Uses, misuses, new uses and fundamental limitations of magnetic resonance imaging in cognitive science

Duchenne and Becker muscular dystrophy: Cellular mechanisms, image analysis, and computational models: A review

The muscle is the principal tissue that is capable to transform potential energy into kinetic energy. This process is due to the transformation of chemical energy into mechanical energy to enhance the movements and all the daily activities. However, muscular tissues can be affected by some pathologies associated with genetic alterations that affect the expression of proteins. As the muscle is a highly organized structure in which most of the signaling pathways and proteins are related to one another, pathologies may overlap. Duchenne muscular dystrophy (DMD) is one of the most severe muscle pathologies triggering degeneration and muscle necrosis. Several mathematical models have been developed to predict muscle response to different scenarios and pathologies. The aim of this review is to describe DMD and Becker muscular dystrophy in terms of cellular behavior and molecular disorders and to present an overview of the computational models implemented to understand muscle behavior with the aim of improving regenerative therapy.

Mapping the Neural Basis of Neuroeconomics with Functional Magnetic Resonance Imaging: A Narrative Literature Review

Neuroeconomics merges neuroscience, economics, and psychology to investigate the neural basis of decision making. Decision making involves assessing outcomes with subjective value, shaped by emotions and experiences, which are crucial in economic decisions. Functional MRI (fMRI) reveals key areas of the brain, including the ventro-medial prefrontal cortex, that are involved in subjective value representation. Collaborative interdisciplinary efforts are essential for advancing the field of neuroeconomics, with implications for clinical interventions and policy design. This review explores subjective value in neuroeconomics, highlighting brain regions identified through fMRI studies.

Structural underpinnings and long-term effects of resilience in Parkinson's disease

Resilience in neuroscience generally refers to an individual's capacity to counteract the adverse effects of a neuropathological condition. While resilience mechanisms in Alzheimer's disease are well-investigated, knowledge regarding its quantification, neurobiological underpinnings, network adaptations, and long-term effects in Parkinson's disease is limited. Our study involved 151 Parkinson's patients from the Parkinson's Progression Marker Initiative Database with available Magnetic Resonance Imaging, Dopamine Transporter Single-Photon Emission Computed Tomography scans, and clinical information. We used an improved prediction model linking neuropathology to symptom severity to estimate individual resilience levels. Higher resilience levels were associated with a more active lifestyle, increased grey matter volume in motor-associated regions, a distinct structural connectivity network and maintenance of relative motor functioning for up to a decade. Overall, the results indicate that relative maintenance of motor function in Parkinson's patients may be associated with greater neuronal substrate, allowing higher tolerance against neurodegenerative processes through dynamic network restructuring.

Functional Localization of the Human Auditory and Visual Thalamus Using a Thalamic Localizer Functional Magnetic Resonance Imaging Task

Functional magnetic resonance imaging (fMRI) of the auditory and visual sensory systems of the human brain is an active area of investigation in the study of human health and disease. The medial geniculate nucleus (MGN) and lateral geniculate nucleus (LGN) are key thalamic nuclei involved in the processing and relay of auditory and visual information, respectively, and are the subject of blood-oxygen-level-dependent (BOLD) fMRI studies of neural activation and functional connectivity in human participants. However, localization of BOLD fMRI signal originating from neural activity in MGN and LGN remains a technical challenge, due in part to the poor definition of boundaries of these thalamic nuclei in standard T1-weighted and T2-weighted magnetic resonance imaging sequences. Here, we report the development and evaluation of an auditory and visual sensory thalamic localizer (TL) fMRI task that produces participant-specific functionally-defined regions of interest (fROIs) of both MGN and LGN, using 3 Tesla multiband fMRI and a clustered-sparse temporal acquisition sequence, in less than 16 minutes of scan time. We demonstrate the use of MGN and LGN fROIs obtained from the TL fMRI task in standard resting-state functional connectivity (RSFC) fMRI analyses in the same participants. In RSFC analyses, we validated the specificity of MGN and LGN fROIs for signals obtained from primary auditory and visual cortex, respectively, and benchmark their performance against alternative atlas- and segmentation-based localization methods. The TL fMRI task and analysis code (written in Presentation and MATLAB, respectively) have been made freely available to the wider research community.

Abnormalities of white matter network properties in middle-aged and elderly patients with functional constipation

Purpose

To explore white matter network topological properties changes in middle-aged and elderly patients with functional constipation (Functional Constipation, FC) by diffusion tensor imaging (DTI), and to evaluate the correlation between the abnormal changes and clinical data.

Methods

29 FC patients and 31 age- and sex-matched healthy controls (HC) were recruited. Magnetic resonance imaging and clinical data were collected. The white matter network changes in FC patients were analyzed using deterministic fiber tracking methods, graph theory algorithms, and partial correlation analysis with clinical data.

Results

The nodal clustering coefficient and nodal local efficiency of FC patients in the right orbital inferior frontal gyrus, right medial superior frontal gyrus, right rectus muscle, right hippocampus, left paracentral lobule and left temporal pole, and the nodal clustering coefficient in right orbital superior frontal gyrus, left cuneus lobe and right superior occipital gyrus, the nodal local efficiency in the right medial and paracingulate gyrus, right precuneus and right dorsolateral superior frontal gyrus of FC patients are lower than that of HC. The nodal local efficiency and clustering coefficient of FC patients in left hippocampus, left amygdala, right parietal inferior limbic angular gyrus and right angular gyrus, the nodal local efficiency in the right fusiform gyrus, left supplementary motor cortex and the nodal efficiency in the left lateral temporal gyrus and right orbital middle frontal gyrus (ORBmid.R) of FC patients are higher than that of HC. The nodal efficiency of ORBmid.R in FC was positively correlated with the Patient Assessment of Constipation quality of life questionnaire (PAC-QoL).

Conclusion

Middle-aged and elderly FC patients have differences in the nodal level properties in the limbic system, supplementary motor cortex, and default mode network brain regions, and the nodal efficiency of ORBmid.R was positively correlated with the PAC-QoL score, revealing that FC may be related to the abnormal processing of visceral sensorimotor in ORBmid.R and providing potential imaging diagnostic markers and therapeutic targets for middle-aged and elderly FC patients.

Point-of-care detection of fibrosis in liver transplant surgery using near-infrared spectroscopy and machine learning

Introduction

Visual assessment and imaging of the donor liver are inaccurate in predicting fibrosis and remain surrogates for histopathology. We demonstrate that 3-s scans using a handheld near-infrared-spectroscopy (NIRS) instrument can identify and quantify fibrosis in fresh human liver samples.

Methods

We undertook NIRS scans on 107 samples from 27 patients, 88 from 23 patients with liver disease, and 19 from four organ donors.

Results

Liver disease patients had a median immature fibrosis of 40% (interquartile range [IQR] 20-60) and mature fibrosis of 30% (10%-50%) on histopathology. The organ donor livers had a median fibrosis (both mature and immature) of 10% (IQR 5%-15%). Using machine learning, this study detected presence of cirrhosis and METAVIR grade of fibrosis with a classification accuracy of 96.3% and 97.2%, precision of 96.3% and 97.0%, recall of 96.3% and 97.2%, specificity of 95.4% and 98.0% and area under receiver operator curve of 0.977 and 0.999, respectively. Using partial-least square regression machine learning, this study predicted the percentage of both immature (R 2 = 0.842) and mature (R 2 = 0.837) with a low margin of error (root mean square of error of 9.76% and 7.96%, respectively).

Conclusion

This study demonstrates that a point-of-care NIRS instrument can accurately detect, quantify and classify liver fibrosis using machine learning.

Functional changes in neural mechanisms underlying post-traumatic stress disorder in World Trade Center responders

World Trade Center (WTC) responders exposed to traumatic and environmental stressors during rescue and recovery efforts have a high prevalence of chronic WTC-related post-traumatic stress disorder (WTC-PTSD). We investigated neural mechanisms underlying WTC-PTSD by applying eigenvector centrality (EC) metrics and data-driven methods on resting state functional magnetic resonance (fMRI). We identified how EC differences relate to WTC-exposure and behavioral symptoms. We found that connectivity differentiated significantly between WTC-PTSD and non-PTSD responders in nine brain regions, as these differences allowed an effective discrimination of PTSD and non-PTSD responders based solely on analysis of resting state data. Further, we found that WTC exposure duration (months on site) moderates the association between PTSD and EC values in two of the nine brain regions; the right anterior parahippocampal gyrus and the left amygdala (p = 0.010; p = 0.005, respectively, adjusted for multiple comparisons). Within WTC-PTSD, a dimensional measure of symptom severity was positively associated with EC values in the right anterior parahippocampal gyrus and brainstem. Functional neuroimaging can provide effective tools to identify neural correlates of diagnostic and dimensional indicators of PTSD.

Influence of Strenuous Physical Activity and Cardiorespiratory Fitness on Age-Related Differences in Brain Activations During Varieties of Cognitive Control

While there is extensive literature on the beneficial effects of physical activity on age differences in cognitive control, limited research exists on comparing the contributions of strenuous physical activity (sPA) and cardiorespiratory fitness (CRF) to fluctuations in the blood oxygen level-dependent (BOLD) signals during varieties of cognitive control. The current study addresses this gap in knowledge by investigating BOLD signal differences between high-fit and low-fit older adults, determined by their sPA or CRF, during a novel fMRI task with a hybrid block and event-related design that included transient activations (during switching, updating and their combination trials) and sustained activations (during proactive and reactive control blocks). fBOLD signals from older (n = 25) were compared to more functionally efficient younger (n = 15) adults. High-sPA old showed higher task accuracy than Low-sPA old and similar accuracy as young. Whole-brain fMRI analyses identified higher BOLD activations (esp. dlPFC/MFG) in high-fit old during updating and combination trials that were similar to young, suggesting maintenance of BOLD signals in higher fit older adults during working memory updating. Additionally, both High-sPA and High-CRF related compensatory overactivation were observed in left parietal and occipital areas during sustained activations, which were positively correlated with older adults' accuracy. These results suggest that physical fitness is a modifier of age-related changes in BOLD signal modulation elicited in response to increasing cognitive control demands, with higher fitness in old contributing to both compensatory overactivations and maintenance of task-related brain activations during cognitive control, whereas lower fitness contributed to maladaptive overactivations during lower cognitive demands.

Laminar VASO fMRI in focal hand dystonia patients

Focal Hand Dystonia (FHD) is a disabling movement disorder characterized by involuntary movements, cramps and spasms. It is associated with pathological neural microcircuits in the cortical somatosensory system. While invasive preclinical modalities allow researchers to probe specific neural microcircuits of cortical layers and columns, conventional functional magnetic resonance imaging (fMRI) cannot resolve such small neural computational units. In this study, we take advantage of recent developments in ultra-high-field MRI hardware and MR-sequences to capture altered digit representations and laminar processing in FHD patients. We aim to characterize the capability and challenges of layer-specific imaging and analysis tools in resolving laminar and columnar structures in clinical research setups. We scanned N = 4 affected and N = 5 unaffected hemispheres at 7T and found consistent results of altered neural microcircuitry in FHD patients: 1) In affected hemispheres of FHD patients, we found a breakdown of ordered finger representation in the primary somatosensory cortex, as suggested from previous low-resolution fMRI. 2) In affected primary motor cortices of FHD patients, we furthermore found increased fMRI activity in superficial cortico-cortical neural input layers (II/III), compared to relatively weaker activity in the cortico-spinal output layers (Vb/VI). Overall, we show that layer-fMRI acquisition and analysis tools have the potential to address clinically-driven neuroscience research questions about altered computational mechanisms at the spatial scales that were previously only accessible in animal models. We believe that this study paves the way for easier translation of preclinical work into clinical research in focal hand dystonia and beyond.

Do mean values tell the full story? Cardiac cycle and biological sex comparisons in temporally derived neurovascular coupling metrics

Previous reports have noted cerebrovascular regulation differs across the cardiac cycle, with greater regulation occurring within systole. However, this methodological notion has not been meticulously scrutinized during temporally deduced neurovascular coupling (NVC) metrics with additional respect to biological sex. Analyses of 111 healthy individuals (40 females/71 males) were performed where participants engaged in the "Where's Waldo?" paradigm. All NVC parameters were quantified in the posterior and middle cerebral arteries at 310 unique timepoints. Several individuals completed repeat testing which enabled for between-day (3 timepoints) and within-day (7 timepoints) reliability comparisons in 17 and 11 individuals, respectively. One-way analysis of variance compared NVC metrics between diastole, mean, and systole values, as well as differences between biological sexes. Greater absolute cerebral blood velocity (CBv; baseline and peak) and total activation (area under the curve) were noted within systole for both posterior cerebral artery (PCA; P < 0.001) and middle cerebral artery (MCA; P < 0.001) values; however, the relative percent increase in CBv was greater within diastole (P < 0.001). Females had an elevated diastolic and mean CBv and a greater diastolic cerebrovascular conductance (P < 0.050). No sex differences were present for systolic CBv measures and within parameters quantifying the NVC response (area under the curve/relative CBv increase) across the cardiac cycle (P > 0.072). Future investigations seeking to differentiate cerebral regulatory mechanisms between clinical populations may benefit by performing their analyses across the cardiac cycle, as certain pathogenesis may affect one aspect of the cardiac cycle independently. Minimal differences were noted between females and males for metrics characterizing the NVC response across the cardiac cycle.NEW & NOTEWORTHY Neurovascular coupling (NVC) studies commonly assess the mean cerebral hemodynamic response with little consideration for diastole, systole, and biological sex. Greater total activation expressed as the area under the curve was seen within systole compared with mean and diastole. Resting cerebral blood velocity sex differences were more prevalent during diastole when the cerebrovasculature was pressure-passive. Future studies should assess the NVC response across the cardiac cycle as it may help delineate the underlying pathophysiology of various clinical populations.

Baseline oxygen consumption decreases with cortical depth

The cerebral cortex is organized in cortical layers that differ in their cellular density, composition, and wiring. Cortical laminar architecture is also readily revealed by staining for cytochrome oxidase-the last enzyme in the respiratory electron transport chain located in the inner mitochondrial membrane. It has been hypothesized that a high-density band of cytochrome oxidase in cortical layer IV reflects higher oxygen consumption under baseline (unstimulated) conditions. Here, we tested the above hypothesis using direct measurements of the partial pressure of O2 (pO2) in cortical tissue by means of 2-photon phosphorescence lifetime microscopy (2PLM). We revisited our previously developed method for extraction of the cerebral metabolic rate of O2 (CMRO2) based on 2-photon pO2 measurements around diving arterioles and applied this method to estimate baseline CMRO2 in awake mice across cortical layers. To our surprise, our results revealed a decrease in baseline CMRO2 from layer I to layer IV. This decrease of CMRO2 with cortical depth was paralleled by an increase in tissue oxygenation. Higher baseline oxygenation and cytochrome density in layer IV may serve as an O2 reserve during surges of neuronal activity or certain metabolically active brain states rather than reflecting baseline energy needs. Our study provides to our knowledge the first quantification of microscopically resolved CMRO2 across cortical layers as a step towards better understanding of brain energy metabolism.

A review of functional brain differences predicting relapse in substance use disorder: Actionable targets for new methods of noninvasive brain stimulation

Neuroimaging studies have identified a variety of brain regions whose activity predicts substance use (i.e., relapse) in patients with substance use disorder (SUD), suggesting that malfunctioning brain networks may exacerbate relapse. However, this knowledge has not yet led to a marked improvement in treatment outcomes. Noninvasive brain stimulation (NIBS) has shown some potential for treating SUDs, and a new generation of NIBS technologies offers the possibility of selectively altering activity in both superficial and deep brain structures implicated in SUDs. The goal of the current review was to identify deeper brain structures involved in relapse to SUD and give an account of innovative methods of NIBS that might be used to target them. Included studies measured fMRI in currently abstinent SUD patients and tracked treatment outcomes, and fMRI results were organized with the framework of the Addictions Neuroclinical Assessment (ANA). Four brain structures were consistently implicated: the anterior and posterior cingulate cortices, ventral striatum and insula. These four deeper brain structures may be appropriate future targets for the treatment of SUD using these innovative NIBS technologies.

Gastrointestinal and Autonomic Symptoms—How to Improve the Diagnostic Process in Panayiotopoulos Syndrome?

One of the most common epileptic disorders in the pediatric population is Panayiotopoulos syndrome. Clinical manifestations of this idiopathic illness include predominantly autonomic symptoms and dysfunction of the cardiorespiratory system. Another feature constitutes prolonged seizures that usually occur at sleep. It is crucial to differentiate the aforementioned disease from other forms of epilepsy, especially occipital and structural epilepsy and non-epileptic disorders. The diagnostic process is based on medical history, clinical examination, neuroimaging and electroencephalography—though results of the latter may be unspecific. Patients with Panayiotopoulos syndrome (PS) do not usually require treatment, as the course of the disease is, in most cases, mild, and the prognosis is good. The purpose of this review is to underline the role of central autonomic network dysfunction in the development of Panayiotopoulos syndrome, as well as the possibility of using functional imaging techniques, especially functional magnetic resonance imaging (fMRI), in the diagnostic process. These methods could be crucial for understanding the pathogenesis of PS. More data arerequired to create algorithms that will be able to predict the exposure to various complications of PS. It also concerns the importance of electroencephalography (EEG) as a tool to distinguish Panayiotopoulos syndrome from other childhood epileptic syndromes and non-epileptic disorders.

A plug-and-play, lightweight, single-axis gradient insert design for increasing spatiotemporal resolution in echo planar imaging-based brain imaging

The goal of this study was to introduce and evaluate the performance of a lightweight, high-performance, single-axis (z-axis) gradient insert design primarily intended for high-resolution functional magnetic resonance imaging, and aimed at providing both ease of use and a boost in spatiotemporal resolution. The optimal winding positions of the coil were obtained using a genetic algorithm with a cost function that balanced gradient performance (minimum 0.30 mT/m/A) and field linearity (≥16 cm linear region). These parameters were verified using field distribution measurements by B₀ -mapping. The correction of geometrical distortions was performed using theoretical field distribution of the coil. Simulations and measurements were performed to investigate the echo planar imaging echo-spacing reduction due to the improved gradient performance. The resulting coil featured a 16-cm linear region, a weight of 45 kg, an installation time of 15 min, and a maximum gradient strength and slew rate of 200 mT/m and 1300 T/m/s, respectively, when paired with a commercially available gradient amplifier (940 V/630 A). The field distribution measurements matched the theoretically expected field. By utilizing the theoretical field distribution, geometrical distortions were corrected to within 6% of the whole-body gradient reference image in the target region. Compared with a whole-body gradient set, a maximum reduction in echo-spacing of a factor of 2.3 was found, translating to a 344 μs echo-spacing, for a field of view of 192 mm, a receiver bandwidth of 920 kHz and a gradient amplitude of 112 mT/m. We present a lightweight, single-axis gradient insert design that can provide high gradient performance and an increase in spatiotemporal resolution with correctable geometrical distortions while also offering a short installation time of less than 15 min and minimal system modifications.

Assessing the effective connectivity of premotor areas during real vs imagined grasping: a DCM-PEB approach

The parieto-frontal circuit underlying grasping, which requires the serial involvement of the anterior intraparietal area (aIPs) and the ventral premotor cortex (PMv), has been recently extended enlightening the role of the dorsal premotor cortex (PMd). The supplementary motor area (SMA) has been also suggested to encode grip force for grasping actions; furthermore, both PMd and SMA are known to play a crucial role in motor imagery. Here, we aimed at assessing the dynamic couplings between left aIPs, PMv, PMd, SMA and primary motor cortex (M1) by comparing executed and imagined right-hand grasping, using Dynamic Causal Modelling (DCM) and Parametrical Empirical Bayes (PEB) analyses. 24 subjects underwent an fMRI exam (3T) during which they were asked to perform or imagine a grasping movement visually cued by photographs of commonly used objects. We tested whether the two conditions a) exert a modulatory effect on both forward and feedback couplings among our areas of interest, and b) differ in terms of strength and sign of these parameters. Results of the real condition confirmed the serial involvement of aIPs, PMv and M1. PMv also exerted a positive influence on PMd and SMA, but received an inhibitory feedback only from PMd. Our results suggest that a general motor program for grasping is planned by the aIPs-PMv circuit; then, PMd and SMA encode high-level features of the movement. During imagery, the connection strength from aIPs to PMv was weaker and the information flow stopped in PMv; thus, a less complex motor program was planned. Moreover, results suggest that SMA and PMd cooperate to prevent motor execution. In conclusion, the comparison between execution and imagery reveals that during grasping premotor areas dynamically interplay in different ways, depending on task demands.

Higher and deeper: Bringing layer fMRI to association cortex

Recent advances in fMRI have enabled non-invasive measurements of brain function in awake, behaving humans at unprecedented spatial resolutions, allowing us to separate activity in distinct cortical layers. While most layer fMRI studies to date have focused on primary cortices, we argue that the next big steps forward in our understanding of cognition will come from expanding this technology into higher-order association cortex, to characterize depth-dependent activity during increasingly sophisticated mental processes. We outline phenomena and theories ripe for investigation with layer fMRI, including perception and imagery, selective attention, and predictive coding. We discuss practical and theoretical challenges to cognitive applications of layer fMRI, including localizing regions of interest in the face of substantial anatomical heterogeneity across individuals, designing appropriate task paradigms within the confines of acquisition parameters, and generating hypotheses for higher-order brain regions where the laminar circuitry is less well understood. We consider how applying layer fMRI in association cortex may help inform computational models of brain function as well as shed light on consciousness and mental illness, and issue a call to arms to our fellow methodologists and neuroscientists to bring layer fMRI to this next frontier.

7 Tesla MRI Followed by Histological 3D Reconstructions in Whole-Brain Specimens

Post mortem magnetic resonance imaging (MRI) studies on the human brain are of great interest for the validation of in vivo MRI. It facilitates a link between functional and anatomical information available from MRI in vivo and neuroanatomical knowledge available from histology/immunocytochemistry. However, linking in vivo and post mortem MRI to microscopy techniques poses substantial challenges. Fixation artifacts and tissue deformation of extracted brains, as well as co registration of 2D histology to 3D MRI volumes complicate direct comparison between modalities. Moreover, post mortem brain tissue does not have the same physical properties as in vivo tissue, and therefore MRI approaches need to be adjusted accordingly. Here, we present a pipeline in which whole-brain human post mortem in situ MRI is combined with subsequent tissue processing of the whole human brain, providing a 3-dimensional reconstruction via blockface imaging. To this end, we adapted tissue processing procedures to allow both post mortem MRI and subsequent histological and immunocytochemical processing. For MRI, tissue was packed in a susceptibility matched solution, tailored to fit the dimensions of the MRI coil. Additionally, MRI sequence parameters were adjusted to accommodate T1 and T2^∗ shortening, and scan time was extended, thereby benefiting the signal-to-noise-ratio that can be achieved using extensive averaging without motion artifacts. After MRI, the brain was extracted from the skull and subsequently cut while performing optimized blockface imaging, thereby allowing three-dimensional reconstructions. Tissues were processed for Nissl and silver staining, and co-registered with the blockface images. The combination of these techniques allows direct comparisons across modalities.

High spatiotemporal vessel-specific hemodynamic mapping with multi-echo single-vessel fMRI

High-resolution fMRI enables noninvasive mapping of the hemodynamic responses from individual penetrating vessels in animal brains. Here, a 2D multi-echo single-vessel fMRI (MESV-fMRI) method has been developed to map the fMRI signal from arterioles and venules with a 100 ms sampling rate at multiple echo times (TE, 3-30 ms) and short acquisition windows (<1 ms). The T₂*-weighted signal shows the increased extravascular effect on venule voxels as a function of TE. In contrast, the arteriole voxels show an increased fMRI signal with earlier onset than venules voxels at the short TE (3 ms) with increased blood inflow and volume effects. MESV-fMRI enables vessel-specific T2* mapping and presents T2*-based fMRI time courses with higher contrast-to-noise ratios (CNRs) than the T2*-weighted fMRI signal at a given TE. The vessel-specific T2* mapping also allows semi-quantitative estimation of the oxygen saturation levels (Y) and their changes (ΔY) at a given blood volume fraction upon neuronal activation. The MESV-fMRI method enables vessel-specific T2* measurements with high spatiotemporal resolution for better modeling of the fMRI signal based on the hemodynamic parameters.

Establishing an acquisition and processing protocol for resting state networks with a 1.5 T scanner: A case series in a middle-income country

OBJECTIVE

The aim of this study was to characterize the capability of detection of the resting state networks (RSNs) with functional magnetic resonance imaging (fMRI) in healthy subjects using a 1.5T scanner in a middle-income country.

MATERIALS AND METHODS

Ten subjects underwent a complete blood-oxygen-level dependent imaging (BOLD) acquisition on a 1.5T scanner. For the imaging analysis, we used the spatial independent component analysis (sICA). We designed a computer tool for 1.5 T (or above) scanners for imaging processing. We used it to separate and delineate the different components of the RSNs of the BOLD signal. The sICA was also used to differentiate the RSNs from noise artifact generated by breathing and cardiac cycles.

RESULTS

For each subject, 20 independent components (IC) were computed from the sICA (a total of 200 ICs). From these ICs, a spatial pattern consistent with RSNs was identified in 161 (80.5%). From the 161, 131 (65.5%) were fit for study. The networks that were found in all subjects were: the default mode network, the right executive control network, the medial visual network, and the cerebellar network. In 90% of the subjects, the left executive control network and the sensory/motor network were observed. The occipital visual network was present in 80% of the subjects. In 39 (19.5%) of the images, no any neural network was identified.

CONCLUSIONS

Reproduction and differentiation of the most representative RSNs was achieved using a 1.5T scanner acquisitions and sICA processing of BOLD imaging in healthy subjects.

Investigating resting brain perfusion abnormalities and disease target-engagement by intranasal oxytocin in women with bulimia nervosa and binge-eating disorder and healthy controls

Advances in the treatment of bulimia nervosa and binge-eating disorder (BN/BED) have been marred by our limited understanding of the underpinning neurobiology. Here we measured regional cerebral blood flow (rCBF) to map resting perfusion abnormalities in women with BN/BED compared with healthy controls and investigate whether intranasal oxytocin (OT), proposed as a potential treatment, can restore perfusion in disorder-related brain circuits. Twenty-four women with BN/BED and 23 healthy women participated in a randomized, double-blind, crossover, placebo-controlled study. We used arterial spin labelling MRI to measure rCBF and the effects of an acute dose of intranasal OT (40 IU) or placebo over 18-26 min post dosing, as we have previously shown robust OT-induced changes in resting rCBF in men in a similar time-window (15-36 min post dosing). We tested for effects of treatment, diagnosis and their interaction on extracted rCBF values in anatomical regions-of-interest previously implicated in BN/BED by other neuroimaging modalities, and conducted exploratory whole-brain analyses to investigate previously unidentified brain regions. We demonstrated that women with BN/BED presented increased resting rCBF in the medial prefrontal and orbitofrontal cortices, anterior cingulate gyrus, posterior insula and middle/inferior temporal gyri bilaterally. Hyperperfusion in these areas specifically correlated with eating symptoms severity in patients. Our data did not support a normalizing effect of intranasal OT on perfusion abnormalities in these patients, at least for the specific dose (40 IU) and post-dosing interval (18-26 min) examined. Our findings enhance our understanding of resting brain abnormalities in BN/BED and identify resting rCBF as a non-invasive potential biomarker for disease-related changes and treatment monitoring. They also highlight the need for a comprehensive investigation of intranasal OT pharmacodynamics in women before we can fully ascertain its therapeutic value in disorders affecting predominantly this gender, such as BN/BED.

Multivariate pattern analysis of fMRI data for imaginary and real colours in grapheme-colour synaesthesia

Grapheme-colour synaesthesia is a subjective phenomenon related to perception and imagination, in which some people involuntarily but systematically associate specific, idiosyncratic colours to achromatic letters or digits. Its investigation is relevant to unravel the neural correlates of colour perception in isolation from low-level neural processing of spectral components, as well as the neural correlates of imagination by being able to reliably trigger imaginary colour experiences. However, functional MRI studies using univariate analyses failed to provide univocal evidence of the activation of the "colour network" by synaesthesia. Applying multivariate (multivoxel) pattern analysis (MVPA) on 20 synaesthetes and 20 control participants, we tested whether the neural processing of real colours (concentric rings) and synaesthetic colours (black graphemes) shared patterns of activations. Region of interest analyses in retinotopically and anatomically defined visual areas revealed neither evidence of shared circuits for real and synaesthetic colour processing, nor processing difference between synaesthetes and controls. We also found no correlation with individual experiences, characterised by measuring the strength of synaesthetic associations. The whole brain searchlight analysis led to similar results. We conclude that revealing the neural coding of the synaesthetic experience of colours is a hard task which requires the improvement of our current methodology: for example involving more individuals and achieving higher MR signal to noise ratio and spatial resolution. So far, we have not found any evidence of the involvement of the cortical colour network in the subjective experience of synaesthetic colours.

Layer-Specific Contributions to Imagined and Executed Hand Movements in Human Primary Motor Cortex

The human ability to imagine motor actions without executing them (i.e., motor imagery) is crucial to a number of cognitive functions, including motor planning and learning, and has been shown to improve response times and accuracy of subsequent motor actions [1, 2]. Although these behavioral findings suggest the possibility that imagined movements directly influence primary motor cortex (M1), how this might occur remains unknown [3]. Here, we use a non-blood-oxygen-level-dependent (BOLD) method for collecting fMRI data, called vascular space occupancy (VASO) [4, 5], to measure neural activations across cortical laminae in M1 while participants either tapped their thumb and forefinger together or simply imagined doing so. We report that, whereas executed movements (i.e., finger tapping) evoked neural responses in both the superficial layers of M1 that receive cortical input and the deep layers of M1 that send output to the spinal cord to support movement, imagined movements evoked responses in superficial cortical layers only. Furthermore, we found that finger tapping preceded by both imagined and executed movements showed a reduced response in the superficial layers (repetition suppression) coupled with a heightened response in the deep layers (repetition enhancement). Taken together, our results provide evidence for a mechanism whereby imagined movements can directly affect motor performance and might explain how neural repetition effects lead to improvements in behavior (e.g., repetition priming).

An EEG-fNIRS hybridization technique in the four-class classification of alzheimer's disease

BACKGROUND

Alzheimer's disease (AD) is projected to become one of the most expensive diseases in modern history, and yet diagnostic uncertainties exist that can only be confirmed by postmortem brain examination. Machine Learning (ML) algorithms have been proposed as a feasible alternative to the diagnosis of several neurological diseases and disorders, such as AD. An ideal ML-derived diagnosis should be inexpensive and noninvasive while retaining the accuracy and versatility that make ML techniques desirable for medical applications.

NEW METHODS

Two portable modalities, Electroencephalography (EEG) and functional Near-Infrared Spectroscopy (fNIRS) have been widely employed in constructing hybrid classification models to compensate for each other's weaknesses. In this study, we present a hybrid EEG-fNIRS model for classifying four classes of subjects including one healthy control (HC) group, one mild cognitive impairment (MCI) group, and, two AD patient groups. A concurrent EEG-fNIRS setup was used to record data from 29 subjects during a random digit encoding-retrieval task. EEG-derived and fNIRS-derived features were sorted using a Pearson correlation coefficient-based feature selection (PCCFS) strategy and then fed into a linear discriminant analysis (LDA) classifier to evaluate their performance.

RESULTS

The hybrid EEG-fNIRS feature set was able to achieve a higher accuracy (79.31 %) by integrating their complementary properties, compared to using EEG (65.52 %) or fNIRS alone (58.62 %). Moreover, our results indicate that the right prefrontal and left parietal regions are associated with the progression of AD.

COMPARISON WITH EXISTING METHODS

Our hybrid and portable system provided enhanced classification performance in multi-class classification of AD population.

CONCLUSIONS

These findings suggest that hybrid EEG-fNIRS systems are a promising tool that may enhance the AD diagnosis and assessment process.

Educational fMRI: From the Lab to the Classroom

Functional MRI (fMRI) findings hold many potential applications for education, and yet, the translation of fMRI findings to education has not flowed. Here, we address the types of fMRI that could better support applications of neuroscience to the classroom. This 'educational fMRI' comprises eight main challenges: (1) collecting artifact-free fMRI data in school-aged participants and in vulnerable young populations, (2) investigating heterogenous cohorts with wide variability in learning abilities and disabilities, (3) studying the brain under natural and ecological conditions, given that many practical topics of interest for education can be addressed only in ecological contexts, (4) depicting complex age-dependent associations of brain and behaviour with multi-modal imaging, (5) assessing changes in brain function related to developmental trajectories and instructional intervention with longitudinal designs, (6) providing system-level mechanistic explanations of brain function, so that useful individualized predictions about learning can be generated, (7) reporting negative findings, so that resources are not wasted on developing ineffective interventions, and (8) sharing data and creating large-scale longitudinal data repositories to ensure transparency and reproducibility of fMRI findings for education. These issues are of paramount importance to the development of optimal fMRI practices for educational applications.

Layer-By-Layer: The Case for 3D Bioprinting Neurons to Create Patient-Specific Epilepsy Models

The ability to create three-dimensional (3D) models of brain tissue from patient-derived cells, would open new possibilities in studying the neuropathology of disorders such as epilepsy and schizophrenia. While organoid culture has provided impressive examples of patient-specific models, the generation of organised 3D structures remains a challenge. 3D bioprinting is a rapidly developing technology where living cells, encapsulated in suitable bioink matrices, are printed to form 3D structures. 3D bioprinting may provide the capability to organise neuronal populations in 3D, through layer-by-layer deposition, and thereby recapitulate the complexity of neural tissue. However, printing neuron cells raises particular challenges since the biomaterial environment must be of appropriate softness to allow for the neurite extension, properties which are anathema to building self-supporting 3D structures. Here, we review the topic of 3D bioprinting of neurons, including critical discussions of hardware and bio-ink formulation requirements.

Gradient theories of brain activation: A novel application to studying the parental brain

PURPOSE OF REVIEW

Parental brain research primarily employs general-linear-model-based (GLM-based) analyses to assess blood-oxygenation-level-dependent responses to infant auditory and visual cues, reporting common responses in shared cortical and subcortical structures. However, this approach does not reveal intermixed neural substrates related to different sensory modalities. We consider this notion in studying the parental brain.

RECENT FINDINGS

Spatial independent component analysis (sICA) has been used to separate mixed source signals from overlapping functional networks. We explore relative differences between GLM-based analysis and sICA as applied to an fMRI dataset acquired from women while they listened to infant cries or viewed infant sad faces.

SUMMARY

There is growing appreciation for the value of moving beyond GLM-based analyses to consider brain functional organization as continuous, distributive, and overlapping gradients of neural substrates related to different sensory modalities. Preliminary findings suggest sICA can be applied to the study of the parental brain.

Advancing Tobacco Product Warning Labels Research Methods and Theory: A Summary of a Grantee Meeting Held by the US National Cancer Institute

BACKGROUND

The World Health Organization's Framework Convention on Tobacco Control recommends prominent pictorial health warnings on tobacco products. To advance research methods, theory and understanding of how tobacco product warning labels (TPWLs) work, the US National Cancer Institute convened a grantee meeting. Our article describes the key insights that emerged from the meeting, situated within the context of the scientific literature.

RESULTS & RECOMMENDATIONS

First, presentations confirmed that large, pictorial TPWLs motivate people to try to quit and encourage smoking cessation. Second, pictorial TPWLs increase attention, knowledge, negative affect, and thinking about the warning. Third, TPWL studies have primarily used brief-exposure laboratory studies and observational studies of sustained exposure through national policy implementation, with a few randomized trials involving several weeks of exposure-with generally consistent results found across study designs. Fourth, novel assessment methods include brain imaging, eye tracking and "best-worst" discrete choice experiments. To make TPWL even more effective, research is needed to confirm the mechanisms of their influence, their impact across vulnerable populations, and their effect on social media posts about tobacco products. Research is also needed on the effect of trial design choices, the predictive validity of new measurement approaches, and warning labels for non-cigarette tobacco products.

IMPLICATIONS

To improve scientific understanding of TPWL effects, this grantee meeting summary describes emerging research methods, theory and study results. Directions for future research include examination of the mechanisms of how warning labels work across diverse tobacco products and across different populations and contexts.

Myelin and Modeling: Bootstrapping Cortical Microcircuits

Histological studies of myelin-stained sectioned cadaver brain and in vivo myelin-weighted magnetic resonance imaging (MRI) show that the cerebral cortex is organized into cortical areas with generally well-defined boundaries, which have consistent internal patterns of myelination. The process of myelination is largely driven by neural experience, in which the axonal passage of action potentials stimulates neighboring oligodendrocytes to perform their task. This bootstrapping process, such that the traffic of action potentials facilitates increased traffic, suggests the hypothesis that the specific pattern of myelination (myeloarchitecture) in each cortical area reveals the principal cortical microcircuits required for the function of that area. If this idea is correct, the observable sequential maturation of specific brain areas can provide evidence for models of the stages of cognitive development.

The uncertain brain: A co-ordinate based meta-analysis of the neural signatures supporting uncertainty during different contexts

Uncertainty is often inevitable in everyday life and can be both stressful and exciting. Given its relevance to psychopathology and wellbeing, recent research has begun to address the brain basis of uncertainty. In the current review we examined whether there are discrete and shared neural signatures for different uncertain contexts. From the literature we identified three broad categories of uncertainty currently empirically studied using functional MRI (fMRI): basic threat and reward uncertainty, decision-making under uncertainty, and associative learning under uncertainty. We examined the neural basis of each category by using a coordinate based meta-analysis, where brain activation foci from previously published fMRI experiments were drawn together (1998-2017; 87 studies). The analyses revealed shared and discrete patterns of neural activation for uncertainty, such as the insula and amygdala, depending on the category. Such findings will have relevance for researchers attempting to conceptualise uncertainty, as well as clinical researchers examining the neural basis of uncertainty in relation to psychopathology.

Amyloid beta-positive subjects exhibit longitudinal network-specific reductions in spontaneous brain activity

Amyloid beta (Aβ) deposition and cognitive decline are key features of Alzheimer's disease. The relationship between Aβ status and changes in neuronal function over time, however, remains unclear. We evaluated the effect of baseline Aβ status on reference region spontaneous brain activity (SBA-rr) using resting-state functional magnetic resonance imaging and fluorodeoxyglucose positron emission tomography in patients with mild cognitive impairment. Patients (N = 62, [43 Aβ-positive]) from the Alzheimer's Disease Neuroimaging Initiative were divided into Aβ-positive and Aβ-negative groups via prespecified cerebrospinal fluid Aβ42 or 18F-florbetapir positron emission tomography standardized uptake value ratio cutoffs measured at baseline. We analyzed interaction of biomarker-confirmed Aβ status with SBA-rr change over a 2-year period using mixed-effects modeling. SBA-rr differences between Aβ-positive and Aβ-negative subjects increased significantly over time within subsystems of the default and visual networks. Changes exhibit an interaction with memory performance over time but were independent of glucose metabolism. Results reinforce the value of resting-state functional magnetic resonance imaging in evaluating Alzheimer''s disease progression and suggest spontaneous neuronal activity changes are concomitant with cognitive decline.

Addressing challenges of high spatial resolution UHF fMRI for group analysis of higher-order cognitive tasks: An inter-sensory task directing attention between visual and somatosensory domains

Functional MRI at ultra‐high field (UHF, ≥7 T) provides significant increases in BOLD contrast‐to‐noise ratio (CNR) compared with conventional field strength (3 T), and has been exploited for reduced field‐of‐view, high spatial resolution mapping of primary sensory areas. Applying these high spatial resolution methods to investigate whole brain functional responses to higher‐order cognitive tasks leads to a number of challenges, in particular how to perform robust group‐level statistical analyses. This study addresses these challenges using an inter‐sensory cognitive task which modulates top‐down attention at graded levels between the visual and somatosensory domains. At the individual level, highly focal functional activation to the task and task difficulty (modulated by attention levels) were detectable due to the high CNR at UHF. However, to assess group level effects, both anatomical and functional variability must be considered during analysis. We demonstrate the importance of surface over volume normalisation and the requirement of no spatial smoothing when assessing highly focal activity. Using novel group analysis on anatomically parcellated brain regions, we show that in higher cognitive areas (parietal and dorsal‐lateral‐prefrontal cortex) fMRI responses to graded attention levels were modulated quadratically, whilst in visual cortex and VIP, responses were modulated linearly. These group fMRI responses were not seen clearly using conventional second‐level GLM analyses, illustrating the limitations of a conventional approach when investigating such focal responses in higher cognitive regions which are more anatomically variable. The approaches demonstrated here complement other advanced analysis methods such as multivariate pattern analysis, allowing UHF to be fully exploited in cognitive neuroscience.

LISA improves statistical analysis for fMRI

One of the principal goals in functional magnetic resonance imaging (fMRI) is the detection of local activation in the human brain. However, lack of statistical power and inflated false positive rates have recently been identified as major problems in this regard. Here, we propose a non-parametric and threshold-free framework called LISA to address this demand. It uses a non-linear filter for incorporating spatial context without sacrificing spatial precision. Multiple comparison correction is achieved by controlling the false discovery rate in the filtered maps. Compared to widely used other methods, it shows a boost in statistical power and allows to find small activation areas that have previously evaded detection. The spatial sensitivity of LISA makes it especially suitable for the analysis of high-resolution fMRI data acquired at ultrahigh field (≥7 Tesla).

Network Neuroscience and Personality

Personality and individual differences originate from the brain. Despite major advances in the affective and cognitive neurosciences, however, it is still not well understood how personality and single personality traits are represented within the brain. Most research on brain-personality correlates has focused either on morphological aspects of the brain such as increases or decreases in local gray matter volume, or has investigated how personality traits can account for individual differences in activation differences in various tasks. Here, we propose that personality neuroscience can be advanced by adding a network perspective on brain structure and function, an endeavor that we label personality network neuroscience. With the rise of resting-state functional magnetic resonance imaging (MRI), the establishment of connectomics as a theoretical framework for structural and functional connectivity modeling, and recent advancements in the application of mathematical graph theory to brain connectivity data, several new tools and techniques are readily available to be applied in personality neuroscience. The present contribution introduces these concepts, reviews recent progress in their application to the study of individual differences, and explores their potential to advance our understanding of the neural implementation of personality. Trait theorists have long argued that personality traits are biophysical entities that are not mere abstractions of and metaphors for human behavior. Traits are thought to actually exist in the brain, presumably in the form of conceptual nervous systems. A conceptual nervous system refers to the attempt to describe parts of the central nervous system in functional terms with relevance to psychology and behavior. We contend that personality network neuroscience can characterize these conceptual nervous systems on a functional and anatomical level and has the potential do link dispositional neural correlates to actual behavior.

Microstructural parcellation of the human brain

The human cerebral cortex is composed of a mosaic of areas thought to subserve different functions. The parcellation of the cortex into areas has a long history and has been carried out using different combinations of structural, connectional, receptotopic, and functional properties. Here we give a brief overview of the history of cortical parcellation, and explore different microstructural properties and analysis techniques that can be used to define the borders between different regions. We show that accounting for the 3D geometry of the highly folded human cortex is especially critical for accurate parcellation. We close with some thoughts on future directions and best practices for combining modalities.

Inter-subject Registration of Functional Images: Do We Need Anatomical Images?

In Echo-Planar Imaging (EPI)-based Magnetic Resonance Imaging (MRI), inter-subject registration typically uses the subject's T1-weighted (T1w) anatomical image to learn deformations of the subject's brain onto a template. The estimated deformation fields are then applied to the subject's EPI scans (functional or diffusion-weighted images) to warp the latter to a template space. Historically, such indirect T1w-based registration was motivated by the lack of clear anatomical details in low-resolution EPI images: a direct registration of the EPI scans to template space would be futile. A central prerequisite in such indirect methods is that the anatomical (aka the T1w) image of each subject is well aligned with their EPI images via rigid coregistration. We provide experimental evidence that things have changed: nowadays, there is a decent amount of anatomical contrast in high-resolution EPI data. That notwithstanding, EPI distortions due to B0 inhomogeneities cannot be fully corrected. Residual uncorrected distortions induce non-rigid deformations between the EPI scans and the same subject's anatomical scan. In this manuscript, we contribute a computationally cheap pipeline that leverages the high spatial resolution of modern EPI scans for direct inter-subject matching. Our pipeline is direct and does not rely on the T1w scan to estimate the inter-subject deformation. Results on a large dataset show that this new pipeline outperforms the classical indirect T1w-based registration scheme, across a variety of post-registration quality-assessment metrics including: Normalized Mutual Information, relative variance (variance-to-mean ratio), and to a lesser extent, improved peaks of group-level General Linear Model (GLM) activation maps.

Quantitative MRI provides markers of intra-, inter-regional, and age-related differences in young adult cortical microstructure

Measuring the structural composition of the cortex is critical to understanding typical development, yet few investigations in humans have charted markers in vivo that are sensitive to tissue microstructural attributes. Here, we used a well-validated quantitative MR protocol to measure four parameters (R₁, MT, R₂*, PD*) that differ in their sensitivity to facets of the tissue microstructural environment (R₁, MT: myelin, macromolecular content; R₂*: myelin, paramagnetic ions, i.e., iron; PD*: free water content). Mapping these parameters across cortical regions in a young adult cohort (18–39 years, N = 93) revealed expected patterns of increased macromolecular content as well as reduced tissue water content in primary and primary adjacent cortical regions. Mapping across cortical depth within regions showed decreased expression of myelin and related processes – but increased tissue water content – when progressing from the grey/white to the grey/pial boundary, in all regions. Charting developmental change in cortical microstructure cross-sectionally, we found that parameters with sensitivity to tissue myelin (R₁ & MT) showed linear increases with age across frontal and parietal cortex (change 0.5–1.0% per year). Overlap of robust age effects for both parameters emerged in left inferior frontal, right parietal and bilateral pre-central regions. Our findings afford an improved understanding of ontogeny in early adulthood and offer normative quantitative MR data for inter- and intra-cortical composition, which may be used as benchmarks in further studies.

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We mapped multi-parameter maps (MPMs) across and within cortical regions.

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We charted age effects (ages 18–39) on myelin and related processes.

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MPMs sensitive to myelin (R₁, MT) showed elevated values in primary areas over most cortical depths.

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R₂* map foci tended to overlap MPMs sensitive to myelin (R₁, MT).

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R₁ and MT increased with age (0.5–1.0% per year) at mid-depth in frontal and parietal cortex.

Interpreting BOLD: towards a dialogue between cognitive and cellular neuroscience

Cognitive neuroscience depends on the use of blood oxygenation level-dependent (BOLD) functional magnetic resonance imaging (fMRI) to probe brain function. Although commonly used as a surrogate measure of neuronal activity, BOLD signals actually reflect changes in brain blood oxygenation. Understanding the mechanisms linking neuronal activity to vascular perfusion is, therefore, critical in interpreting BOLD. Advances in cellular neuroscience demonstrating differences in this neurovascular relationship in different brain regions, conditions or pathologies are often not accounted for when interpreting BOLD. Meanwhile, within cognitive neuroscience, the increasing use of high magnetic field strengths and the development of model-based tasks and analyses have broadened the capability of BOLD signals to inform us about the underlying neuronal activity, but these methods are less well understood by cellular neuroscientists. In 2016, a Royal Society Theo Murphy Meeting brought scientists from the two communities together to discuss these issues. Here, we consolidate the main conclusions arising from that meeting. We discuss areas of consensus about what BOLD fMRI can tell us about underlying neuronal activity, and how advanced modelling techniques have improved our ability to use and interpret BOLD. We also highlight areas of controversy in understanding BOLD and suggest research directions required to resolve these issues.This article is part of the themed issue 'Interpreting BOLD: a dialogue between cognitive and cellular neuroscience'.

Understanding the neurovascular unit at multiple scales: Advantages and limitations of multi-photon and functional ultrasound imaging

Developing efficient brain imaging technologies by combining a high spatiotemporal resolution and a large penetration depth is a key step for better understanding the neurovascular interface that emerges as a main pathway to neurodegeneration in many pathologies such as dementia. This review focuses on the advances in two complementary techniques: multi-photon laser scanning microscopy (MPLSM) and functional ultrasound imaging (fUSi). MPLSM has become the gold standard for in vivo imaging of cellular dynamics and morphology, together with cerebral blood flow. fUSi is an innovative imaging modality based on Doppler ultrasound, capable of recording vascular brain activity over large scales (i.e., tens of cubic millimeters) at unprecedented spatial and temporal resolution for such volumes (up to 10μm pixel size at 10kHz). By merging these two technologies, researchers may have access to a more detailed view of the various processes taking place at the neurovascular interface. MPLSM and fUSi are also good candidates for addressing the major challenge of real-time delivery, monitoring, and in vivo evaluation of drugs in neuronal tissue.

Vascular density and distribution in neocortex

An amazingly wide range of complex behavior emerges from the cerebral cortex. Much of the information processing that leads to these behaviors is performed in neocortical circuits that span throughout the six layers of the cortex. Maintaining this circuit activity requires substantial quantities of oxygen and energy substrates, which are delivered by the complex yet well-organized and tightly-regulated vascular system. In this review, we provide a detailed characterization of the most relevant anatomical and functional features of the cortical vasculature. This includes a compilation of the available data on laminar variation of vascular density and the topological aspects of the microvascular system. We also review the spatio-temporal dynamics of cortical blood flow regulation and oxygenation, many aspects of which remain poorly understood. Finally, we discuss some of the important implications of vascular density, distribution, oxygenation and blood flow regulation for (laminar) fMRI.