What's new in neuroimaging methods?

Neurophotonics beyond the surface: unmasking the brain's complexity exploiting optical scattering

The intricate nature of the brain necessitates the application of advanced probing techniques to comprehensively study and understand its working mechanisms. Neurophotonics offers minimally invasive methods to probe the brain using optics at cellular and even molecular levels. However, multiple challenges persist, especially concerning imaging depth, field of view, speed, and biocompatibility. A major hindrance to solving these challenges in optics is the scattering nature of the brain. This perspective highlights the potential of complex media optics, a specialized area of study focused on light propagation in materials with intricate heterogeneous optical properties, in advancing and improving neuronal readouts for structural imaging and optical recordings of neuronal activity. Key strategies include wavefront shaping techniques and computational imaging and sensing techniques that exploit scattering properties for enhanced performance. We discuss the potential merger of the two fields as well as potential challenges and perspectives toward longer term in vivo applications.

Real-time motion-enabling positron emission tomography of the brain of upright ambulatory humans

BACKGROUND

Mobile upright PET devices have the potential to enable previously impossible neuroimaging studies. Currently available options are imagers with deep brain coverage that severely limit head/body movements or imagers with upright/motion enabling properties that are limited to only covering the brain surface.

METHODS

In this study, we test the feasibility of an upright, motion-compatible brain imager, our Ambulatory Motion-enabling Positron Emission Tomography (AMPET) helmet prototype, for use as a neuroscience tool by replicating a variant of a published PET/fMRI study of the neurocorrelates of human walking. We validate our AMPET prototype by conducting a walking movement paradigm to determine motion tolerance and assess for appropriate task related activity in motor-related brain regions. Human participants (n = 11 patients) performed a walking-in-place task with simultaneous AMPET imaging, receiving a bolus delivery of F18-Fluorodeoxyglucose.

RESULTS

Here we validate three pre-determined measure criteria, including brain alignment motion artifact of less than <2 mm and functional neuroimaging outcomes consistent with existing walking movement literature.

CONCLUSIONS

The study extends the potential and utility for use of mobile, upright, and motion-tolerant neuroimaging devices in real-world, ecologically-valid paradigms. Our approach accounts for the real-world logistics of an actual human participant study and can be used to inform experimental physicists, engineers and imaging instrumentation developers undertaking similar future studies. The technical advances described herein help set new priorities for facilitating future neuroimaging devices and research of the human brain in health and disease.

Neurobiological Effects of Transcranial Direct Current Stimulation over the Inferior Frontal Gyrus: A Systematic Review on Cognitive Enhancement in Healthy and Neurological Adults

The neurobiological effects of transcranial direct current stimulation (tDCS) have still not been unequivocally clarified. Some studies have suggested that the application of tDCS over the inferior frontal gyrus (IFG) enhances different aspects of cognition in healthy and neurological individuals, exerting neural changes over the target area and its neural surroundings. In this systematic review, randomized sham-controlled trials in healthy and neurological adults were selected through a database search to explore whether tDCS over the IFG combined with cognitive training modulates functional connectivity or neural changes. Twenty studies were finally included, among which twelve measured tDCS effects through functional magnetic resonance (fMRI), two through functional near-infrared spectroscopy (fNIRS), and six through electroencephalography (EEG). Due to the high heterogeneity observed across studies, data were qualitatively described and compared to assess reliability. Overall, studies that combined fMRI and tDCS showed widespread changes in functional connectivity at both local and distant brain regions. The findings also suggested that tDCS may also modulate electrophysiological changes underlying the targeted area. However, these outcomes were not always accompanied by corresponding significant behavioral results. This work raises the question concerning the general efficacy of tDCS, the implications of which extend to the steadily increasing tDCS literature.

Neurophotonics beyond the Surface: Unmasking the Brain's Complexity Exploiting Optical Scattering

The intricate nature of the brain necessitates the application of advanced probing techniques to comprehensively study and understand its working mechanisms. Neurophotonics offers minimally invasive methods to probe the brain using optics at cellular and even molecular levels. However, multiple challenges persist, especially concerning imaging depth, field of view, speed, and biocompatibility. A major hindrance to solving these challenges in optics is the scattering nature of the brain. This perspective highlights the potential of complex media optics, a specialized area of study focused on light propagation in materials with intricate heterogeneous optical properties, in advancing and improving neuronal readouts for structural imaging and optical recordings of neuronal activity. Key strategies include wavefront shaping techniques and computational imaging and sensing techniques that exploit scattering properties for enhanced performance. We discuss the potential merger of the two fields as well as potential challenges and perspectives toward longer term in vivo applications.

First activity and interactions in thalamus and cortex using raw single-trial EEG and MEG elicited by somatosensory stimulation

Introduction

One of the primary motivations for studying the human brain is to comprehend how external sensory input is processed and ultimately perceived by the brain. A good understanding of these processes can promote the identification of biomarkers for the diagnosis of various neurological disorders; it can also provide ways of evaluating therapeutic techniques. In this work, we seek the minimal requirements for identifying key stages of activity in the brain elicited by median nerve stimulation.

Methods

We have used a priori knowledge and applied a simple, linear, spatial filter on the electroencephalography and magnetoencephalography signals to identify the early responses in the thalamus and cortex evoked by short electrical stimulation of the median nerve at the wrist. The spatial filter is defined first from the average EEG and MEG signals and then refined using consistency selection rules across ST. The refined spatial filter is then applied to extract the timecourses of each ST in each targeted generator. These ST timecourses are studied through clustering to quantify the ST variability. The nature of ST connectivity between thalamic and cortical generators is then studied within each identified cluster using linear and non-linear algorithms with time delays to extract linked and directional activities. A novel combination of linear and non-linear methods provides in addition discrimination of influences as excitatory or inhibitory.

Results

Our method identifies two key aspects of the evoked response. Firstly, the early onset of activity in the thalamus and the somatosensory cortex, known as the P14 and P20 in EEG and the second M20 for MEG. Secondly, good estimates are obtained for the early timecourse of activity from these two areas. The results confirm the existence of variability in ST brain activations and reveal distinct and novel patterns of connectivity in different clusters.

Discussion

It has been demonstrated that we can extract new insights into stimulus processing without the use of computationally costly source reconstruction techniques which require assumptions and detailed modeling of the brain. Our methodology, thanks to its simplicity and minimal computational requirements, has the potential for real-time applications such as in neurofeedback systems and brain-computer interfaces.

Reproducibility and Reliability of Computing Models in Segmentation and Volumetric Measurement of Brain

Background

Segmentation and morphometric measurement of brain tissue and regions from non-invasive magnetic resonance images have clinical and research applications. Several software tools and models have been developed by different research groups which are increasingly used for segmentation and morphometric measurements. Variability in results has been observed in the imaging data processed with different neuroimaging pipelines which have increased the focus on standardization.

Purpose

The availability of several tools and models for brain morphometry poses challenges as an analysis done on the same set of data using different sets of tools and pipelines may result in different results and interpretations and there is a need for understanding the reliability and accuracy of such models.

Methods

T1-weighted (T1-w) brain volumes from the publicly available OASIS3 dataset have been analysed using recent versions of FreeSurfer, FSL-FAST, CAT12, and ANTs pipelines. grey matter (GM), white matter (WM), and estimated total intracranial volume (eTIV) have been extracted and compared for inter-method variability and accuracy.

Results

All four methods are consistent and strongly reproducible in their measurement across subjects however there is a significant degree of variability between these methods.

Conclusion

CAT12 and FreeSurfer methods have the highest degree of agreement in tissue class segmentation and are most reproducible compared to others.

BrainForge: an online data analysis platform for integrative neuroimaging acquisition, analysis, and sharing

BrainForge is a cloud-enabled, web-based analysis platform for neuroimaging research. This website allows users to archive data from a study and effortlessly process data on a high-performance computing cluster. After analyses are completed, results can be quickly shared with colleagues. BrainForge solves multiple problems for researchers who want to analyze neuroimaging data, including issues related to software, reproducibility, computational resources, and data sharing. BrainForge can currently process structural, functional, diffusion, and arterial spin labeling MRI modalities, including preprocessing and group level analyses. Additional pipelines are currently being added, and the pipelines can accept the BIDS format. Analyses are conducted completely inside of Singularity containers and utilize popular software packages including Nipype, Statistical Parametric Mapping, the Group ICA of fMRI Toolbox, and FreeSurfer. BrainForge also features several interfaces for group analysis, including a fully automated adaptive ICA approach.

A method for non-destructive microwave focusing for deep brain and tissue stimulation

Non-invasive stimulation of biological tissue is highly desirable for several biomedical applications. Of specific interest are methods for tumor treatment, endometrial ablation, and neuro-modulation. In traditional neuro-modulation, single- and multi-coil transcranial stimulation techniques in low oscillation frequencies are utilized to non-invasively penetrate the skull and elicit action potentials in cortical neurons. Although these methods have been proven effective, tightly focusing these signals to localized regions is difficult. In recent years, microwave (MW) methods have seen an increase usage as a minimally invasive treatment modality for ablation and neuro-stimulation. Unlike low frequency signals, MW signals can be focused to localized sub-centimeter regions. In this work we demonstrate that a three-dimensional array of MW antennas can be used to tightly focus signals to a localized region in space within the human body with MW frequencies. Assuming an array of small MW loop antennas are placed around the body, the optimal amplitude and phase of each array element can be accurately determined to match an arbitrary desired field profile. The major innovation of the presented method is that the fields that penetrate the biological region are determined via computing numerical Green's functions (NGF) that are then used to drive an optimization algorithm. Using simplified models of regions in the human body, it is shown that the MW fields at 1 GHz can be focused to sub-centimeter sized "hot spots" at depths of several centimeters. The algorithm can be easily extended to more realistic models of the human body or for non-biological applications.

In silico assessment of electrophysiological neuronal recordings mediated by magnetoelectric nanoparticles

Magnetoelectric materials hold untapped potential to revolutionize biomedical technologies. Sensing of biophysical processes in the brain is a particularly attractive application, with the prospect of using magnetoelectric nanoparticles (MENPs) as injectable agents for rapid brain-wide modulation and recording. Recent studies have demonstrated wireless brain stimulation in vivo using MENPs synthesized from cobalt ferrite (CFO) cores coated with piezoelectric barium titanate (BTO) shells. CFO-BTO core-shell MENPs have a relatively high magnetoelectric coefficient and have been proposed for direct magnetic particle imaging (MPI) of brain electrophysiology. However, the feasibility of acquiring such readouts has not been demonstrated or methodically quantified. Here we present the results of implementing a strain-based finite element magnetoelectric model of CFO-BTO core-shell MENPs and apply the model to quantify magnetization in response to neural electric fields. We use the model to determine optimal MENPs-mediated electrophysiological readouts both at the single neuron level and for MENPs diffusing in bulk neural tissue for in vivo scenarios. Our results lay the groundwork for MENP recording of electrophysiological signals and provide a broad analytical infrastructure to validate MENPs for biomedical applications.

Unveiling the phantom: What neuroimaging has taught us about phantom limb pain

Phantom limb pain (PLP) is a complicated condition with diverse clinical challenges. It consists of pain perception of a previously amputated limb. The exact pain mechanism is disputed and includes mechanisms involving cerebral, peripheral, and spinal origins. Such controversy limits researchers' and clinicians' ability to develop consistent therapeutics or management. Neuroimaging is an essential tool that can address this problem. This review explores diffusion tensor imaging, functional magnetic resonance imaging, electroencephalography, and magnetoencephalography in the context of PLP. These imaging modalities have distinct mechanisms, implications, applications, and limitations. Diffusion tensor imaging can outline structural changes and has surgical applications. Functional magnetic resonance imaging captures functional changes with spatial resolution and has therapeutic applications. Electroencephalography and magnetoencephalography can identify functional changes with a strong temporal resolution. Each imaging technique provides a unique perspective and they can be used in concert to reveal the true nature of PLP. Furthermore, researchers can utilize the respective strengths of each neuroimaging technique to support the development of innovative therapies. PLP exemplifies how neuroimaging and clinical management are intricately connected. This review can assist clinicians and researchers seeking a foundation for applications and understanding the limitations of neuroimaging techniques in the context of PLP.

Emerging behavioral and neuroimaging biomarkers for early and accurate characterization of autism spectrum disorders: a systematic review

The possibility of early treatment and a better outcome is the direct product of early identification and characterization of any pathological condition. Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by impairment in social communication, restricted, and repetitive patterns of behavior. In recent times, various tools and methods have been developed for the early identification and characterization of ASD features as early as 6 months of age. Thorough and exhaustive research has been done to identify biomarkers in ASD using noninvasive neuroimaging and various molecular methods. By employing advanced assessment tools such as MRI and behavioral assessment methods for accurate characterization of the ASD features and may facilitate pre-emptive interventional and targeted therapy programs. However, the application of advanced quantitative MRI methods is still confined to investigational/laboratory settings, and the clinical implication of these imaging methods in personalized medicine is still in infancy. Longitudinal research studies in neurodevelopmental disorders are the need of the hour for accurate characterization of brain-behavioral changes that could be monitored over a period of time. These findings would be more reliable and consistent with translating into the clinics. This review article aims to focus on the recent advancement of early biomarkers for the characterization of ASD features at a younger age using behavioral and quantitative MRI methods.

Applications of EEG indices for the quantification of human cognitive performance: A systematic review and bibliometric analysis

BACKGROUND

Neuroergonomics combines neuroscience with ergonomics to study human performance using recorded brain signals. Such neural signatures of performance can be measured using a variety of neuroimaging techniques, including functional magnetic resonance imaging (fMRI), functional near-infrared spectroscopy (fNIRS), and electroencephalography (EEG). EEG has an excellent temporal resolution, and EEG indices are highly sensitive to human brain activity fluctuations.

OBJECTIVE

The focus of this systematic review was to explore the applications of EEG indices for quantifying human performance in a variety of cognitive tasks at the macro and micro scales. To identify trends and the state of the field, we examined global patterns among selected articles, such as journal contributions, highly cited papers, affiliations, and high-frequency keywords. Moreover, we discussed the most frequently used EEG indices and synthesized current knowledge regarding the EEG signatures of associated human performance measurements.

METHODS

In this systematic review, we analyzed articles published in English (from peer-reviewed journals, proceedings, and conference papers), Ph.D. dissertations, textbooks, and reference books. All articles reviewed herein included exclusively EEG-based experimental studies in healthy participants. We searched Web-of-Science and Scopus databases using specific sets of keywords.

RESULTS

Out of 143 papers, a considerable number of cognitive studies focused on quantifying human performance with respect to mental fatigue, mental workload, mental effort, visual fatigue, emotion, and stress. An increasing trend for publication in this area was observed, with the highest number of publications in 2017. Most studies applied linear methods (e.g., EEG power spectral density and the amplitude of event-related potentials) to evaluate human cognitive performance. A few papers utilized nonlinear methods, such as fractal dimension, largest Lyapunov exponent, and signal entropy. More than 50% of the studies focused on evaluating an individual's mental states while operating a vehicle. Several different methods of artifact removal have also been noted. Based on the reviewed articles, research gaps, trends, and potential directions for future research were explored.

CONCLUSION

This systematic review synthesized current knowledge regarding the application of EEG indices for quantifying human performance in a wide variety of cognitive tasks. This knowledge is useful for understanding the global patterns of applications of EEG indices for the analysis and design of cognitive tasks.

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.

The peripartum human brain: Current understanding and future perspectives

The peripartum period offers a unique opportunity to improve our understanding of how dramatic fluctuations in endogenous ovarian hormones affect the human brain and behavior. This notwithstanding, peripartum depression remains an underdiagnosed and undertreated disorder. Here, we review recent neuroimaging findings with respect to the neuroplastic changes in the maternal brain during pregnancy and the postpartum period. We seek to provide an overview of multimodal neuroimaging designs of current peripartum depression models of hormone withdrawal, changes in monoaminergic signaling, and maladaptive neuroplasticity, which likely lead to the development of a condition that puts the lives of mother and infant at risk. We discuss the need to effectively integrate the available information on psychosocial and neurobiological risk factors contributing to individual vulnerability. Finally, we propose a systematic approach to neuroimaging the peripartum brain that acknowledges important co-morbidities and variation in disease onset.

Connectomic consistency: a systematic stability analysis of structural and functional connectivity

OBJECTIVE

Connectomics, the study of brain connectivity, has become an indispensable tool in neuroscientific research as it provides insights into brain organization. Connectomes are generated using different modalities such as diffusion MRI to capture structural organization of the brain or functional MRI to elaborate brain's functional organization. Understanding links between structural and functional organizations is crucial in explaining how observed behavior emerges from the underlying neurobiological mechanisms. Many studies have investigated how these two organizations relate to each other; however, we still lack a comparative understanding on how much variation should be expected in the two modalities, both between people and within a single person across scans.

APPROACH

In this study, we systematically analyzed the consistency of connectomes, that is the similarity between connectomes in terms of individual connections between brain regions and in terms of overall network topology. We present a comprehensive study of consistency in connectomes for a single subject examined longitudinally and across a large cohort of subjects cross-sectionally, in structure and function separately. Within structural connectomes, we compared connectomes generated by different tracking algorithms, parcellations, edge weighting schemes, and edge pruning techniques. In functional connectomes, we compared full, positive, and negative connectivity separately along with thresholding of weak edges. We evaluated consistency using correlation (incorporating information at the level of individual edges) and graph matching accuracy (evaluating connectivity at the level of network topology). We also examined the consistency of connectomes that are generated using different communication schemes.

MAIN RESULTS

Our results demonstrate varying degrees of consistency for the two modalities, with structural connectomes showing higher consistency than functional connectomes. Moreover, we observed a wide variation in consistency depending on how connectomes are generated.

SIGNIFICANCE

Our study sets a reference point for consistency of connectome types, which is especially important for structure-function coupling studies in evaluating mismatches between modalities.

Adaptations in reward-related behaviors and mesolimbic dopamine function during motherhood and the postpartum period

Initiation and maintenance of maternal behavior is driven by a complex interaction between the physiology of parturition and offspring stimulation, causing functional changes in maternal brain and behavior. Maternal behaviors are among the most robust and rewarding motivated behaviors. Mesolimbic dopamine (DA) system alterations during pregnancy and the postpartum enable enhanced reward-related responses to offspring stimuli. Here, we review behavioral evidence demonstrating postpartum rodents exhibit a bias towards pups and pup-related stimuli in reward-related tasks. Next, we provide an overview of normative adaptations in the mesolimbic DA system induced by parturition and the postpartum, which likely mediate shifts in offspring valence. We also discuss a causal link between dopaminergic dysfunction and disrupted maternal behaviors, which are recapitulated in postpartum depression (PPD) and relevant rodent models. In sum, mesolimbic DA system activation drives infant-seeking behavior and strengthens the mother-infant bond, potentially representing a therapeutic target for reward-related deficits in PPD.

Prediction of poor outcome after hypoxic-ischemic brain injury by diffusion-weighted imaging: A systematic review and meta-analysis

Accurate prediction of the neurological outcome following hypoxic-ischemic brain injury (HIBI) remains difficult. Diffusion-weighted imaging (DWI) can detect acute and subacute brain abnormalities following global cerebral hypoxia. Therefore, DWI can be used to predict the outcomes of HIBI. To this end, we searched the PubMed, EMBASE, and Cochrane Library databases for studies that examine the diagnostic accuracy of DWI in predicting HIBI outcomes in adult patients between January1995 and September 2019. Next, we conducted a comprehensive meta-analysis using the Meta-DiSc and several complementary techniques. Following the application of inclusion and exclusion criteria, a total of 28 studies were included with 98 data subsets. The overall sensitivity and specificity, with 95% confidence interval, were 0.613(0.599-0.628) and 0.958(0.947-0.967), respectively, and the area under the curve was 0.9090. Significant heterogeneity among the included studies and a threshold effect were observed (p<0.001). Different positive indices were the major sources for the heterogeneity, followed by the anatomical region examined, both of which significantly affected the prognostic accuracy. In conclusion, we demonstrated that DWI can be an instrumental modality in predicting the outcome of HIBI with good prognostic accuracy. However, the lack of clear and generally accepted positive indices limits its clinical application. Therefore, using more reliable positive indices and combining DWI with other clinical predictors may improve the diagnostic accuracy of HIBI.

Functional imaging

Functional imaging methodology has revolutionized our ability to understand brain-behavior relationships. In contrast with the static images obtained with standard imaging methods, functional images permit us to track brain activity as humans view stimuli, hear sounds, consider choices, and make decisions. The insights now possible because of this technology have not only provided new potential markers for disease but have also permitted questions of neural mechanism to be addressed in living humans. Because of the breadth and depth of research that directly or tangentially touches upon functional imaging, it is impossible to do justice to the various subfields, analysis streams, and methodological complexities in one chapter. Instead, this chapter will provide a brief overview of the underlying conceptual framework, basic analytic techniques, and details of the imaging methodologies available for the acquisition of functional imaging data.

Effective Connectivity of Beta Oscillations in Endometriosis-Related Chronic Pain During rest and Pain-Related Mental Imagery

Using the EEG recordings of patients with endometriosis-related chronic pelvic pain, we have examined the effective connectivity within the cortical pain-related network during rest and during pain-related imagery. During rest, an altered connectivity was hypothesized between cortical somatosensory pain areas and regions involved in emotional and cognitive modulation of pain. During pain-related imagery, alterations in prefrontal-temporal connectivity were expected. The effective connectivity was estimated using the Directed Transfer Function method. Differences between endometriosis patients and controls were found in the beta band (14-25 Hz). During rest, endometriosis was associated with an increased connectivity from the left dorsolateral prefrontal cortex to the left somatosensory cortex and also from the left somatosensory cortex to the orbitofrontal cortex and the right temporal cortex. These results might be related to sustained activation of the somatosensory pain system caused by the ongoing pain. During pain-related imagery, endometriosis patients showed an increased connectivity from the left dorsolateral prefrontal cortex to the right temporal cortex. This finding might point to impaired emotional regulation when processing pain-related stimuli, or it might be related to altered memorization of pain experiences. Results of this study open up new directions in chronic pain research aimed at exploring the beta band connectivity alterations. PERSPECTIVE: This study examined the pain system's dynamics in endometriosis patients with chronic pelvic pain during resting-state and pain-related mental imagery. The results could contribute to the development of new therapies using guided mental imagery.

Examining the relationship between perinatal depression and neurodevelopment in infants and children through structural and functional neuroimaging research

Depression is the most common perinatal psychiatric disorder, but little is known about how it may impact offspring neurodevelopment, as well as the mechanisms by which it may confer transgenerational psychiatric risk. This review presents imaging studies conducted to evaluate the relationship between perinatal depression (PND) and infant and child neurodevelopment. Altered structural and functional connectivity is implicated in children exposed to PND and anxiety. Overall, there are changes in connectivity between amygdala and the prefrontal cortex. Studies suggest decreased hippocampal growth in the first 6 months after birth, decreased cortical thickness in children, and increased amygdala volumes, that are more pronounced in female offspring. Future research is needed to understand the impact of PND on development so that early interventions which promote mother-infant bonding and cognitive development may improve developmental outcomes in children exposed to PND, reducing later risk of psychopathology.

Dissociating neural activity associated with the subjective phenomenology of monocular stereopsis: An EEG study

The subjective phenomenology associated with stereopsis, of solid tangible objects separated by a palpable negative space, is conventionally thought to be a by-product of the derivation of depth from binocular disparity. However, the same qualitative impression has been reported in the absence of disparity, e.g., when viewing pictorial images monocularly through an aperture. Here we aimed to explore if we could identify dissociable neural activity associated with the qualitative impression of stereopsis in the absence of the processing of binocular disparities. We measured EEG activity while subjects viewed pictorial (non-stereoscopic) images of 2D and 3D geometric forms under four different viewing conditions (binocular, monocular, binocular aperture, monocular aperture). EEG activity was analysed by oscillatory source localization (beamformer technique) to examine power change in occipital and parietal regions across viewing and stimulus conditions in targeted frequency bands (alpha: 8-13 Hz & gamma: 60-90 Hz). We observed expected event-related gamma synchronization and alpha desynchronization in occipital cortex and predominant gamma synchronization in parietal cortex across viewing and stimulus conditions. However, only the viewing condition predicted to generate the strongest impression of stereopsis (monocular aperture) revealed significantly elevated gamma synchronization within the parietal cortex for the critical contrasts (3D vs. 2D form). These findings suggest dissociable neural processes specific to the qualitative impression of stereopsis as distinguished from disparity processing.

Mean diffusivity related to collectivism among university students in Japan

Collectivism is an important factor for coping with stress in one’s social life. To date, no imaging studies have revealed a direct association between collectivism and white matter structure. Collectivism is positively related to independence, harm avoidance, rejection sensitivity, cooperativeness, external locus of control, and self-monitoring and negatively related to need for uniqueness. Accordingly, we hypothesised that the neural structures underpinning collectivism are those that are also involved with its relationship using magnetic resonance imaging (MRI). This study aimed to identify the brain structures associated with collectivism in healthy young adults (n = 797), using regional grey and white matter volume, fractional anisotropy, and mean diffusivity (MD) analyses of MRI data. Scores on the collectivism scale were positively associated with MD values in the bilateral dorsolateral prefrontal cortex, left orbitofrontal cortex, inferior frontal gyrus, right superior temporal gyrus, ventral posterior cingulate cortex, globus pallidus, and calcarine cortex using the threshold-free cluster enhancement method with family-wise errors corrected to P < 0.05 at the whole-brain level. No significant associations between were found collectivism and other measures. Thus, the present findings supported our hypothesis that the neural correlates of collectivism are situated in regions involved in its related factors.

What Can Computational Models Contribute to Neuroimaging Data Analytics?

Over the past years, nonlinear dynamical models have significantly contributed to the general understanding of brain activity as well as brain disorders. Appropriately validated and optimized mathematical models can be used to mechanistically explain properties of brain structure and neuronal dynamics observed from neuroimaging data. A thorough exploration of the model parameter space and hypothesis testing with the methods of nonlinear dynamical systems and statistical physics can assist in classification and prediction of brain states. On the one hand, such a detailed investigation and systematic parameter variation are hardly feasible in experiments and data analysis. On the other hand, the model-based approach can establish a link between empirically discovered phenomena and more abstract concepts of attractors, multistability, bifurcations, synchronization, noise-induced dynamics, etc. Such a mathematical description allows to compare and differentiate brain structure and dynamics in health and disease, such that model parameters and dynamical regimes may serve as additional biomarkers of brain states and behavioral modes. In this perspective paper we first provide very brief overview of the recent progress and some open problems in neuroimaging data analytics with emphasis on the resting state brain activity. We then focus on a few recent contributions of mathematical modeling to our understanding of the brain dynamics and model-based approaches in medicine. Finally, we discuss the question stated in the title. We conclude that incorporating computational models in neuroimaging data analytics as well as in translational medicine could significantly contribute to the progress in these fields.

Do We Need a Human post mortem Whole-Brain Anatomical Ground Truth in in vivo Magnetic Resonance Imaging?

Non-invasive in vivo neuroimaging techniques provide a wide array of possibilities to study human brain function. A number of approaches are available that improve our understanding of the anatomical location of brain activation patterns, including the development of probabilistic conversion tools to register individual in vivo data to population based neuroanatomical templates. Two elegant examples were published by Horn et al. (2017) in which a method was described to warp DBS electrode coordinates, and histological data to MNI-space (Ewert et al., 2017). The conversion of individual brain scans to a standard space is done assuming that individual anatomical scans provide a reliable image of the underlying neuroanatomy. It is unclear to what extent spatial distortions related to tissue properties, or MRI artifacts exist in these scans. Therefore, the question rises whether the anatomical information from the individual scans can be considered a real ground truth. To accommodate the knowledge-gap as a result of limited anatomical information, generative brain models have been developed circumventing these challenges through the application of assumption sets without recourse to any ground truth. We would like to argue that, although these efforts are valuable, the definition of an anatomical ground truth is preferred. Its definition requires a system in which non-invasive approaches can be validated using invasive methods of investigation. We argue that the application of post mortem MRI studies in combination with microscopy analyses brings an anatomical ground truth for the human brain within reach, which is of importance for all research within the human in vivo neuroimaging field.

Wireless resonant circuits for the minimally invasive sensing of biophysical processes in magnetic resonance imaging

Biological electromagnetic fields arise throughout all tissue depths and types, and correlate with physiological processes and signalling in organs of the body. Most of the methods for monitoring these fields are either highly invasive or spatially coarse. Here, we show that implantable active coil-based transducers that are detectable via magnetic resonance imaging enable the remote sensing of biological fields. These devices consist of inductively coupled resonant circuits that change their properties in response to electrical or photonic cues, thereby modulating the local magnetic resonance imaging signal without the need for onboard power or wired connectivity. We discuss design parameters relevant to the construction of the transducers on millimetre and submillimetre scales, and demonstrate their in vivo functionality for measuring time-resolved bioluminescence in rodent brains. Biophysical sensing via microcircuits that leverage the capabilities of magnetic resonance imaging may enable a wide range of biological and biomedical applications.

White matter integrity in medication-free women with peripartum depression: a tract-based spatial statistics study

Diffusion tensor imaging (DTI) studies in depression show decreased structural connectivity in the left anterior limb of the internal capsule and the genu of the corpus callosum but no such studies exist in peripartum depression (PPD), which affects 1 in 8 women. We analyzed fractional anisotropy (FA) as a measure of white matter integrity of these two tracts using tract-based spatial statistics (TBSS). We then conducted an exploratory whole-brain analysis to identify additional regions implicated in PPD. Seventy-five pregnant, medication-free women were evaluated with the Edinburgh Postnatal Depression Scale (EPDS) and Structured Clinical Interview (SCID) for DSM-IV-TR in pregnancy and in the postpartum. Structural MRI and DTI sequences were acquired in forty-four women within 2-8 weeks postpartum. TBSS data were analyzed between healthy comparison postpartum women (HCW) and women who developed PPD to determine differences in white matter integrity within the left anterior limb of the internal capsule and the genu of the corpus callosum, then analyzed across participants to explore correlation between FA and the EPDS score. An exploratory whole-brain analysis was also conducted to identify other potential regions showing differences in white matter integrity between groups, as well as correlation between EPDS and FA across groups. All results were corrected for multiple comparisons and analyses conducted using FSL, p < 0.05, K > 10. In comparison to HCW, women with PPD had significantly lower FA in left anterior limb of the internal capsule (p = 0.010). FA was negatively correlated with EPDS scores in the left anterior limb of the internal capsule (p = 0.019). In the whole-brain analysis, FA in the right retrolenticular internal capsule (p = 0.03) and two clusters within the body of the corpus callosum (p = 0.044, p = 0.050) were negatively correlated with EPDS; there were no between-group differences in FA. Reduced FA in the left anterior limb of the internal capsule suggests disruption of fronto-subcortical circuits in PPD. A negative correlation between FA within the body of the corpus callosum and EPDS total score could additionally reflect disrupted interhemispheric structural connectivity in women with depressive symptoms.

Visually-Driven Maps in Area 3b

Sensory perception relies on the precise neuronal encoding of modality-specific environmental features in primary sensory cortices. Some studies have reported the penetration of signals from other modalities even into early sensory areas. So far, no comprehensive account of maps induced by "foreign sources" exists. We addressed this question using surface-based topographic mapping techniques applied to ultra-high resolution fMRI neuroimaging data, measured in female participants. We show that fine-grained finger maps in human primary somatosensory cortex, area 3b, are somatotopically activated not only during tactile mechanical stimulation, but also when viewing the same fingers being touched. Visually-induced maps were weak in amplitude, but overlapped with the stronger tactile maps tangential to the cortical sheet when finger touches were observed in both first- and third-person perspectives. However, visually-induced maps did not overlap tactile maps when the observed fingers were only approached by an object but not actually touched. Our data provide evidence that "foreign source maps" in early sensory cortices are present in the healthy human brain, that their arrangement is precise, and that their induction is feature-selective. The computations required to generate such specific responses suggest that counterflow (feedback) processing may be much more spatially specific than has been often assumed.SIGNIFICANCE STATEMENT Using ultra-high field fMRI, we provide empirical evidence that viewing touches activates topographically aligned single finger maps in human primary somatosensory cortical area 3b. This shows that "foreign source maps" in early sensory cortices are topographic, precise, and feature-selective in healthy human participants with intact sensory pathways.

Polygenic Risk of Spasmodic Dysphonia is Associated With Vulnerable Sensorimotor Connectivity

Spasmodic dysphonia (SD), or laryngeal dystonia, is an isolated task-specific dystonia of unknown causes and pathophysiology that selectively affects speech production. Using next-generation whole-exome sequencing in SD patients, we computed polygenic risk score from 1804 genetic markers based on a genome-wide association study in another form of similar task-specific focal dystonia, musician's dystonia. We further examined the associations between the polygenic risk score, resting-state functional connectivity abnormalities within the sensorimotor network, and SD clinical characteristics. We found that the polygenic risk of dystonia was significantly associated with decreased functional connectivity in the left premotor/primary sensorimotor and inferior parietal cortices in SD patients. Reduced connectivity of the inferior parietal cortex was correlated with the age of SD onset. The polygenic risk score contained a significant number of genetic variants lying near genes related to synaptic transmission and neural development. Our study identified a polygenic contribution to the overall genetic risk of dystonia in the cohort of SD patients. Associations between the polygenic risk and reduced functional connectivity of the sensorimotor and inferior parietal cortices likely represent an endophenotypic imaging marker of SD, while genes involved in synaptic transmission and neuron development may be linked to the molecular pathophysiology of this disorder.

Glaucoma and the brain: Trans-synaptic degeneration, structural change, and implications for neuroprotection

A recent hypothesis to enter the literature suggests that glaucoma is a neurodegenerative disease. The basis for this has been the finding of central nervous system changes in glaucoma patients on histology and neuroimaging. It is known that retinal ganglion cell pathology of any cause leads to anterograde and retrograde retinal ganglion cell degeneration, as well as trans-synaptic (transneuronal) anterograde degeneration. Trans-synaptic degeneration has been demonstrated in a range of optic neuropathies including optic nerve transection, optic neuritis, and hereditary optic neuropathies. More recently, similar changes have been confirmed in glaucoma patients using the neuroimaging techniques of voxel-based morphometry and diffusion tensor imaging. Some studies have reported brain changes in glaucoma outside the retino-geniculo-cortical pathway; however, these are preliminary and exploratory in nature. Further research is required to identify whether the degenerative brain changes in glaucoma are entirely secondary to the optic neuropathy or whether there is additional primary central nervous system pathology. This has critical implications for neuroprotective and regenerative treatment strategies and our basic understanding of glaucoma.

Understanding Peripartum Depression Through Neuroimaging: a Review of Structural and Functional Connectivity and Molecular Imaging Research

PURPOSE OF REVIEW

Imaging research has sought to uncover brain structure, function, and metabolism in women with postpartum depression (PPD) as little is known about its underlying pathophysiology. This review discusses the imaging modalities used to date to evaluate postpartum depression and highlights recent findings.

RECENT FINDINGS

Altered functional connectivity and activity changes in brain areas implicated in executive functioning and emotion and reward processing have been identified in PPD. Metabolism changes involving monoamine oxidase A, gamma-aminobutyric acid, glutamate, serotonin, and dopamine have additionally been reported. To date, no studies have evaluated gray matter morphometry, voxel-based morphometry, surface area, cortical thickness, or white matter tract integrity in PPD. Recent imaging studies report changes in functional connectivity and metabolism in women with PPD vs. healthy comparison women. Future research is needed to extend these findings as they have important implications for the prevention and treatment of postpartum mood disorders.

Dancing or Fitness Sport? The Effects of Two Training Programs on Hippocampal Plasticity and Balance Abilities in Healthy Seniors

Age-related degenerations in brain structure are associated with balance disturbances and cognitive impairment. However, neuroplasticity is known to be preserved throughout lifespan and physical training studies with seniors could reveal volume increases in the hippocampus (HC), a region crucial for memory consolidation, learning and navigation in space, which were related to improvements in aerobic fitness. Moreover, a positive correlation between left HC volume and balance performance was observed. Dancing seems a promising intervention for both improving balance and brain structure in the elderly. It combines aerobic fitness, sensorimotor skills and cognitive demands while at the same time the risk of injuries is low. Hence, the present investigation compared the effects of an 18-month dancing intervention and traditional health fitness training on volumes of hippocampal subfields and balance abilities. Before and after intervention, balance was evaluated using the Sensory Organization Test and HC volumes were derived from magnetic resonance images (3T, MP-RAGE). Fourteen members of the dance (67.21 ± 3.78 years, seven females), and 12 members of the fitness group (68.67 ± 2.57 years, five females) completed the whole study. Both groups revealed hippocampal volume increases mainly in the left HC (CA1, CA2, subiculum). The dancers showed additional increases in the left dentate gyrus and the right subiculum. Moreover, only the dancers achieved a significant increase in the balance composite score. Hence, dancing constitutes a promising candidate in counteracting the age-related decline in physical and mental abilities.

Four-Dimensional Graded Consciousness

Both the multidimensional phenomenon and the polysemous notion of consciousness continue to prove resistant to consistent measurement and unambiguous definition. This is hardly surprising, given that there is no agreement even as regards the most fundamental issues they involve. One of the basic disagreements present in the continuing debate about consciousness pertains to its gradational nature. The general aim of this article is to show how consciousness might be graded and multidimensional at the same time. We therefore focus on the question of what it is, exactly, that is or could be graded in cases of consciousness, and how we can measure it. Ultimately, four different gradable aspects of consciousness will be described: quality, abstractness, complexity and usefulness, which belong to four different dimensions, these being understood, respectively, as phenomenal, semantic, physiological, and functional. Consequently, consciousness may be said to vary with respect to phenomenal quality, semantic abstraction, physiological complexity, and functional usefulness. It is hoped that such a four-dimensional approach will help to clarify and justify claims about the hierarchical nature of consciousness. The approach also proves explanatorily advantageous, as it enables us not only to draw attention to certain new and important differences in respect of subjective measures of awareness and to justify how a given creature may be ranked higher in one dimension of consciousness and lower in terms of another, but also allows for innovative explanations of a variety of well-known phenomena (amongst these, the interpretations of blindsight and locked-in syndrome will be briefly outlined here). Moreover, a 4D framework makes possible many predictions and hypotheses that may be experimentally tested (We point out a few such possibilities pertaining to interdimensional dependencies).

Is advanced neuroimaging for neuroradiologists? A systematic review of the scientific literature of the last decade

INTRODUCTION

To evaluate if advanced neuroimaging research is mainly conducted by imaging specialists, we investigated the number of first authorships by radiologists and non-radiologist scientists in articles published in the field of advanced neuroimaging in the past 10 years.

METHODS

Articles in the field of advanced neuroimaging identified in this retrospective bibliometric analysis were divided in four groups, depending on the imaging technique used. For all included studies, educational background of the first authors was recorded (based on available online curriculum vitae) and classified in subgroups, depending on their specialty. Finally, journal impact factors were recorded and comparatively assessed among subgroups as a metric of research quality.

RESULTS

A total number of 3831 articles were included in the study. Radiologists accounted as first authors for only 12.8 % of these publications, while 56.9 % of first authors were researchers without a medical degree. Mean impact factor (IF) of journals with non-MD researchers as first authors was significantly higher than the MD subgroup (p < 10^-20), while mean IF of journals with radiologists as first authors was significantly lower than articles authored by other MD specialists (p < 10^-11).

CONCLUSIONS

The majority of the studies in the field of advanced neuroimaging in the last decade is conducted by professional figures other than radiologists, who account for less than the 13 % of the publications. Furthermore, the mean IF value of radiologists-authored articles was the lowest among all subgroups. These results, taken together, should question the radiology community about its future role in the development of advanced neuroimaging.

The value of magnetic resonance imaging as a biomarker for amyotrophic lateral sclerosis: a systematic review

BACKGROUND

Amyotrophic lateral sclerosis (ALS) is a fatal, rapidly progressive neurodegenerative disease that mainly affects the motor system. A number of potentially neuroprotective and neurorestorative disease-modifying drugs are currently in clinical development. At present, the evaluation of a drug's clinical efficacy in ALS is based on the ALS Functional Rating Scale Revised, motor tests and survival. However, these endpoints are general, variable and late-stage measures of the ALS disease process and thus require the long-term assessment of large cohorts. Hence, there is a need for more sensitive radiological biomarkers. Various sequences for magnetic resonance imaging (MRI) of the brain and spinal cord have may have value as surrogate biomarkers for use in future clinical trials. Here, we review the MRI findings in ALS, their clinical correlations, and their limitations and potential role as biomarkers.

METHODS

The PubMed database was screened to identify studies using MRI in ALS. We included general MRI studies with a control group and an ALS group and longitudinal studies even if a control group was lacking.

RESULTS

A total of 116 studies were analysed with MRI data and clinical correlations. The most disease-sensitive MRI patterns are in motor regions but the brain is more broadly affected.

CONCLUSION

Despite the existing MRI biomarkers, there is a need for large cohorts with long term MRI and clinical follow-up. MRI assessment could be improved by standardized MRI protocols with multicentre studies.

Functional near-infrared spectroscopy for neuroimaging in cochlear implant recipients

Functional neuroimaging can provide insight into the neurobiological factors that contribute to the variations in individual hearing outcomes following cochlear implantation. To date, measuring neural activity within the auditory cortex of cochlear implant (CI) recipients has been challenging, primarily because the use of traditional neuroimaging techniques is limited in people with CIs. Functional near-infrared spectroscopy (fNIRS) is an emerging technology that offers benefits in this population because it is non-invasive, compatible with CI devices, and not subject to electrical artifacts. However, there are important considerations to be made when using fNIRS to maximize the signal to noise ratio and to best identify meaningful cortical responses. This review considers these issues, the current data, and future directions for using fNIRS as a clinical application in individuals with CIs. This article is part of a Special Issue entitled <Annual Reviews 2016>.

White Matter Abnormalities in Post-traumatic Stress Disorder Following a Specific Traumatic Event

Studies of posttraumatic stress disorder (PTSD) are complicated by wide variability in the intensity and duration of prior stressors in patient participants, secondary effects of chronic psychiatric illness, and a variable history of treatment with psychiatric medications. In magnetic resonance imaging (MRI) studies, patient samples have often been small, and they were not often compared to similarly stressed patients without PTSD in order to control for general stress effects. Findings from these studies have been inconsistent. The present study investigated whole-brain microstructural alterations of white matter in a large drug-naive population who survived a specific, severe traumatic event (a major 8.0-magnitude earthquake). Using diffusion tensor imaging (DTI), we explored group differences between 88 PTSD patients and 91 matched traumatized non-PTSD controls in fractional anisotropy (FA), as well as its component elements axial diffusivity (AD) and radial diffusivity (RD), and examined these findings in relation to findings from deterministic DTI tractography. Relations between white matter alterations and psychiatric symptom severity were examined. PTSD patients, relative to similarly stressed controls, showed an FA increase as well as AD and RD changes in the white matter beneath left dorsolateral prefrontal cortex and forceps major. The observation of increased FA in the PTSD group suggests that the pathophysiology of PTSD after a specific acute traumatic event is distinct from what has been reported in patients with several years duration of illness. Alterations in dorsolateral prefrontal cortex may be an important aspect of illness pathophysiology, possibly via the region's established role in fear extinction circuitry. Use-dependent myelination or other secondary compensatory changes in response to heightened demands for threat appraisal and emotion regulation may be involved.

What has functional connectivity and chemical neuroimaging in fibromyalgia taught us about the mechanisms and management of 'centralized' pain?

Research suggests that fibromyalgia is a central, widespread pain syndrome supported by a generalized disturbance in central nervous system pain processing. Over the past decades, multiple lines of research have identified the locus for many functional, chronic pain disorders to the central nervous system, and the brain. In recent years, brain neuroimaging techniques have heralded a revolution in our understanding of chronic pain, as they have allowed researchers to non-invasively (or minimally invasively) evaluate human patients suffering from various pain disorders. While many neuroimaging techniques have been developed, growing interest in two specific imaging modalities has led to significant contributions to chronic pain research. For instance, resting functional connectivity magnetic resonance imaging (fcMRI) is a recent adaptation of fMRI that examines intrinsic brain connectivity - defined as synchronous oscillations of the fMRI signal that occurs in the resting basal state. Proton magnetic resonance spectroscopy (1H-MRS) is a non-invasive magnetic resonance imaging technique that can quantify the concentration of multiple metabolites within the human brain. This review will outline recent applications of the complementary imaging techniques - fcMRI and 1H-MRS - to improve our understanding of fibromyalgia pathophysiology and how pharmacological and non-pharmacological therapies contribute to analgesia in these patients. A better understanding of the brain in chronic pain, with specific linkage as to which neural processes relate to spontaneous pain perception and hyperalgesia, will greatly improve our ability to develop novel therapeutics. Neuroimaging will play a growing role in the translational research approaches needed to make this a reality.

White matter disease and cognitive impairment in FMR1 premutation carriers

OBJECTIVE

This cross-sectional, observational study examined the role of white matter involvement in the cognitive impairment of individuals with the fragile X mental retardation 1 (FMR1) premutation.

METHODS

Eight asymptomatic premutation carriers, 5 participants with fragile X tremor/ataxia syndrome (FXTAS), and 7 noncarrier controls were studied. The mean age of the asymptomatic premutation carriers, participants with FXTAS, and noncarrier controls was 60, 71, and 67 years, respectively. Magnetic resonance spectroscopy (MRS) and diffusion tensor imaging (DTI) were used to examine the middle cerebellar peduncles (MCP) and the genu and splenium of the corpus callosum in relation to executive function and processing speed. MRS measures were N-acetyl aspartate/creatine (NAA/Cr) and choline/creatine, and fractional anisotropy (FA) was used for DTI. Executive function was assessed with the Behavioral Dyscontrol Scale and the Controlled Oral Word Association Test (COWAT), and processing speed with the Symbol Digit Modalities Test.

RESULTS

Among all 13 FMR1 premutation carriers, significant correlations were found between N-acetyl aspartate/creatine and choline/creatine in the MCP and COWAT scores, and between FA in the genu and performance on the Behavioral Dyscontrol Scale, COWAT, and Symbol Digit Modalities Test; a correlation was also found between FA in the splenium and COWAT performance. In all regions studied, participants with FXTAS had the lowest mean FA.

CONCLUSION

Microstructural white matter disease as determined by MRS and DTI correlated with executive dysfunction and slowed processing speed in these FMR1 premutation carriers. Neuroimaging abnormalities in the genu and MCP suggest that disruption of white matter within frontocerebellar networks has an important role in the cognitive impairment associated with the FMR1 premutation.

Long-range neural synchrony in behavior

Long-range synchrony between distant brain regions accompanies multiple forms of behavior. This review compares and contrasts the methods by which long-range synchrony is evaluated in both humans and model animals. Three examples of behaviorally relevant long-range synchrony are discussed in detail: gamma-frequency synchrony during visual perception, hippocampal-prefrontal synchrony during working memory, and prefrontal-amygdala synchrony during anxiety. Implications for circuit mechanism, translation, and clinical relevance are discussed.

Microstructural abnormalities in children with post-traumatic stress disorder: a diffusion tensor imaging study at 3.0T

Posttraumatic stress disorder (PTSD) is a severe anxiety disorder characterized by re-experiencing, avoidance and hyperarousal. Brain microstructure abnormalities in PTSD, especially in children, are not yet well characterized. The aim of this study was to use MR diffusion tensor imaging (DTI) to identify brain microstructure alterations in children with PTSD compared to non-PTSD controls who experienced the same time-limited trauma. We studied 27 children with PTSD and 24 age- and gender-matched traumatized controls without PTSD, who all experienced the 2008 Sichuan major earthquake. DTI data were acquired and analyzed in terms of fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD) and axial diffusivity (AD). Children with PTSD showed an abnormal pattern, not only of FA, but also of the diffusivity measures MD, AD and RD. Most of the abnormal brain regions belonged to two important networks: the default-mode network, including precuneus and angular gyrus, and the salience network, including insula, putamen and thalamus. This DTI study identifies microstructural abnormalities of children with PTSD after a major earthquake, our results are consistent with the suggestion that pediatric PTSD is accompanied by a connectivity disequilibrium between the salience and default-mode networks, a finding of potential pathophysiological significance.

False positive rates in Voxel-based Morphometry studies of the human brain: should we be worried?

Voxel-based Morphometry (VBM) is a widely used automated technique for the analysis of neuroanatomical images. Despite its popularity within the neuroimaging community, there are outstanding concerns about its potential susceptibility to false positive findings. Here we review the main methodological factors that are known to influence the results of VBM studies comparing two groups of subjects. We then use two large, open-access data sets to empirically estimate false positive rates and how these depend on sample size, degree of smoothing and modulation. Our review and investigation provide three main results: (i) when groups of equal size are compared false positive rate is not higher than expected, i.e. about 5%; (ii) the sample size, degree of smoothing and modulation do not appear to influence false positive rate; (iii) when they exist, false positive findings are randomly distributed across the brain. These results provide reassurance that VBM studies comparing groups are not vulnerable to the higher than expected false positive rates that are evident in single case VBM.

Advances in imaging explosive blast mild traumatic brain injury

In the past, direct physical evidence of mild traumatic brain injury (mTBI) from explosive blast has been difficult to obtain through conventional imaging modalities such as T1- and T2-weighted magnetic resonance imaging (MRI) and computed tomography (CT). Here, we review current progress in detecting evidence of brain injury from explosive blast using advanced imaging, including diffusion tensor imaging (DTI), functional MRI (fMRI), and the metabolic imaging methods such as positron emission tomography (PET) and magnetic resonance spectroscopic imaging (MRSI), where each targets different aspects of the pathology involved in mTBI. DTI provides a highly sensitive measure to detect primary changes in the microstructure of white matter tracts. fMRI enables the measurement of changes in brain activity in response to different stimuli or tasks. Remarkably, all three of these paradigms have found significant success in conventional mTBI where conventional clinical imaging frequently fails to provide definitive differences. Additionally, although used less frequently for conventional mTBI, PET has the potential to characterize a variety of neurotransmitter systems using target agents and will undoubtedly play a larger role, once the basic mechanisms of injury are better understood and techniques to identify the injury are more common. Finally, our MRSI imaging studies, although acquired at much lower spatial resolution, have demonstrated selectivity to different metabolic and physiologic processes, uncovering some of the most profound differences on an individual by individual basis, suggesting the potential for utility in the management of individual patients.

Resting state functional connectivity changes induced by prior brain state are not network specific

Resting state functional connectivity (rFC) is used to identify functionally related brain areas without requiring subjects to perform specific tasks. Previous work suggests that prior brain state, as determined by the activity engaged in immediately prior to collection of resting state data, can influence the networks recovered by rFC analyses. We determined the prevalence and network specificity of rFC changes induced by manipulations of prior state (including an unstructured (unconstrained) state, and language and motor tasks). Three blocks of rest data (one after each of the specified prior states) were acquired on each of 25 subjects. We hypothesised that prior state induced changes in rFC would be greatest within the networks most actively recruited by that prior state. Changes in rFC were greatest following the motor task and, contrary to our hypothesis, were not network specific. This was demonstrated by comparing (1) the timecourses within a set of ROIs selected on the basis of task-related de/activation, and (2) seed-based whole brain voxel-wise connectivity maps, seeded from local maxima in the task-related de/activation maps. Changes in connectivity strength tended to manifest as increases in rFC relative to that in the unstructured rest state, with change maps resembling partially complete maps of the primary sensory cortices and the cognitive control network. The majority of rFC changes occurred in areas moderately (but not weakly) connected to the seeds. Constrained prior states were associated with lower across-participant variance in rFC. This systematic investigation of the effect of prior brain state on rFC indicates that the rFC changes induced by prior brain state occur both in brain networks related to that brain activity and in networks nominally unrelated to that brain activity.

Non-contrast-enhanced whole-body magnetic resonance imaging in the general population: the incidence of abnormal findings in patients 50 years old and younger compared to older subjects

Purpose

To assess and compare the incidence of abnormal findings detected during non-contrast-enhanced whole-body magnetic resonance imaging (WB-MRI) in the general population in two age groups: (1) 50 years old and younger; and (2) over 50 years old.

Materials and Methods

The analysis included 666 non-contrast-enhanced WB-MRIs performed on a 1.5-T scanner between December 2009 and June 2013 in a private hospital in 451 patients 50 years old and younger and 215 patients over 50 years old. The following images were obtained: T2-STIR (whole body-coronal plane), T2-STIR (whole spine-sagittal), T2-TSE with fat-saturation (neck and trunk-axial), T2-FLAIR (head-axial), 3D T1-GRE (thorax-coronal, axial), T2-TSE (abdomen-axial), chemical shift (abdomen-axial). Detected abnormalities were classified as: insignificant (type I), potentially significant, requiring medical attention (type II), significant, requiring treatment (type III).

Results

There were 3375 incidental findings depicted in 659 (98.9%) subjects: 2997 type I lesions (88.8%), 363 type II lesions (10.8%) and 15 type III lesions (0.4%), including malignant or possibly malignant lesions in seven subjects. The most differences in the prevalence of abnormalities on WB-MRI between patients 50 years old and younger and over 50 years old concerned: brain infarction (22.2%, 45.0% respectively), thyroid cysts/nodules (8.7%, 18.8%), pulmonary nodules (5.0%, 16.2%), significant degenerative disease of the spine (23.3%, 44.5%), extra-spinal degenerative disease (22.4%, 61.1%), hepatic steatosis (15.8%, 24.9%), liver cysts/hemangiomas (24%, 34.5%), renal cysts (16.9%, 40.6%), prostate enlargement (5.1% of males, 34.2% of males), uterine fibroids (16.3% of females, 37.9% of females).

Conclusions

Incidental findings were detected in almost all of the subjects. WB-MRI demonstrated that the prevalence of the vast majority of abnormalities increases with age.