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Processing of incoming sensory stimulation triggers an increase of cerebral perfusion and blood oxygenation (neurovascular response) as well as an alteration of the metabolic neurochemical profile (neurometabolic response). Here we show that perceived and unperceived isoluminant chromatic flickering stimuli designed to have similar neurovascular responses as measured by blood oxygenation level dependent functional MRI (BOLD-fMRI) in primary visual cortex (V1) have markedly different neurometabolic responses as measured by functional MRS. In particular, a significant regional buildup of lactate, an index of aerobic glycolysis, and glutamate, an index of malate-aspartate shuttle, occurred in V1 only when the flickering is perceived, without any relation with behavioral or physiological variables. Wheras the BOLD-fMRI signal in V1, a proxy for input to V1, was insensitive to flickering perception by design, the BOLD-fMRI signal in secondary visual areas was larger during perceived than unperceived flickering indicating increased output from V1. These results indicate that the upregulation of energy metabolism induced by visual stimulation depends on the type of information processing taking place in V1, and that 1H-fMRS provides unique information about local input/output balance that is not measured by BOLD-fMRI.Significance statementVisual perception has a measurable metabolic effect in the primary visual cortex (V1). Here we show that the upregulation of energy metabolism induced by isoluminant chromatic flickering depends on subjective visual perception. Within V1, perceived and unperceived stimuli that are contrast-matched to elicit similar blood-oxygenation level-dependent fMRI response are associated with clearly distinct neurochemical profiles. Specifically, regional accumulations of lactate (an index of aerobic glycolysis) and glutamate (an index of malate-aspartate shuttle) only develop during perceived stimuli, together with a larger activation of secondary visual areas. Our results imply a dissociation between metabolic and functional response, and indicate that that the upregulation of energy metabolism induced by visual stimulation depends on the type of information processing taking place in V1.

In‐scanner head motion represents a major confounding factor in functional connectivity studies and it raises particular concerns when motion correlates with the effect of interest. One such instance regards research focused on functional connectivity modulations induced by sustained cognitively demanding tasks. Indeed, cognitive engagement is generally associated with substantially lower in‐scanner movement compared with unconstrained, or minimally constrained, conditions. Consequently, the reliability of condition‐dependent changes in functional connectivity relies on effective denoising strategies. In this study, we evaluated the ability of common denoising pipelines to minimize and balance residual motion‐related artifacts between resting‐state and task conditions. Denoising pipelines—including realignment/tissue‐based regression, PCA/ICA‐based methods (aCompCor and ICA‐AROMA, respectively), global signal regression, and censoring of motion‐contaminated volumes—were evaluated according to a set of benchmarks designed to assess either residual artifacts or network identifiability. We found a marked heterogeneity in pipeline performance, with many approaches showing a differential efficacy between rest and task conditions. The most effective approaches included aCompCor, optimized to increase the noise prediction power of the extracted confounding signals, and global signal regression, although both strategies performed poorly in mitigating the spurious distance‐dependent association between motion and connectivity. Censoring was the only approach that substantially reduced distance‐dependent artifacts, yet this came at the great cost of reduced network identifiability. The implications of these findings for best practice in denoising task‐based functional connectivity data, and more generally for resting‐state data, are discussed., We showed the inefficacy of many denoising strategies in balancing motion‐related artifacts between functional conditions that are differently prone to motion, such as rest and cognitively demanding conditions. While no pipeline was able to completely suppress motion artifacts and simultaneously maximize network identifiability, we found that strategies exploiting an optimized aCompCor yielded the best results. Importantly, we advocate the use of censoring with caution, since we found the method to be cost ineffective, prone to introduce biases and to reduce network identifiability.

In a companion paper by Cohen-Adad et al. we introduce the spine generic quantitative MRI protocol that provides valuable metrics for assessing spinal cord macrostructural and microstructural integrity. This protocol was used to acquire a single subject dataset across 19 centers and a multi-subject dataset across 42 centers (for a total of 260 participants), spanning the three main MRI manufacturers: GE, Philips and Siemens. Both datasets are publicly available via git-annex. Data were analysed using the Spinal Cord Toolbox to produce normative values as well as inter/intra-site and inter/intra-manufacturer statistics. Reproducibility for the spine generic protocol was high across sites and manufacturers, with an average inter-site coefficient of variation of less than 5% for all the metrics. Full documentation and results can be found at https://spine-generic.rtfd.io/ . The datasets and analysis pipeline will help pave the way towards accessible and reproducible quantitative MRI in the spinal cord.

Purpose Image processing plays a fundamental role in the study of central nervous system, for example in the analysis of the vascular network in neurodegenerative diseases. Synchrotron X-ray Phase-contrast micro-Tomography (SXPCT) is a very attractive method to study weakly absorbing samples and features, such as the vascular network in the spinal cord (SC). However, the identification and segmentation of vascular structures in SXPCT images is seriously hampered by the presence of image noise and strong contrast inhomogeneities, due to the sensitivity of the technique to small electronic density variations. In order to help with these tasks, we implemented a user-friendly ImageJ plugin based on a 3D Gaussian steerable filter, tuned up for the enhancement of tubular structures in SXPCT images. Methods The developed 3D Gaussian steerable filter plugin for ImageJ is based on the steerability properties of Gaussian derivatives. We applied it to SXPCT images of ex-vivo mouse SCs acquired at different experimental conditions. Results The filter response shows a strong amplification of the source image contrast-to-background ratio (CBR), independently of structures orientation. We found that after the filter application, the CBR ratio increases by a factor ranging from ~6 to ~60. In addition, we also observed an increase of 35% of the contrast to noise ratio in the case of injured mouse SC. Conclusion The developed tool can generally facilitate the detection/segmentation of capillaries, veins and arteries that were not clearly observable in non-filtered SXPCT images. Its systematic application could allow obtaining quantitative information from pre-clinical and clinical images.

Skull-stripping and region segmentation are fundamental steps in preclinical magnetic resonance imaging (MRI) studies, and these common procedures are usually performed manually. We present Multi-task U-Net (MU-Net), a convolutional neural network designed to accomplish both tasks simultaneously. MU-Net achieved higher segmentation accuracy than state-of-the-art multi-atlas segmentation methods with an inference time of 0.35 seconds and no pre-processing requirements. We evaluated the performance of our network in the presence of skip connections and recently proposed framing connections, finding the simplest network to be the most effective. We tested MU-Net with an unusually large dataset combining several independent studies consisting of 1,782 mouse brain MRI volumes of both healthy and Huntington animals, and measured average Dice scores of 0.906 (striati), 0.937 (cortex), and 0.978 (brain mask). These high evaluation scores demonstrate that MU-Net is a powerful tool for segmentation and skull-stripping, decreasing inter and intra-rater variability of manual segmentation. The MU-Net code and the trained model are publicly available at https://github.com/Hierakonpolis/MU-Net.

One of the most common tasks in small rodents MRI pipelines is the voxel-wise segmentation of the volume in multiple classes. While many segmentation schemes have been developed for the human brain, fewer are available for rodent MRI, often by adaptation from human neuroimaging. Common methods include atlas-based and clustering schemes. The former labels the target volume by registering one or more pre-labeled atlases using a deformable registration method, in which case the result depends on the quality of the reference volumes, the registration algorithm and the label fusion approach, if more than one atlas is employed. The latter is based on an expectation maximization procedure to maximize the variance between voxel categories, and is often combined with Markov Random Fields and the atlas based approach to include spatial information, priors, and improve the classification accuracy. Our primary goal is to critically review the state of the art of rat and mouse segmentation of neuro MRI volumes and compare the available literature on popular, readily and freely available MRI toolsets, including SPM, FSL and ANTs, when applied to this task in the context of common pre-processing steps. Furthermore, we will briefly address the emerging Deep Learning methods for the segmentation of medical imaging, and the perspectives for applications to small rodents.

Idiopathic rapid eye movement sleep behavior disorder (iRBD) is a condition that often evolves into Parkinson's disease (PD). Therefore, by monitoring iRBD it is possible to track the neurodegeneration of individuals who may progress to PD. Here we aimed at piloting the characterization of brain tissue properties in mid-brain subcortical regions of 10 healthy subjects, 8 iRBD, and 9 early-diagnosed PD. We used a battery of magnetic resonance imaging (MRI) contrasts at 3 T, including adiabatic and non-adiabatic rotating frame techniques developed by our group, along with diffusion tensor imaging (DTI) and resting-state fMRI. Adiabatic T1ρ and T2ρ, and non-adiabatic RAFF4 (Relaxation Along a Fictitious Field in the rotating frame of rank 4) were found to have lower coefficient of variations and higher sensitivity to detect group differences as compared to DTI parameters such as fractional anisotropy and mean diffusivity. Significantly longer T1ρ were observed in the amygdala of PD subjects vs. controls, along with a trend of lower functional connectivity as measured by regional homogeneity, thereby supporting the notion that amygdalar dysfunction occurs in PD. Significant abnormalities in reward networks occurred in iRBD subjects, who manifested lower network strength of the accumbens. In agreement with previous studies, significantly longer T1ρ occurred in the substantia nigra compacta of PD vs. controls, indicative of neuronal degeneration, while regional homogeneity was lower in the substantia nigra reticulata. Finally, other trend-level findings were observed, i.e., lower RAFF4 and T2ρ in the midbrain of iRBD subjects vs. controls, possibly indicating changes in non-motor features as opposed to motor function in the iRBD group. We conclude that rotating frame relaxation methods along with functional connectivity measures are valuable to characterize iRBD and PD subjects, and with proper validation in larger cohorts may provide pathological signatures of iRBD and PD.

Cognitive decline in Down syndrome generally shows neurodegenerative aspects similar to what is observed in Alzheimer’s disease. Few studies reported information on white matter integrity. The aim of this study was to evaluate white matter alterations in a cohort of young Down subjects, without dementia, by means of DTI technique, compared to a normal control group. The study group consisted of 17 right-handed subjects with DS and many control subjects. All individuals participating in this study were examined by MR exam including DTI acquisition (32 non-coplanar directions); image processing and analysis were performed using FMRIB Software Library (FSL version 4.1.9, http://www.fmrib.ox.ac.uk/fsl )) software package. Finally, the diffusion tensor was estimated voxel by voxel and the FA map derived from the tensor. A two-sample t test was performed to assess differences between DS and control subjects. The FA is decreased in DS subjects, compared to control subjects, in the region of the anterior thalamic radiation, the inferior fronto-occipital fasciculum, the inferior longitudinal fasciculum, and the cortico-spinal tract, bilaterally. The early white matter damage visible in our DS subjects could have great impact in the therapeutic management, in particular in better adapting the timing of therapies to counteract the toxic effect of the deposition of amyloid that leads to oxidative stress.

Idiopathic rapid eye movement sleep behavior disorder (iRBD) is a condition that often evolves into Parkinson’s disease (PD). Therefore, by monitoring iRBD it is possible to track the neurodegeneration of individuals who may progress to PD. Here we aimed at piloting the characterization of brain tissue properties in mid-brain subcortical regions of 10 healthy subjects, 8 iRBD, and 9 early-diagnosed PD. We used a battery of magnetic resonance imaging (MRI) contrasts at 3 T, including adiabatic and non-adiabatic rotating frame techniques developed by our group, along with diffusion tensor imaging and resting-state fMRI. Adiabatic T1 and T2, and non-adiabatic RAFF4 (Relaxation Along a Fictitious Field in the rotating frame of rank 4) were found to have lower coefficient of variations and higher sensitivity to detect group differences as compared to DTI parameters such as fractional anisotropy and mean diffusivity. Significantly longer T1 were observed in the amygdala of PD subjects versus controls, along with a trend of lower functional connectivity as measured by network regional homogeneity, thereby supporting the notion that amygdalar dysfunction occurs in PD. Significant abnormalities in reward networks occurred in iRBD subjects, who manifested lower network strength of the accumbens. In agreement with previous studies, significantly longer T1 occurred in the substantia nigra compacta of PD versus controls, indicative of neuronal degeneration, while regional homogeneity was lower in the network of the substantia nigra reticulata. Finally, other trend-level findings were observed, i.e., lower RAFF4 and T2 in the midbrain of iRBD subjects vs controls, possibly indicating changes in non-motor features as opposed to motor function in the iRBD group. We conclude that rotating frame relaxation methods along with functional connectivity measures are valuable to characterize iRBD and PD subjects, and with proper validation in larger cohorts may provide pathological signatures of iRBD and PD.

Functional magnetic resonance imaging (fMRI) techniques are widely exploited for the study of brain activation. In recent years, similar approaches have been attempted for the study of spinal cord function; however, obtaining good functional images of spinal cord still represents a technical and scientific challenge. Some of the main limiting factors can be classified under the broad category of "physiological noise," and are related to 1) the cerebrospinal fluid (CSF) flux in the subarachnoid space surrounding the spinal cord; 2) the cord motion itself; and 3) the small area of the cord, which makes it critical to have a high image resolution. In addition, the different magnetic susceptibility properties of tissues surrounding the spinal cord reduce the local homogeneity of the static magnetic field, causing image distortion, reduction of the effective resolution, and signal loss, all effects that are modulated by motion. For these reasons, a number of methods have been developed for the purpose of denoising spinal cord fMRI time series. In this work, after a short introduction on the relevant features of the spinal cord anatomy, we review the main sources of physiological noise in spinal cord fMRI and discuss the main approaches useful for its mitigation.

Nowadays hybrid PET/MRI systems and magnetic resonance spectroscopy (MRS) with high field strength are becoming more widespread not only in research applications but also in clinical practice. The aim of this paper is to review the current roles of high-field PET/MRI and high-field magnetic resonance spectroscopy (MRS) in the clinical setting and their applications in research. A non-systematic literature search of the Medline and Cochrane Library databases was performed up to September 2012. Bibliographies of retrieved articles and review articles were also examined. Only those articles reporting complete data of clinical and research relevance for the present review (i.e., diagnosis, therapeutic response monitoring) were selected. The advent of higher field strengths (3-T and above), and thus, higher spectral resolution, increased the potentiality and the diffusion of MRS evaluations. This review article is divided into two major parts. The first considers the potential clinical and research roles of MRS at 3-T and of hybrid system PET/MRI, particularly with regard to simultaneous PET/MRI systems which provide some major advantages over the conventional PET/CT systems (such as identical position of the patient during image acquisition, no radiation burden from the MRI part, more structural, functional and molecular imaging at the same time). The second part provides detailed information about the use of ultra-high-field MRS and MRI (7-T) integrated with PET. The fusion of biological information (PET) and morpho-metabolic patterns (MRI–MRS) shows promise for obtaining early diagnosis, coherent follow-up and the desired response to therapy.

Functional Magnetic Resonance Imaging (fMRI) based on Blood Oxygenation Level Dependent (BOLD) contrast has become one of the most powerful tools in neuroscience research. On the other hand, fMRI approaches have seen limited use in the study of spinal cord and subcortical brain regions (such as the brainstem and portions of the diencephalon). Indeed obtaining good BOLD signal in these areas still represents a technical and scientific challenge, due to poor control of physiological noise and to a limited overall quality of the functional series. A solution can be found in the combination of optimized experimental procedures at acquisition stage, and well-adapted artifact mitigation procedures in the data processing. In this framework, we studied two different data processing strategies to reduce physiological noise in cortical and subcortical brain regions and in the spinal cord, based on the aCompCor and RETROICOR denoising tools respectively. The study, performed in healthy subjects, was carried out using an ad hoc isometric motor task. We observed an increased signal to noise ratio in the denoised functional time series in the spinal cord and in the subcortical brain region.

Defaults in vascular (VN) and neuronal networks of spinal cord are responsible for serious neurodegenerative pathologies. Because of inadequate investigation tools, the lacking knowledge of the complete fine structure of VN and neuronal systems is a crucial problem. Conventional 2D imaging yields incomplete spatial coverage leading to possible data misinterpretation, whereas standard 3D computed tomography imaging achieves insufficient resolution and contrast. We show that X-ray high-resolution phase-contrast tomography allows the simultaneous visualization of three-dimensional VN and neuronal systems of mouse spinal cord at scales spanning from millimeters to hundreds of nanometers, with neither contrast agent nor a destructive sample-preparation. We image both the 3D distribution of micro-capillary network and the micrometric nerve fibers, axon-bundles and neuron soma. Our approach is a crucial tool for pre-clinical investigation of neurodegenerative pathologies and spinal-cord-injuries. In particular, it should be an optimal tool to resolve the entangled relationship between VN and neuronal system., Comment: 15 pages, 6 figures

Noninvasive functional studies on human spinal cord by means of magnetic resonance imaging (MRI) are gaining attention because of the promising applications in the study of healthy and injured central nervous system. The findings obtained are generally consistent with the anatomic knowledge based on invasive methods, but the origin and specificity of functional contrast is still debated. In this paper, a review of current knowledge and major issues about functional MRI (fMRI) in the human spinal cord is presented, with emphasis on the main methodological and technical problems and on forthcoming applications as clinical tool.

Prostate cancer (PC) is one of the most common diseases in western countries and a leading cause of cancer death. A variety of treatment options are available but a precise disease characterization is needed: evaluation of cancer location, size, and extent and an indication of tumor aggressiveness. The current standard for diagnosing PC is transrectal ultrasound guided sextant biopsy. A novel multidisciplinary approach is required. Imaging may play a key role provided that dedicated prostate imagers and procedures are available: considerable improvements have been achieved in diagnosis with the Magnetic Resonance Imaging (MRI) and nuclear medicine (Positron Emission Tomography (PET) and Single Photon Emission Computed Tomography (SPECT)) techniques. Nevertheless, due to sub-optimal prostate imaging geometries with these generic large instruments preventing separation of the signal from surrounding organs, the sensitivity, spatial resolution and lesion contrast detected are lower compared to what can be potentially achievable with optimized dedicated prostate imagers and procedures. Fully exploiting the Time Of Flight (TOF) capability would allow not only to increase the Signal-to-Noise ratio (SNR) / Noise Equivalent Count Rate (NECR) but also to get rid of the huge background coming from neighboring organs i.e. the bladder. Recently a new research project was initiated by a large INFN collaboration, and a “TOF-PET and MRI for prostate cancer diagnosis and follow up experiment” (TOPEM) was financed as a 3-year experiment by the Italian “INFN Commissione Scientifica Nazionale V” with the goal of designing, building and testing in phantom tests an endorectal PET-TOF probe compatible with MRI.

Prostate cancer is the most common disease in men and the second leading cause of cancer death. Generic large instruments for diagnosis have sensitivity, spatial resolution, and contrast inferior with respect to dedicated prostate imagers. Multimodality imaging can play a significant role merging anatomical and functional details coming from simultaneous PET and MRI. The TOPEM project has the goal of designing, building, and testing an endorectal PET-TOF MRI probe. The performance is dominated by the detector close to the source. Results from simulation show spatial resolution of ~1.5 mm for source distances up to 80 mm. The efficiency is significantly improved with respect to the external PET. Mini-detectors have been built and tested. We obtained, for the first time, to our best knowledge, timing resolution of o400 ps and at the same time Depth Of Interaction (DOI) resolution of 1 mm or less. © 2012 Elsevier B.V. All rights reserved.

The number of functional magnetic resonance imaging (fMRI) studies performed on the human spinal cord (SC) has considerably increased in recent years. The lack of a validated processing pipeline is, however, a significant obstacle to the spread of SC fMRI. One component likely to be involved in any such pipeline is the process of SC masking, analogous to brain extraction in cerebral fMRI. In general, SC masking has been performed manually, with the incumbent costs of being very time consuming and operator dependent. To overcome these drawbacks, we have developed a tailored semiautomatic method for segmenting echoplanar images (EPI) of human spine that is able to identify the spinal canal and the SC. The method exploits both temporal and spatial features of the EPI series and was tested and optimized on EPI images of cervical spine acquired at 3 T. The dependence of algorithm performance on the degree of EPI image distortion was assessed by computing the displacement warping field that best matched the EPI to the corresponding high-resolution T 2 images. Segmentation accuracy was above 80%, a significant improvement over values obtained with similar approaches, but not exploiting temporal information. Geometric distortion was found to explain about 50% of the variance of algorithm classification efficiency.

The correlation and the interactions between neuronal activity and underlying metabolic dynamics are still a matter of debate, especially in pathological conditions. This study reports findings obtained on a subject suffering from fixation-off sensitivity (FOS) epilepsy, exploited as a model system of triggerable anomalous electrical activity. Functional Magnetic Resonance Spectroscopy was used to investigate the metabolic response to visual spike-inducing stimuli in a single voxel placed in the temporo-occipital lobe of a FOS epilepsy patient. MRS measurements were additionally performed on a control group of five healthy volunteers. The FOS patient also underwent an EEG session with the same stimulus paradigm. Uniquely in the FOS patient, glutamate and glutamine concentration increased during the first 10 min of stimulation and then returned to baseline. On the other hand, FOS-induced epileptic activity (spiking) endured throughout all the stimulation epoch. The observed metabolic dynamics may be likely linked to a complex interplay between alterations of the metabolic pathways of glutamate and modulation of the neuronal activity.

Multimodality imaging plays a significant role on specific diagnosis of prostate cancer. An endorectal PET-TOF MRI probe, designed here, allows for improved SNR and NECR with respect to standard imagers, providing better functional diagnosis of prostate diseases.

Parkinson's disease is a neurological disorder associated with the disfunction of dopaminergic pathways of the basal ganglia, mainly resulting in a progressive alteration in the execution of voluntary movements. We present a functional magnetic resonance imaging (fMRI) study on cortical activations during simple motor task performance, in six early–stage hemiparkinsonian patients and seven healthy volunteers. We acquired data in three sessions, during which subjects performed the task with right or left hand, or bimanually. We observed consistent bilateral activations in cingulate cortex and dorsolateral prefrontal cortex of Parkinsonian subjects during the execution of the task with the affected hand. In addition, patients showed both larger and stronger activations in motor cortex of the affected hemisphere with respect to the healthy hemisphere. Compared with the control group, patients showed a hyperactivation of the dorsolateral prefrontal cortex of the affected hemisphere. We concluded that a presymptomatic reorganization of the motor system is likely to occur in Parkinson's disease at earlier stages than previously hypothesized. Moreover, our results support fMRI as a sensitive technique for revealing the initial involvement of motor cortex areas at the debut of this degenerative disorder.

Low frequency fluctuations (LFFs) of the BOLD signal are a major discovery in the study of the resting brain with functional magnetic resonance imaging (fMRI). Two fMRI-based measures, functional connectivity (FC), a measure of signal synchronicity, and the amplitude of LFFs (ALFF), a measure of signal periodicity, have been proved to be sensitive to changes induced by several neurological diseases, including degenerative dementia. In spite of the increasing use of these measures, whether and how they are related to each other remains to be elucidated. In this work we used voxel-wise FC and ALFF computed in different frequency bands (slow-5: 0.01-0.027 Hz; slow-4: 0.027-0.073 Hz; and full-band: 0.01-0.073 Hz), in order to assess their relationship in healthy elderly as well as the relevant changes induced by Alzheimer's Disease (AD) and Mild Cognitive Impairment (MCI). We found that in healthy elderly subjects FC and ALFF are positively correlated in anterior and posterior cingulate cortex (full-band, slow-4 and slow-5), temporal cortex (full-band and slow-5), and in a set of subcortical regions (full-band and slow-4). These correlation patterns between FC and ALFF were absent in either AD or MCI patients. Notably, the loss of correlation between FC and ALFF in the AD group was primarily due to changes in FC rather than in ALFF. Our results indicate that degenerative dementia is characterized by a loss of global connection rather than by a decrease of fluctuation amplitude.

The aim of the present study was to analyze blood oxygenation level-dependent (BOLD) signal variation during an apnea-based task in order to assess the capability of a functional magnetic resonance imaging (MRI) procedure to estimate cerebral vascular dynamic effects. We measured BOLD contrast by hierarchical cluster analysis in healthy subjects undergoing an fMRI experiment, in which the task paradigm was one phase of inspirational apnea (IA). By processing the time courses of the fMRI experiment, analysis was performed only on a subclass of all the possible signal patterns; basically, root mean square and absolute variation differences have been calculated. Considering the baseline value obtained by computing the mean value of the initial rest period as reference, particular voxels showed relative important variations during the IA task and during the recovery phase following the IA. We focused our interest on the signal response of voxels that would correspond mainly to white and gray matter regions and that also may be affected by the proximity of large venous vessels. The results are presented as maps of space-temporal distribution of time series variations with two levels of hierarchical clustering among voxels with low to high initial response. Furthermore, we have presented a clustering of the signal response delay, conducting to a partition and identification of specified brain sites. (c) 2006 Elsevier Inc. All rights reserved.