. DISCUSSION AND CONCLUSION: As previously reported, brain regions affected by AD during its initial stages, such as hippocampus, amygdala, entorhinal cortex, and parahippocampal region, were found to be altered even in individuals with severe AD. In addition, individuals, specifically, with CDR 3, have multiple regions with lower volumes than individuals with a CDR 2. These results indicate that rates of atrophy have not plateaued out at CDR 2-3, and in severe patients there are yet neuronal loss and gliosis. These findings can add important information to the more accepted model in the literature that focuses mainly on early stages. Our findings allow a better understanding on the AD pathophysiologic process and follow-up process of drug treatment even at advanced disease stages.

Behavioral and neuroimaging responses induced by mental imagery of threatening scenarios.

Functional neuroimaging studies have shown that actual situations of uncertain or distant threats increase the activity of forebrain regions, whereas proximal threats increase the activity of the dorsal midbrain. This experiment aimed at testing the hypothesis that brain activity elicited by imagined scenarios of threats with two different magnitudes, potential and imminent, resembles that found in response to actual threats. First, we measured subjective responses to imagined scenarios of potential and imminent threats compared with neutral and pleasant scenarios. The same scenarios were used as a paradigm in a functional magnetic resonance imaging experiment. Behavioral results show that the scenarios draw a gradient of hedonic valence and arousal dimensions. Both potential and imminent threat scenarios increased subjective anxiety; the imminent threat scenario also increased feelings of discomfort and bodily symptoms. The functional magnetic resonance imaging results revealed modulations of BOLD signal in the ventromedial prefrontal cortex by potential threat and in the periaqueductal gray matter by imminent threat. These results agree with previously reported evidence using actual threat situations, indicating that mental imagery is a reliable method for studying the functional neuroanatomy of relevant behavioral processes.

Using network dynamic fMRI for detection of epileptogenic foci.

BACKGROUND: Epilepsy is one of the most prevalent neurological disorders. It remains medically intractable for about one-third of patients with focal epilepsy, for whom precise localization of the epileptogenic zone responsible for seizure initiation may be critical for successful surgery. Existing fMRI literature points to widespread network disturbances in functional connectivity. Per previous scalp and intracranial EEG studies and consistent with excessive local synchronization during interictal discharges, we hypothesized that, relative to same regions in healthy controls, epileptogenic foci would exhibit less chaotic dynamics, identifiable via entropic analyses of resting state fMRI time series. METHODS: In order to first validate this hypothesis on a cohort of patients with known ground truth, here we test individuals with well-defined epileptogenic foci (left mesial temporal lobe epilepsy). We analyzed voxel-wise resting-state fMRI time-series using the autocorrelation function (ACF), an entropic measure of regulation and feedback, and performed follow-up seed-to-voxel functional connectivity analysis. Disruptions in connectivity of the region exhibiting abnormal dynamics were examined in relation to duration of epilepsy and patients' cognitive performance using a delayed verbal memory recall task. RESULTS: ACF analysis revealed constrained (less chaotic) functional dynamics in left temporal lobe epilepsy patients, primarily localized to ipsilateral temporal pole, proximal to presumed focal points. Autocorrelation decay rates differentiated, with 100 % accuracy, between patients and healthy controls on a subject-by-subject basis within a leave-one-subject out classification framework. Regions identified via ACF analysis formed a less efficient network in patients, as compared to controls. Constrained dynamics were linked with locally increased and long-range decreased connectivity that, in turn, correlated significantly with impaired memory (local left temporal connectivity) and epilepsy duration (left temporal - posterior cingulate cortex connectivity). CONCLUSIONS: Our current results suggest that data driven functional MRI methods that target network dynamics hold promise in providing clinically valuable tools for identification of epileptic regions.

A Comparison of Independent Component Analysis (ICA) of fMRI and Electrical Source Imaging (ESI) in Focal Epilepsy Reveals Misclassification Using a Classifier.

Interictal epileptiform discharges (IEDs) can produce haemodynamic responses that can be detected by electroencephalography-functional magnetic resonance imaging (EEG-fMRI) using different analysis methods such as the general linear model (GLM) of IEDs or independent component analysis (ICA). The IEDs can also be mapped by electrical source imaging (ESI) which has been demonstrated to be useful in presurgical evaluation in a high proportion of cases with focal IEDs. ICA advantageously does not require IEDs or a model of haemodynamic responses but its use in EEG-fMRI of epilepsy has been limited by its ability to separate and select epileptic components. Here, we evaluated the performance of a classifier that aims to filter all non-BOLD responses and we compared the spatial and temporal features of the selected independent components (ICs). The components selected by the classifier were compared to those components selected by a strong spatial correlation with ESI maps of IED sources. Both sets of ICs were subsequently compared to a temporal model derived from the convolution of the IEDs (derived from the simultaneously acquired EEG) with a standard haemodynamic response. Selected ICs were compared to the patients' clinical information in 13 patients with focal epilepsy. We found that the misclassified ICs clearly related to IED in 16/25 cases. We also found that the classifier failed predominantly due to the increased spectral range of fMRIs temporal responses to IEDs. In conclusion, we show that ICA can be an efficient approach to separate responses related to epilepsy but that contemporary classifiers need to be retrained for epilepsy data. Our findings indicate that, for ICA to contribute to the analysis of data without IEDs to improve its sensitivity, classification strategies based on data features other than IC time course frequency is required.

Brain activation inhomogeneity highlighted by the Isotropic Anomalous Diffusion filter.

The visual appealing nature of the now popular BOLD fMRI may give the false impression of extreme simplicity, as if the the functional maps could be generated with the press of a single button. However, one can only get plausible maps after long and cautious processing, considering that time and noise come into play during acquisition. One of the most popular ways to account for noise and individual variability in fMRI is the use of a Gaussian spatial filter. Although very robust, this filter may introduce excessive blurring, given the strong dependence of results on the central voxel value. Here, we propose the use of the Isotropic Anomalous Diffusion (IAD) approach, aiming to reduce excessive homogeneity while retaining the natural variability of signal across brain space. We found differences between Gaussian and IAD filters in two parameters gathered from Independent Component maps (ICA), identified on brain areas responsible for auditory processing during rest. Analysis of data gathered from 7 control subjects shows that the IAD filter rendered more localized active areas and higher contrast-to-noise ratios, when compared to equivalent Gaussian filtered data (Student t-test, p<0.05). The results seem promising, since the anomalous filter performs satisfactorily in filtering noise with less distortion of individual localized brain responses.

Auditory stimuli from a sensor glove model modulate cortical audiotactile integration.

The purpose of this study was to shed light on cortical audiotactile integration and sensory substitution mechanisms, thought to serve as a basis for the use of a sensor glove in the preservation of the cortical map of the hand after peripheral nerve injuries. Fourteen subjects were selected and randomly assigned either to a training group, trained to replace touch for hearing with the use of a sensor glove, or to a control group, untrained. Training group volunteers had to identify textures just by the sound. In an fMRI experiment, all subjects received three types of stimuli: tactile only, combined audiotactile stimulation, and auditory only. Results indicate that, for trained subjects, a coupling between auditory and somatosensory cortical areas is established through associative areas. Differences in signal correlation between groups point to a pairing mechanism, which, at first, connects functionally the primary auditory and sensory areas (trained subjects). Later, this connection seems to be mediated by associative areas. The training with the sensor glove influences cortical audiotactile integration mechanisms, determining BOLD signal changes in the somatosensory area during auditory stimulation.

From EEG to BOLD: brain mapping and estimating transfer functions in simultaneous EEG-fMRI acquisitions.

Simultaneous acquisition of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) aims to disentangle the description of brain processes by exploiting the advantages of each technique. Most studies in this field focus on exploring the relationships between fMRI signals and the power spectrum at some specific frequency bands (alpha, beta, etc.). On the other hand, brain mapping of EEG signals (e.g., interictal spikes in epileptic patients) usually assumes an haemodynamic response function for a parametric analysis applying the GLM, as a rough approximation. The integration of the information provided by the high spatial resolution of MR images and the high temporal resolution of EEG may be improved by referencing them by transfer functions, which allows the identification of neural driven areas without strong assumptions about haemodynamic response shapes or brain haemodynamic's homogeneity. The difference on sampling rate is the first obstacle for a full integration of EEG and fMRI information. Moreover, a parametric specification of a function representing the commonalities of both signals is not established. In this study, we introduce a new data-driven method for estimating the transfer function from EEG signal to fMRI signal at EEG sampling rate. This approach avoids EEG subsampling to fMRI time resolution and naturally provides a test for EEG predictive power over BOLD signal fluctuations, in a well-established statistical framework. We illustrate this concept in resting state (eyes closed) and visual simultaneous fMRI-EEG experiments. The results point out that it is possible to predict the BOLD fluctuations in occipital cortex by using EEG measurements.