Optimized brain extraction for pathological brains (optiBET)

Large-scale differences in functional organization of left- and right-handed individuals using whole-brain, data-driven analysis of connectivity

Handedness influences differences in lateralization of language areas as well as dominance of motor and somatosensory cortices. However, differences in whole-brain functional connectivity (i.e., functional connectomes) due to handedness have been relatively understudied beyond pre-specified networks of interest. Here, we compared functional connectomes of left- and right-handed individuals at the whole brain level. We explored differences in functional connectivity of previously established regions of interest, and showed differences between primarily left- and primarily right-handed individuals in the motor, somatosensory, and language areas using functional connectivity. We then proceeded to investigate these differences in the whole brain and found that the functional connectivity of left- and right-handed individuals are not specific to networks of interest, but extend across every region of the brain. In particular, we found that connections between and within the cerebellum show distinct patterns of connectivity. To put these effects into context, we show that the effect sizes associated with handedness differences account for a similar amount of individual differences in the connectome as sex differences. Together these results shed light on regions of the brain beyond those traditionally explored that contribute to differences in the functional organization of left- and right-handed individuals and underscore that handedness effects are neurobiologically meaningful in addition to being statistically significant.

Vestibular/ocular motor symptoms in concussed adolescents are linked to retrosplenial activation

Following concussion, adolescents often experience vestibular and ocular motor symptoms as well as working memory deficits that may affect their cognitive, academic and social well-being. Complex visual environments including school activities, playing sports, or socializing with friends may be overwhelming for concussed adolescents suffering from headache, dizziness, nausea and fogginess, thus imposing heightened requirements on working memory to adequately function in such environments. While understanding the relationship between working memory and vestibular/ocular motor symptoms is critically important, no previous study has examined how an increase in working memory task difficulty affects the relationship between severity of vestibular/ocular motor symptoms and brain and behavioural responses in a working memory task. To address this question, we examined 80 adolescents (53 concussed, 27 non-concussed) using functional MRI while performing a 1-back (easy) and 2-back (difficult) working memory tasks with angry, happy, neutral and sad face distractors. Concussed adolescents completed the vestibular/ocular motor screening and were scanned within 10 days of injury. We found that all participants showed lower accuracy and slower reaction time on difficult (2-back) versus easy (1-back) tasks (P-values < 0.05). Concussed adolescents were significantly slower than controls across all conditions (P < 0.05). In concussed adolescents, higher vestibular/ocular motor screening total scores were associated with significantly greater differences in reaction time between 1-back and 2-back across all distractor conditions and significantly greater differences in retrosplenial cortex activation for the 1-back versus 2-back condition with neutral face distractors (P-values < 0.05). Our findings suggest that processing of emotionally ambiguous information (e.g. neutral faces) additionally increases the task difficulty for concussed adolescents. Post-concussion vestibular/ocular motor symptoms may reduce the ability to inhibit emotionally ambiguous information during working memory tasks, potentially affecting cognitive, academic and social functioning in concussed adolescents.

Language lateralization and outcome in perinatal stroke patients with different vascular types

Perinatal stroke affects child's language development and can change language lateralization. Language generation and comprehension tasks in functional magnetic resonance imaging were used to determine language lateralization in term born children with perinatal left-side arterial ischemic stroke (AIS) (n = 9, mean age (SD) 13.4 (3.1) y.) and periventricular venous infarction (PVI) (n = 12, 11.8 (2.8) y.), and in healthy right-handed controls (n = 30, 11.6 (2.6) y.). Lateralization index was calculated for the Broca and Wernicke areas and correlated with language and cognitive outcomes measured by the Kaufman Assessment Battery for Children II ed. Language outcome in children with perinatal stroke is poorer compared to healthy controls. Children with small AIS lesions and most children with PVI showed left-side language activation. Most children with large AIS lesions and one child with large PVI had language activation reorganized to the right hemisphere. Language reorganization to the unlesioned right hemisphere did not ensure normal language outcome.

Resting State Functional Connectivity between Dorsal Attentional Network and Right Inferior Frontal Gyrus in Concussed and Control Adolescents

Concussion among adolescents continues to be a public health concern. Yet, the differences in brain function between adolescents with a recent concussion and adolescents with no history of concussion are not well understood. Although resting state functional magnetic resonance imaging (fMRI) can be a useful tool in examining these differences, few studies have used this technique to examine concussion in adolescents. Here, we investigate the differences in the resting state functional connectivity of 52 adolescents, 38 with a concussion in the previous 10 days (mean age = 15.6; female = 36.8%), and 14 controls with no concussion history (mean age = 15.1; female = 57.1%). Independent component analysis and dual regression revealed that control adolescents had significantly greater functional connectivity between the dorsal attention network (DAN) and right inferior frontal gyrus (RIFG) compared to concussed adolescents (p-corrected < 0.001). Specifically, there was a positive DAN-RIFG connectivity in control, but not concussed, adolescents. Our findings indicate that concussion is associated with disrupted DAN-RIFG connectivity, which may reflect a general, nonspecific response to injury.

Ultrasound stimulation of the motor cortex during tonic muscle contraction

Transcranial ultrasound stimulation (tUS) shows potential as a noninvasive brain stimulation (NIBS) technique, offering increased spatial precision compared to other NIBS techniques. However, its reported effects on primary motor cortex (M1) are limited. We aimed to better understand tUS effects in human M1 by performing tUS of the hand area of M1 (M1hand) during tonic muscle contraction of the index finger. Stimulation during muscle contraction was chosen because of the transcranial magnetic stimulation-induced phenomenon known as cortical silent period (cSP), in which transcranial magnetic stimulation (TMS) of M1hand involuntarily suppresses voluntary motor activity. Since cSP is widely considered an inhibitory phenomenon, it presents an ideal parallel for tUS, which has often been proposed to preferentially influence inhibitory interneurons. Recording electromyography (EMG) of the first dorsal interosseous (FDI) muscle, we investigated effects on muscle activity both during and after tUS. We found no change in FDI EMG activity concurrent with tUS stimulation. Using single-pulse TMS, we found no difference in M1 excitability before versus after sparsely repetitive tUS exposure. Using acoustic simulations in models made from structural MRI of the participants that matched the experimental setups, we estimated in-brain pressures and generated an estimate of cumulative tUS exposure experienced by M1hand for each subject. We were unable to find any correlation between cumulative M1hand exposure and M1 excitability change. We also present data that suggest a TMS-induced MEP always preceded a near-threshold cSP.

What's new and what's next in diffusion MRI preprocessing

Diffusion MRI (dMRI) provides invaluable information for the study of tissue microstructure and brain connectivity, but suffers from a range of imaging artifacts that greatly challenge the analysis of results and their interpretability if not appropriately accounted for. This review will cover dMRI artifacts and preprocessing steps, some of which have not typically been considered in existing pipelines or reviews, or have only gained attention in recent years: brain/skull extraction, B-matrix incompatibilities w.r.t the imaging data, signal drift, Gibbs ringing, noise distribution bias, denoising, between- and within-volumes motion, eddy currents, outliers, susceptibility distortions, EPI Nyquist ghosts, gradient deviations, B1 bias fields, and spatial normalization. The focus will be on "what's new" since the notable advances prior to and brought by the Human Connectome Project (HCP), as presented in the predecessing issue on "Mapping the Connectome" in 2013. In addition to the development of novel strategies for dMRI preprocessing, exciting progress has been made in the availability of open source tools and reproducible pipelines, databases and simulation tools for the evaluation of preprocessing steps, and automated quality control frameworks, amongst others. Finally, this review will consider practical considerations and our view on "what's next" in dMRI preprocessing.

Ultrasonic Deep Brain Neuromodulation in Acute Disorders of Consciousness: A Proof-of-Concept

The promotion of recovery in patients who have entered a disorder of consciousness (DOC; e.g., coma or vegetative states) following severe brain injury remains an enduring medical challenge despite an ever-growing scientific understanding of these conditions. Indeed, recent work has consistently implicated altered cortical modulation by deep brain structures (e.g., the thalamus and the basal ganglia) following brain damage in the arising of, and recovery from, DOCs. The (re)emergence of low-intensity focused ultrasound (LIFU) neuromodulation may provide a means to selectively modulate the activity of deep brain structures noninvasively for the study and treatment of DOCs. This technique is unique in its combination of relatively high spatial precision and noninvasive implementation. Given the consistent implication of the thalamus in DOCs and prior results inducing behavioral recovery through invasive thalamic stimulation, here we applied ultrasound to the central thalamus in 11 acute DOC patients, measured behavioral responsiveness before and after sonication, and applied functional MRI during sonication. With respect to behavioral responsiveness, we observed significant recovery in the week following thalamic LIFU compared with baseline. With respect to functional imaging, we found decreased BOLD signals in the frontal cortex and basal ganglia during LIFU compared with baseline. In addition, we also found a relationship between altered connectivity of the sonicated thalamus and the degree of recovery observed post-LIFU.

White matter hyperintensities relate to executive dysfunction, apathy, but not disinhibition in long-term adult survivors of pediatric cerebellar tumor

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Pediatric brain tumor survivors have more executive dysfunction than controls.

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White matter hyperintensities are positively associated with executive dysfunction.

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White matter hyperintensities are positively associated with apathy.

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Multivariate regression supports white matter hyperintensity associations.

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Survivors appear to drive white matter hyperintensities associations.

White matter hyperintensities (WMHs) have been related to executive dysfunction, apathy and disinhibition in a wide range of neurological populations. However, this relationship has not been examined in survivors of pediatric brain tumor. The goal of this study was to investigate how executive dysfunction, apathy, and disinhibition relate to WMHs in 31 long-term survivors of pediatric cerebellar brain tumor and 58 controls, using informant-report data from the Frontal Systems Behavior Scale. Total WMH volume was quantified using the Lesion Growth Algorithm. Further, periventricular, and subcortical volumes were identified based on proximity to custom ventricle masks generated in FSL. A ratio of WMH volume to whole brain volume was used to obtain normalized WMH volumes. Additionally, a multivariate regression analysis was performed. On average, informant-report scores were within normal limits and only executive dysfunction was significantly higher in survivors compared to controls (t(47.9) = -2.4, p=.023). Informants reported clinically significant levels of apathy in 32.3% of survivors. Informants also reported clinically significant executive dysfunction in 19.4 % of survivors and clinically significant disinhibition in, again, 19.4 % of survivors. Increased volume of WMHs was positively correlated with executive dysfunction (r = 0.33, p = 0.02) and apathy (r = 0.23, p = .04). Similarly, multivariate regression demonstrated correlations with executive dysfunction (p=.05, FDR corrected) and apathy (p=.05, FDR corrected). Exploratory analysis demonstrated an interaction wherein the relationship between total WMHs and executive dysfunction and apathy depends on whether the participant was a survivor. The current findings indicate that increased WMH volumes are associated with higher ratings of apathy and executive dysfunction, and that these results are likely unique to cerebellar brain tumor survivors. WMH burden may serve as a useful marker to identify survivors at risk of executive dysfunction or increased apathy.

The SACT Template: A Human Brain Diffusion Tensor Template for School-age Children

School-age children are in a specific development stage corresponding to juvenility, when the white matter of the brain experiences ongoing maturation. Diffusion-weighted magnetic resonance imaging (DWI), especially diffusion tensor imaging (DTI), is extensively used to characterize the maturation by assessing white matter properties in vivo. In the analysis of DWI data, spatial normalization is crucial for conducting inter-subject analyses or linking the individual space with the reference space. Using tensor-based registration with an appropriate diffusion tensor template presents high accuracy regarding spatial normalization. However, there is a lack of a standardized diffusion tensor template dedicated to school-age children with ongoing brain development. Here, we established the school-age children diffusion tensor (SACT) template by optimizing tensor reorientation on high-quality DTI data from a large sample of cognitively normal participants aged 6-12 years. With an age-balanced design, the SACT template represented the entire age range well by showing high similarity to the age-specific templates. Compared with the tensor template of adults, the SACT template revealed significantly higher spatial normalization accuracy and inter-subject coherence upon evaluation of subjects in two different datasets of school-age children. A practical application regarding the age associations with the normalized DTI-derived data was conducted to further compare the SACT template and the adult template. Although similar spatial patterns were found, the SACT template showed significant effects on the distributions of the statistical results, which may be related to the performance of spatial normalization. Looking forward, the SACT template could contribute to future studies of white matter development in both healthy and clinical populations. The SACT template is publicly available now ( https://figshare.com/articles/dataset/SACT_template/14071283 ).

Associations among Household and Neighborhood Socioeconomic Disadvantages, Resting-state Frontoamygdala Connectivity, and Internalizing Symptoms in Youth

Exposure to socioeconomic disadvantages (SED) can have negative impacts on mental health, yet SED are a multifaceted construct and the precise processes by which SED confer deleterious effects are less clear. Using a large and diverse sample of preadolescents (ages 9-10 years at baseline, n = 4038, 49% female) from the Adolescent Brain Cognitive Development Study, we examined associations among SED at both household (i.e., income-needs and material hardship) and neighborhood (i.e., area deprivation and neighborhood unsafety) levels, frontoamygdala resting-state functional connectivity, and internalizing symptoms at baseline and 1-year follow-up. SED were positively associated with internalizing symptoms at baseline and indirectly predicted symptoms 1 year later through elevated symptoms at baseline. At the household level, youth in households characterized by higher disadvantage (i.e., lower income-to-needs ratio) exhibited more strongly negative frontoamygdala coupling, particularly between the bilateral amygdala and medial OFC (mOFC) regions within the frontoparietal network. Although more strongly positive amygdala-mOFC coupling was associated with higher levels of internalizing symptoms at baseline and 1-year follow-up, it did not mediate the association between income-to-needs ratio and internalizing symptoms. However, at the neighborhood level, amygdala-mOFC functional coupling moderated the effect of neighborhood deprivation on internalizing symptoms. Specifically, higher neighborhood deprivation was associated with higher internalizing symptoms for youth with more strongly positive connectivity, but not for youth with more strongly negative connectivity, suggesting a potential buffering effect. Findings highlight the importance of capturing multilevel socioecological contexts in which youth develop to identify youth who are most likely to benefit from early interventions.

Linking Multi-Modal MRI to Clinical Measures of Visual Field Loss After Stroke

Loss of vision across large parts of the visual field is a common and devastating complication of cerebral strokes. In the clinic, this loss is quantified by measuring the sensitivity threshold across the field of vision using static perimetry. These methods rely on the ability of the patient to report the presence of lights in particular locations. While perimetry provides important information about the intactness of the visual field, the approach has some shortcomings. For example, it cannot distinguish where in the visual pathway the key processing deficit is located. In contrast, brain imaging can provide important information about anatomy, connectivity, and function of the visual pathway following stroke. In particular, functional magnetic resonance imaging (fMRI) and analysis of population receptive fields (pRF) can reveal mismatches between clinical perimetry and maps of cortical areas that still respond to visual stimuli after stroke. Here, we demonstrate how information from different brain imaging modalities-visual field maps derived from fMRI, lesion definitions from anatomical scans, and white matter tracts from diffusion weighted MRI data-provides a more complete picture of vision loss. For any given location in the visual field, the combination of anatomical and functional information can help identify whether vision loss is due to absence of gray matter tissue or likely due to white matter disconnection from other cortical areas. We present a combined imaging acquisition and visual stimulus protocol, together with a description of the analysis methodology, and apply it to datasets from four stroke survivors with homonymous field loss (two with hemianopia, two with quadrantanopia). For researchers trying to understand recovery of vision after stroke and clinicians seeking to stratify patients into different treatment pathways, this approach combines multiple, convergent sources of data to characterize the extent of the stroke damage. We show that such an approach gives a more comprehensive measure of residual visual capacity-in two particular respects: which locations in the visual field should be targeted and what kind of visual attributes are most suited for rehabilitation.

Spinal cord atrophy predicts progressive disease in relapsing multiple sclerosis

Objective

A major challenge in multiple sclerosis (MS) research is the understanding of silent progression and Progressive MS. Using a novel method to accurately capture upper cervical cord area from legacy brain MRI scans we aimed to study the role of spinal cord and brain atrophy for silent progression and conversion to secondary progressive disease (SPMS).

Methods

From a single‐center observational study, all RRMS (n = 360) and SPMS (n = 47) patients and 80 matched controls were evaluated. RRMS patient subsets who converted to SPMS (n = 54) or silently progressed (n = 159), respectively, during the 12‐year observation period were compared to clinically matched RRMS patients remaining RRMS (n = 54) or stable (n = 147), respectively. From brain MRI, we assessed the value of brain and spinal cord measures to predict silent progression and SPMS conversion.

Results

Patients who developed SPMS showed faster cord atrophy rates (−2.19%/yr) at least 4 years before conversion compared to their RRMS matches (−0.88%/yr, p < 0.001). Spinal cord atrophy rates decelerated after conversion (−1.63%/yr, p = 0.010) towards those of SPMS patients from study entry (−1.04%). Each 1% faster spinal cord atrophy rate was associated with 69% (p < 0.0001) and 53% (p < 0.0001) shorter time to silent progression and SPMS conversion, respectively.

Interpretation

Silent progression and conversion to secondary progressive disease are predominantly related to cervical cord atrophy. This atrophy is often present from the earliest disease stages and predicts the speed of silent progression and conversion to Progressive MS. Diagnosis of SPMS is rather a late recognition of this neurodegenerative process than a distinct disease phase. ANN NEUROL 2022;91:268–281

Nonlinear manifold learning in functional magnetic resonance imaging uncovers a low-dimensional space of brain dynamics

Large-scale brain dynamics are believed to lie in a latent, low-dimensional space. Typically, the embeddings of brain scans are derived independently from different cognitive tasks or resting-state data, ignoring a potentially large-and shared-portion of this space. Here, we establish that a shared, robust, and interpretable low-dimensional space of brain dynamics can be recovered from a rich repertoire of task-based functional magnetic resonance imaging (fMRI) data. This occurs when relying on nonlinear approaches as opposed to traditional linear methods. The embedding maintains proper temporal progression of the tasks, revealing brain states and the dynamics of network integration. We demonstrate that resting-state data embeds fully onto the same task embedding, indicating similar brain states are present in both task and resting-state data. Our findings suggest analysis of fMRI data from multiple cognitive tasks in a low-dimensional space is possible and desirable.

Sex-Specific Abnormalities and Treatment-Related Plasticity of Subgenual Anterior Cingulate Cortex Functional Connectivity in Chronic Pain

The subgenual anterior cingulate cortex (sgACC) is a key node of the descending antinociceptive system with sex differences in its functional connectivity (FC). We previously reported that, in a male-prevalent chronic pain condition, sgACC FC is abnormal in women but not in men. This raises the possibility that, within a sex, sgACC FC may be either protective or represent a vulnerability to develop a sex-dominant chronic pain condition. The aim of this study was to characterize sgACC FC in a female-dominant chronic pain condition, carpal tunnel syndrome (CTS), to investigate whether sgACC abnormalities are a common feature in women with chronic pain or unique to individuals with pain conditions that are more prevalent in the opposite sex. We used fMRI to determine the resting state FC of the sgACC in healthy controls (HCs, n = 25, 18 women; 7 men) and people with CTS before (n = 25, 18 women; 7 men) and after (n = 17, 13 women; 4 men) successful surgical treatment. We found reduced sgACC FC with the medial pre-frontal cortex (mPFC) and temporal lobe in CTS compared with HCs. The group-level sgACC-mPFC FC abnormality was driven by men with CTS, while women with CTS did not have sgACC FC abnormalities compared with healthy women. We also found that age and sex influenced sgACC FC in both CTS and HCs, with women showing greater FC with bilateral frontal poles and men showing greater FC with the parietal operculum. After surgery, there was reduced sgACC FC with the orbitofrontal cortex, striatum, and premotor areas and increased FC with the posterior insula and precuneus compared with pre-op scans. Abnormally reduced sgACC-mPFC FC in men but not women with a female-prevalent chronic pain condition suggests pain-related sgACC abnormalities may not be specific to women but rather to individuals who develop chronic pain conditions that are more dominant in the opposite sex. Our data suggest the sgACC plays a role in chronic pain in a sex-specific manner, and its communication with other regions of the dynamic pain connectome undergoes plasticity following pain-relieving treatment, supporting it as a potential therapeutic target for neuromodulation in chronic pain.

White matter hyperintensity volumes are related to processing speed in long-term survivors of childhood cerebellar tumors

PURPOSE

Across several clinical populations, higher white matter hyperintensity (WMH) burden is consistently associated with decreases in cognitive performance, especially processing speed. Research of childhood cancer survivors has not utilized WMH quantification methodology to better understand the impact of WMH burden and its relationship with core cognitive skills. The present study aimed to quantify WMH volumes in a sample of long-term survivors of childhood cerebellar tumor and investigate the relationships with performance on a measure of oral processing speed. To further explore brain-behavior relationships, multivariate sparse canonical correlations was employed to identify WMH areas that predict processing speed performance.

METHODS

Thirty-five survivors and 56 healthy controls underwent neuroimaging and completed a measure of oral processing speed. The survivor group was further divided based on treatment (i.e., chemoradiation therapy (n = 20) vs. surgery only (n = 15)) to better understand the impact of treatment.

RESULTS

Survivors, and especially those treated with chemoradiation therapy, showed higher total WMH volumes and slower processing speed. Higher total WMH volumes were significantly associated with poorer processing speed (r = - 0.492, p = 0.003). Multivariate brain-behavior relationships revealed that periventricular WMHs were significantly associated with slower processing speed performance (p < 0.05).

CONCLUSION

Results exemplify that long-term survivors treated with and without chemoradiation therapy are at increased risk of developing higher WMH volumes compared to healthy peers. In addition, processing speed was robustly shown to be related to periventricular WMHs using an automated neuroimaging pipeline. This methodology to monitor WMH burden has the potential to be implemented efficiently with routine clinical neuroimaging of cancer survivors.

Virtual Connectomic Datasets in Alzheimer's Disease and Aging Using Whole-Brain Network Dynamics Modelling

Large neuroimaging datasets, including information about structural connectivity (SC) and functional connectivity (FC), play an increasingly important role in clinical research, where they guide the design of algorithms for automated stratification, diagnosis or prediction. A major obstacle is, however, the problem of missing features [e.g., lack of concurrent DTI SC and resting-state functional magnetic resonance imaging (rsfMRI) FC measurements for many of the subjects]. We propose here to address the missing connectivity features problem by introducing strategies based on computational whole-brain network modeling. Using two datasets, the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset and a healthy aging dataset, for proof-of-concept, we demonstrate the feasibility of virtual data completion (i.e., inferring “virtual FC” from empirical SC or “virtual SC” from empirical FC), by using self-consistent simulations of linear and nonlinear brain network models. Furthermore, by performing machine learning classification (to separate age classes or control from patient subjects), we show that algorithms trained on virtual connectomes achieve discrimination performance comparable to when trained on actual empirical data; similarly, algorithms trained on virtual connectomes can be used to successfully classify novel empirical connectomes. Completion algorithms can be combined and reiterated to generate realistic surrogate connectivity matrices in arbitrarily large number, opening the way to the generation of virtual connectomic datasets with network connectivity information comparable to the one of the original data.

Neural Substrates of Muscle Co-contraction during Dynamic Motor Adaptation

As we learn to perform a motor task with novel dynamics, the central nervous system must adapt motor commands and modify sensorimotor transformations. The objective of the current research is to identify the neural mechanisms underlying the adaptive process. It has been shown previously that an increase in muscle co-contraction is frequently associated with the initial phase of adaptation and that co-contraction is gradually reduced as performance improves. Our investigation focused on the neural substrates of muscle co-contraction during the course of motor adaptation using a resting-state fMRI approach in healthy human subjects of both genders. We analyzed the functional connectivity in resting-state networks during three phases of adaptation, corresponding to different muscle co-contraction levels and found that change in the strength of functional connectivity in one brain network was correlated with a metric of co-contraction, and in another with a metric of motor learning. We identified the cerebellum as the key component for regulating muscle co-contraction, especially its connection to the inferior parietal lobule, which was particularly prominent in early stage adaptation. A neural link between cerebellum, superior frontal gyrus and motor cortical regions was associated with reduction of co-contraction during later stages of adaptation. We also found reliable changes in the functional connectivity of a network involving primary motor cortex, superior parietal lobule and cerebellum that were specifically related to the motor learning.SIGNIFICANCE STATEMENT It is well known that co-contracting muscles is an effective strategy for providing postural stability by modulating mechanical impedance and thereby allowing the central nervous system to compensate for unfamiliar or unexpected physical conditions until motor commands can be appropriately adapted. The present study elucidates the neural substrates underlying the ability to modulate the mechanical impedance of a limb as we learn during motor adaptation. Using resting-state fMRI analysis we demonstrate that a distributed cerebellar-parietal-frontal network functions to regulate muscle co-contraction with the cerebellum as its key component.

Sex differences in brain modular organization in chronic pain

ABSTRACT

Men and women can exhibit different pain sensitivities, and many chronic pain conditions are more prevalent in one sex. Although there is evidence of sex differences in the brain, it is not known whether there are sex differences in the organization of large-scale functional brain networks in chronic pain. Here, we used graph theory with modular analysis and machine-learning of resting-state-functional magnetic resonance imaging data from 220 participants: 155 healthy controls and 65 individuals with chronic low back pain due to ankylosing spondylitis, a form of arthritis. We found an extensive overlap in the graph partitions with the major brain intrinsic systems (ie, default mode, central, visual, and sensorimotor modules), but also sex-specific network topological characteristics in healthy people and those with chronic pain. People with chronic pain exhibited higher cross-network connectivity, and sex-specific nodal graph properties changes (ie, hub disruption), some of which were associated with the severity of the chronic pain condition. Females exhibited atypically higher functional segregation in the mid cingulate cortex and subgenual anterior cingulate cortex and lower connectivity in the network with the default mode and frontoparietal modules, whereas males exhibited stronger connectivity with the sensorimotor module. Classification models on nodal graph metrics could classify an individual's sex and whether they have chronic pain with high accuracies (77%-92%). These findings highlight the organizational abnormalities of resting-state-brain networks in people with chronic pain and provide a framework to consider sex-specific pain therapeutics.

Abnormal subgenual anterior cingulate circuitry is unique to women but not men with chronic pain

The subgenual anterior cingulate cortex (sgACC) plays an important role in pain modulation. We previously demonstrated sex differences in sgACC functional connectivity (FC) in healthy individuals. Given that many chronic pain conditions show sex differences in prevalence, here we tested the hypothesis that people with chronic pain exhibit a sex-specific pattern of abnormal sgACC FC. We acquired resting-state functional magnetic resonance imaging data from 156 (82 W: 74 M) healthy participants and 38 (19 W: 19 M) people with chronic low back pain resulting from ankylosing spondylitis, a condition that predominantly affects men. We confirmed that there are sex differences in sgACC FC in our large cohort of healthy adults; women had greater sgACC FC with the precuneus, a key node of the default mode network, and men had greater sgACC FC with the posterior insula and the operculum. Next, we identified an interaction effect between sex and pain status (healthy/chronic pain) for sgACC FC. Within the chronic pain group, women had greater sgACC FC than men to the default mode and sensorimotor networks. Compared to healthy women, women with chronic pain also had greater sgACC FC to the precuneus and lower FC to the hippocampus and frontal regions. No differences in sgACC FC were seen in men with vs without chronic pain. Our findings indicate that abnormal sgACC circuitry is unique to women but not men with ankylosing spondylitis-related chronic pain. These sex differences may impact the benefit of therapeutics that target the sgACC for chronic pain.

Neural Development of Speech Sensorimotor Learning

The development of the human brain continues through to early adulthood. It has been suggested that cortical plasticity during this protracted period of development shapes circuits in associative transmodal regions of the brain. Here we considered how cortical plasticity during development might contribute to the coordinated brain activity required for speech motor learning. Specifically, we examined patterns of brain functional connectivity (FC), whose strength covaried with the capacity for speech audio-motor adaptation in children ages 5-12 and in young adults of both sexes. Children and adults showed distinct patterns of the encoding of learning in the brain. Adult performance was associated with connectivity in transmodal regions that integrate auditory and somatosensory information, whereas children rely on basic somatosensory and motor circuits. A progressive reliance on transmodal regions is consistent with human cortical development and suggests that human speech motor adaptation abilities are built on cortical remodeling, which is observable in late childhood and is stabilized in adults.SIGNIFICANCE STATEMENT A protracted period of neuro plasticity during human development is associated with extensive reorganization of associative cortex. We examined how the relationship between FC and speech motor learning capacity are reconfigured in conjunction with this cortical reorganization. Young adults and children aged 5-12 years showed distinctly different patterns. Mature brain networks related to learning included associative cortex, which integrates auditory and somatosensory feedback in speech, whereas the immature networks in children included motor regions of the brain. These patterns are consistent with the cortical reorganization that is initiated in late childhood. The result provides insights into the human biology of speech as well as to the mature neural mechanisms for multisensory integration in motor learning.

Functional Connectivity and Response Inhibition: A Secondary Analysis of an 8-Week Randomized Controlled Trial of Computerized Cognitive Training

BACKGROUND

Evidence suggests that computerized cognitive training (CCT) can improve cognitive function in older adults, particularly executive functions. However, the underlying mechanisms by which CCT may improve executive functions are not well established.

OBJECTIVE

To determine: 1) inter-network functional connectivity correlates of changes in executive functions; and 2) the effect of CCT on these functional connectivity correlates.

METHODS

This secondary analysis included a subset of 124 adults aged 65-85 years enrolled in an 8-week randomized controlled trial of CCT. Participants were randomized to either: 1) group-based CCT 3x/week for 1 hour plus 3x/week home-based training; 2) group-based CCT preceded by brisk walking (Ex+CCT) 3x/week for 1 hour plus 3x/week home-based training; or 3) group-based balanced and toned (BAT) classes 3x/week for 1 hour (control). At baseline and trial completion, 65 of the 124 participants completed resting-state functional magnetic resonance imaging and neuropsychological tests of executive functions, specifically the Stroop Colour-Word Test and Flanker Test.

RESULTS

Improved performance on the Stroop Colour-Word Test and Flanker Test were associated with decreased correlation between the default mode network (DMN) and the fronto-parietal network (FPN) (p < 0.05). Compared with BAT, CCT alone significantly decreased correlation between the left dorsolateral prefrontal cortex and both the left and right medial temporal gyrus (-0.143, 95%CI [-0.256,-0.030], p = 0.014, and -0.123, 95%CI [-0.242,-0.004], p = 0.043, respectively).

CONCLUSION

Decreased correlation between DMN and FPN, indicating less connection between these networks, may be an underlying mechanism by which CCT improves executive functions. Future studies are needed to replicate this finding.

Toward a global and reproducible science for brain imaging in neurotrauma: the ENIGMA adult moderate/severe traumatic brain injury working group

The global burden of mortality and morbidity caused by traumatic brain injury (TBI) is significant, and the heterogeneity of TBI patients and the relatively small sample sizes of most current neuroimaging studies is a major challenge for scientific advances and clinical translation. The ENIGMA (Enhancing NeuroImaging Genetics through Meta-Analysis) Adult moderate/severe TBI (AMS-TBI) working group aims to be a driving force for new discoveries in AMS-TBI by providing researchers world-wide with an effective framework and platform for large-scale cross-border collaboration and data sharing. Based on the principles of transparency, rigor, reproducibility and collaboration, we will facilitate the development and dissemination of multiscale and big data analysis pipelines for harmonized analyses in AMS-TBI using structural and functional neuroimaging in combination with non-imaging biomarkers, genetics, as well as clinical and behavioral measures. Ultimately, we will offer investigators an unprecedented opportunity to test important hypotheses about recovery and morbidity in AMS-TBI by taking advantage of our robust methods for large-scale neuroimaging data analysis. In this consensus statement we outline the working group's short-term, intermediate, and long-term goals.

Feasibility of Auricular Field Stimulation in Fibromyalgia: Evaluation by Functional Magnetic Resonance Imaging, Randomized Trial

OBJECTIVE

To evaluate the feasibility of recruitment, preliminary efficacy, and acceptability of auricular percutaneous electrical nerve field stimulation (PENFS) for the treatment of fibromyalgia in veterans, using neuroimaging as an outcome measure and a biomarker of treatment response.

DESIGN

Randomized, controlled, single-blind.

SETTING

Government hospital.

SUBJECTS

Twenty-one veterans with fibromyalgia were randomized to standard therapy (ST) control or ST with auricular PENFS treatment.

METHODS

Participants received weekly visits with a pain practitioner over 4 weeks. The PENFS group received reapplication of PENFS at each weekly visit. Resting-state functional connectivity magnetic resonance imaging (rs-fcMRI) data were collected within 2 weeks prior to initiating treatment and 2 weeks following the final treatment. Analysis of rs-fcMRI used a right posterior insula seed. Pain and function were assessed at baseline and at 2, 6, and 12 weeks post-treatment.

RESULTS

At 12 weeks post-treatment, there was a nonsignificant trend toward improved pain scores and significant improvements in pain interference with sleep among the PENFS treatment group as compared with the ST controls. Neuroimaging data displayed increased connectivity to areas of the cerebellum and executive control networks in the PENFS group as compared with the ST control group following treatment.

CONCLUSIONS

There was a trend toward improved pain and function among veterans with fibromyalgia in the ST + PENFS group as compared with the ST control group. Pain and functional outcomes correlated with altered rs-fcMRI network connectivity. Neuroimaging results differed between groups, suggesting an alternative underlying mechanism for PENFS analgesia.

Real time and delayed effects of subcortical low intensity focused ultrasound

Deep brain nuclei are integral components of large-scale circuits mediating important cognitive and sensorimotor functions. However, because they fall outside the domain of conventional non-invasive neuromodulatory techniques, their study has been primarily based on neuropsychological models, limiting the ability to fully characterize their role and to develop interventions in cases where they are damaged. To address this gap, we used the emerging technology of non-invasive low-intensity focused ultrasound (LIFU) to directly modulate left lateralized basal ganglia structures in healthy volunteers. During sonication, we observed local and distal decreases in blood oxygenation level dependent (BOLD) signal in the targeted left globus pallidus (GP) and in large-scale cortical networks. We also observed a generalized decrease in relative perfusion throughout the cerebrum following sonication. These results show, for the first time using functional MRI data, the ability to modulate deep-brain nuclei using LIFU while measuring its local and global consequences, opening the door for future applications of subcortical LIFU.

MR Elastography demonstrates reduced white matter shear stiffness in early-onset hydrocephalus

INTRODUCTION

Hydrocephalus that develops early in life is often accompanied by developmental delays, headaches and other neurological deficits, which may be associated with changes in brain shear stiffness. However, noninvasive approaches to measuring stiffness are limited. Magnetic Resonance Elastography (MRE) of the brain is a relatively new noninvasive imaging method that provides quantitative measures of brain tissue stiffness. Herein, we aimed to use MRE to assess brain stiffness in hydrocephalus patients compared to healthy controls, and to assess its associations with ventricular size, as well as demographic, shunt-related and clinical outcome measures.

METHODS

MRE was collected at two imaging sites in 39 hydrocephalus patients and 33 healthy controls, along with demographic, shunt-related, and clinical outcome measures including headache and quality of life indices. Brain stiffness was quantified for whole brain, global white matter (WM), and lobar WM stiffness. Group differences in brain stiffness between patients and controls were compared using two-sample t-tests and multivariable linear regression to adjust for age, sex, and ventricular volume. Among patients, multivariable linear or logistic regression was used to assess which factors (age, sex, ventricular volume, age at first shunt, number of shunt revisions) were associated with brain stiffness and whether brain stiffness predicts clinical outcomes (quality of life, headache and depression).

RESULTS

Brain stiffness was significantly reduced in patients compared to controls, both unadjusted (p ≤ 0.002) and adjusted (p ≤ 0.03) for covariates. Among hydrocephalic patients, lower stiffness was associated with older age in temporal and parietal WM and whole brain (WB) (beta (SE): -7.6 (2.5), p = 0.004; -9.5 (2.2), p = 0.0002; -3.7 (1.8), p = 0.046), being female in global and frontal WM and WB (beta (SE): -75.6 (25.5), p = 0.01; -66.0 (32.4), p = 0.05; -73.2 (25.3), p = 0.01), larger ventricular volume in global, and occipital WM (beta (SE): -11.5 (3.4), p = 0.002; -18.9 (5.4), p = 0.0014). Lower brain stiffness also predicted worse quality of life and a higher likelihood of depression, controlling for all other factors.

CONCLUSIONS

Brain stiffness is reduced in hydrocephalus patients compared to healthy controls, and is associated with clinically-relevant functional outcome measures. MRE may emerge as a clinically-relevant biomarker to assess the neuropathological effects of hydrocephalus and shunting, and may be useful in evaluating the effects of therapeutic alternatives, or as a supplement, of shunting.

Automatic segmentation of white matter hyperintensities from brain magnetic resonance images in the era of deep learning and big data - A systematic review

BACKGROUND

White matter hyperintensities (WMH), of presumed vascular origin, are visible and quantifiable neuroradiological markers of brain parenchymal change. These changes may range from damage secondary to inflammation and other neurological conditions, through to healthy ageing. Fully automatic WMH quantification methods are promising, but still, traditional semi-automatic methods seem to be preferred in clinical research. We systematically reviewed the literature for fully automatic methods developed in the last five years, to assess what are considered state-of-the-art techniques, as well as trends in the analysis of WMH of presumed vascular origin.

METHOD

We registered the systematic review protocol with the International Prospective Register of Systematic Reviews (PROSPERO), registration number - CRD42019132200. We conducted the search for fully automatic methods developed from 2015 to July 2020 on Medline, Science direct, IEE Explore, and Web of Science. We assessed risk of bias and applicability of the studies using QUADAS 2.

RESULTS

The search yielded 2327 papers after removing 104 duplicates. After screening titles, abstracts and full text, 37 were selected for detailed analysis. Of these, 16 proposed a supervised segmentation method, 10 proposed an unsupervised segmentation method, and 11 proposed a deep learning segmentation method. Average DSC values ranged from 0.538 to 0.91, being the highest value obtained from an unsupervised segmentation method. Only four studies validated their method in longitudinal samples, and eight performed an additional validation using clinical parameters. Only 8/37 studies made available their methods in public repositories.

CONCLUSIONS

We found no evidence that favours deep learning methods over the more established k-NN, linear regression and unsupervised methods in this task. Data and code availability, bias in study design and ground truth generation influence the wider validation and applicability of these methods in clinical research.

Virtual brain grafting: Enabling whole brain parcellation in the presence of large lesions

Brain atlases and templates are at the heart of neuroimaging analyses, for which they facilitate multimodal registration, enable group comparisons and provide anatomical reference. However, as atlas-based approaches rely on correspondence mapping between images they perform poorly in the presence of structural pathology. Whilst several strategies exist to overcome this problem, their performance is often dependent on the type, size and homogeneity of any lesions present. We therefore propose a new solution, referred to as Virtual Brain Grafting (VBG), which is a fully-automated, open-source workflow to reliably parcellate magnetic resonance imaging (MRI) datasets in the presence of a broad spectrum of focal brain pathologies, including large, bilateral, intra- and extra-axial, heterogeneous lesions with and without mass effect. The core of the VBG approach is the generation of a lesion-free T1-weighted image, which enables further image processing operations that would otherwise fail. Here we validated our solution based on Freesurfer recon-all parcellation in a group of 10 patients with heterogeneous gliomatous lesions, and a realistic synthetic cohort of glioma patients (n = 100) derived from healthy control data and patient data. We demonstrate that VBG outperforms a non-VBG approach assessed qualitatively by expert neuroradiologists and Mann-Whitney U tests to compare corresponding parcellations (real patients U(6,6) = 33, z = 2.738, P < .010, synthetic-patients U(48,48) = 2076, z = 7.336, P < .001). Results were also quantitatively evaluated by comparing mean dice scores from the synthetic-patients using one-way ANOVA (unilateral VBG = 0.894, bilateral VBG = 0.903, and non-VBG = 0.617, P < .001). Additionally, we used linear regression to show the influence of lesion volume, lesion overlap with, and distance from the Freesurfer volumes of interest, on labeling accuracy. VBG may benefit the neuroimaging community by enabling automated state-of-the-art MRI analyses in clinical populations using methods such as FreeSurfer, CAT12, SPM, Connectome Workbench, as well as structural and functional connectomics. To fully maximize its availability, VBG is provided as open software under a Mozilla 2.0 license (https://github.com/KUL-Radneuron/KUL_VBG).

Lipid-suppressed and tissue-fraction corrected metabolic distributions in human central brain structures using 2D 1 H magnetic resonance spectroscopic imaging at 7 T

INTRODUCTION

Magnetic resonance spectroscopic imaging (MRSI) has the potential to add a layer of understanding of the neurobiological mechanisms underlying brain diseases, disease progression, and treatment efficacy. Limitations related to metabolite fitting of low signal-to-noise ratios data, signal variations due to partial-volume effects, acquisition and extracranial lipid artifacts, along with clinically relevant aspects such as scan time constraints, are among the challenges associated with in vivo MRSI.

METHODS

The aim of this work was to address some of these factors and to develop an acquisition, reconstruction, and postprocessing pipeline to derive lipid-suppressed metabolite values of central brain structures based on free-induction decay measurements made using a 7 T MR scanner. Anatomical images were used to perform high-resolution (1 mm³ ) partial-volume correction to account for gray matter, white matter (WM), and cerebral-spinal fluid signal contributions. Implementation of automatic quality control thresholds and normalization of metabolic maps from 23 subjects to the Montreal Neurological Institute (MNI) standard atlas facilitated the creation of high-resolution average metabolite maps of several clinically relevant metabolites in central brain regions, while accounting for macromolecular distributions. Partial-volume correction improved the delineation of deep brain nuclei. We report average metabolite values including glutamate + glutamine (Glx), glycerophosphocholine, choline and phosphocholine (tCho), (phospo)creatine, myo-inositol and glycine (mI-Gly), glutathione, N-acetyl-aspartyl glutamate(and glutamine), and N-acetyl-aspartate in the basal ganglia, central WM (thalamic radiation, corpus callosum) as well as insular cortex and intracalcarine sulcus.

CONCLUSION

MNI-registered average metabolite maps facilitate group-based analysis, thus offering the possibility to mitigate uncertainty in variable MRSI data.

Multiplex Networks to Characterize Seizure Development in Traumatic Brain Injury Patients

Traumatic brain injury (TBI) may cause secondary debilitating problems, such as post-traumatic epilepsy (PTE), which occurs with unprovoked recurrent seizures, months or even years after TBI. Currently, the Epilepsy Bioinformatics Study for Antiepileptogenic Therapy (EpiBioS4Rx) has been enrolling moderate-severe TBI patients with the goal to identify biomarkers of epileptogenesis that may help to prevent seizure occurrence and better understand the mechanism underlying PTE. In this work, we used a novel complex network approach based on segmenting T1-weighted Magnetic Resonance Imaging (MRI) scans in patches of the same dimension (network nodes) and measured pairwise patch similarities using Pearson's correlation (network connections). This network model allowed us to obtain a series of single and multiplex network metrics to comprehensively analyze the different interactions between brain components and capture structural MRI alterations related to seizure development. We used these complex network features to train a Random Forest (RF) classifier and predict, with an accuracy of 70 and a 95% confidence interval of [67, 73%], which subjects from EpiBioS4Rx have had at least one seizure after a TBI. This complex network approach also allowed the identification of the most informative scales and brain areas for the discrimination between the two clinical groups: seizure-free and seizure-affected subjects, demonstrating to be a promising pilot study which, in the future, may serve to identify and validate biomarkers of PTE.

A method to mitigate spatio-temporally varying task-correlated motion artifacts from overt-speech fMRI paradigms in aphasia

Quantifying accurate functional magnetic resonance imaging (fMRI) activation maps can be dampened by spatio‐temporally varying task‐correlated motion (TCM) artifacts in certain task paradigms (e.g., overt speech). Such real‐world tasks are relevant to characterize longitudinal brain reorganization poststroke, and removal of TCM artifacts is vital for improved clinical interpretation and translation. In this study, we developed a novel independent component analysis (ICA)‐based approach to denoise spatio‐temporally varying TCM artifacts in 14 persons with aphasia who participated in an overt language fMRI paradigm. We compared the new methodology with other existing approaches such as “standard” volume registration, nonselective motion correction ICA packages (i.e., AROMA), and combining the novel approach with AROMA. Results show that the proposed methodology outperforms other approaches in removing TCM‐related false positive activity (i.e., improved detectability power) with high spatial specificity. The proposed method was also effective in maintaining a balance between removal of TCM‐related trial‐by‐trial variability and signal retention. Finally, we show that the TCM artifact is related to clinical metrics, such as speech fluency and aphasia severity, and the implication of TCM denoising on such relationship is also discussed. Overall, our work suggests that routine bulkhead motion based denoising packages cannot effectively account for spatio‐temporally varying TCM. Further, the proposed TCM denoising approach requires a one‐time front‐end effort to hand label and train the classifiers that can be cost‐effectively utilized to denoise large clinical data sets.

Behavioral and brain signatures of substance use vulnerability in childhood

The prevalence of risky behavior such as substance use increases during adolescence; however, the neurobiological precursors to adolescent substance use remain unclear. Predictive modeling may complement previous work observing associations with known risk factors or substance use outcomes by developing generalizable models that predict early susceptibility. The aims of the current study were to identify and characterize behavioral and brain models of vulnerability to future substance use. Principal components analysis (PCA) of behavioral risk factors were used together with connectome-based predictive modeling (CPM) during rest and task-based functional imaging to generate predictive models in a large cohort of nine- and ten-year-olds enrolled in the Adolescent Brain & Cognitive Development (ABCD) study (NDA release 2.0.1). Dimensionality reduction (n = 9,437) of behavioral measures associated with substance use identified two latent dimensions that explained the largest amount of variance: risk-seeking (PC1; e.g., curiosity to try substances) and familial factors (PC2; e.g., family history of substance use disorder). Using cross-validated regularized regression in a subset of data (Year 1 Fast Track data; n>1,500), functional connectivity during rest and task conditions (resting-state; monetary incentive delay task; stop signal task; emotional n-back task) significantly predicted individual differences in risk-seeking (PC1) in held-out participants (partial correlations between predicted and observed scores controlling for motion and number of frames [r_p]: 0.07-0.21). By contrast, functional connectivity was a weak predictor of familial risk factors associated with substance use (PC2) (r_p: 0.03-0.06). These results demonstrate a novel approach to understanding substance use vulnerability, which—together with mechanistic perspectives—may inform strategies aimed at early identification of risk for addiction.

Early brain biomarkers of post-traumatic seizures: initial report of the multicentre epilepsy bioinformatics study for antiepileptogenic therapy (EpiBioS4Rx) prospective study

BACKGROUND

Traumatic brain injury (TBI) causes early seizures and is the leading cause of post-traumatic epilepsy. We prospectively assessed structural imaging biomarkers differentiating patients who develop seizures secondary to TBI from patients who do not.

DESIGN

Multicentre prospective cohort study starting in 2018. Imaging data are acquired around day 14 post-injury, detection of seizure events occurred early (within 1 week) and late (up to 90 days post-TBI).

RESULTS

From a sample of 96 patients surviving moderate-to-severe TBI, we performed shape analysis of local volume deficits in subcortical areas (analysable sample: 57 patients; 35 no seizure, 14 early, 8 late) and cortical ribbon thinning (analysable sample: 46 patients; 29 no seizure, 10 early, 7 late). Right hippocampal volume deficit and inferior temporal cortex thinning demonstrated a significant effect across groups. Additionally, the degree of left frontal and temporal pole thinning, and clinical score at the time of the MRI, could differentiate patients experiencing early seizures from patients not experiencing them with 89% accuracy.

CONCLUSIONS AND RELEVANCE

Although this is an initial report, these data show that specific areas of localised volume deficit, as visible on routine imaging data, are associated with the emergence of seizures after TBI.

Neural sensitivity to phonological characteristics is stable across the lifespan

Aging is often associated with declines in language production. For example, compared to younger adults, older adults experience more tip-of-the-tongue (TOT) states, show decreased speed and accuracy in naming objects, and have more pauses and fillers in speech, all of which indicate age-related increases in retrieval difficulty. While prior work has suggested that retrieval difficulty may be phonologically based, it is unclear whether there are age-related differences in the organization of phonological information per se or whether age-related difficulties may arise from accessing that information. Here we used fMRI to investigate the neural and behavioral basis of phonological neighborhood denisty (PND) effects on picture naming across the lifespan (N=91, ages 20-75). Consistent with prior work, behavioral results revealed that higher PND led to faster picture naming times and higher accuracies overall, and that older adults were less accurate in their responses. Consistent with the behavioral analyses, fMRI analyses showed that increasing PND was associated with decreased activation in auditory and motor language regions, including bilateral superior temporal gyri and bilateral precentral gyri. Interestingly, although there were age-related increases in functional activation to picture naming, there were no age-related modulations of neural sensitivity to PND. Overall, these results suggest that having a large cohort of phonological neighbors facilitates language production, and although aging is associated with increases in language production difficulty, sensitivity to phonological features during language production is stable across the lifespan.

The subcortical basis of outcome and cognitive impairment in TBI: A longitudinal cohort study

OBJECTIVE

To understand how, biologically, the acute event of traumatic brain injury gives rise to a long-term disease, we address the relationship between evolving cortical and subcortical brain damage and measures of functional outcome and cognitive functioning at 6 months after injury.

METHODS

For this longitudinal analysis, clinical and MRI data were collected in a tertiary neurointensive care setting in a continuous sample of 157 patients surviving moderate to severe traumatic brain injury between 2000 and 2018. For each patient, we collected T1- and T2-weighted MRI data acutely and at the 6-month follow-up, as well as acute measures of injury severity (Glasgow Coma Scale), follow-up measures of functional impairment (Glasgow Outcome Scale-extended), and, in a subset of patients, neuropsychological measures of attention, executive functions, and episodic memory.

RESULTS

In the final cohort of 113 subcortical and 92 cortical datasets that survived (blind) quality control, extensive atrophy was observed over the first 6 months after injury across the brain. However, only atrophy within subcortical regions, particularly in the left thalamus, was associated with functional outcome and neuropsychological measures of attention, executive functions, and episodic memory. Furthermore, when brought together in an analytical model, longitudinal brain measurements could distinguish good from bad outcome with 90% accuracy, whereas acute brain and clinical measurements alone could achieve only 20% accuracy.

CONCLUSION

Despite great injury heterogeneity, secondary thalamic pathology is a measurable minimum common denominator mechanism directly relating biology to clinical measures of outcome and cognitive functioning, potentially linking the acute event and the longer-term disease of traumatic brain injury.

Subcortical atrophy correlates with the perturbational complexity index in patients with disorders of consciousness

BACKGROUND

The complexity of neurophysiological brain responses to direct cortical stimulation, referred to as the perturbational complexity index (PCI), has been shown able to discriminate between consciousness and unconsciousness in patients surviving severe brain injury as well as several other conditions (e.g., wake, dreamless sleep, sleep and ketamine dreaming, anesthesia).

OBJECTIVE

This study asks whether, in patients with a disorder of consciousness (DOC), the complexity of the neurophysiological response to cortical stimulation is preferentially associated with atrophy within specific brain structures.

METHODS

We perform a retrospective analysis of 40 DOC patients and correlate their maximal PCI to MR-based measurements of cortical thinning and subcortical atrophy.

RESULTS

PCI was systematically and inversely associated with the degree of local atrophy within the globus pallidus, a region previously linked to electrocortical and behavioral arousal. Conversely, we fail to detect any association between variance in cortical ribbon thickness and PCI.

CONCLUSION

These findings corroborate the previously reported association between pallidal atrophy and low behavioral arousal and suggest that this region's role in maintaining the overall balance of excitation and inhibition may critically affect the emergence of complex cortical interactions in chronic disorders of consciousness. This finding thus also suggests a target for potential neuromodulatory intervention in DOC patients.

Resting-state brain and spinal cord networks in humans are functionally integrated

In the absence of any task, both the brain and spinal cord exhibit spontaneous intrinsic activity organised in a set of functionally relevant neural networks. However, whether such resting-state networks (RSNs) are interconnected across the brain and spinal cord is unclear. Here, we used a unique scanning protocol to acquire functional images of both brain and cervical spinal cord (CSC) simultaneously and examined their spatiotemporal correspondence in humans. We show that the brain and spinal cord activities are strongly correlated during rest periods, and specific spinal cord regions are functionally linked to consistently reported brain sensorimotor RSNs. The functional organisation of these networks follows well-established anatomical principles, including the contralateral correspondence between the spinal hemicords and brain hemispheres as well as sensory versus motor segregation of neural pathways along the brain–spinal cord axis. Thus, our findings reveal a unified functional organisation of sensorimotor networks in the entire central nervous system (CNS) at rest.

This neuroimaging study reveals novel insights into the functional organization of resting-state networks in the brain and spinal cord, such as the contralateral correspondence between the two halves of the brain and spinal cord, and segregation of sensory versus motor neural pathways along this axis.

Parietal Perfusion Alterations in Parkinson's Disease Patients Without Dementia

Fronto-parietal regions are involved in cognitive processes that are commonly affected in Parkinson's disease (PD). The aims of this study were to investigate cerebral blood flow (CBF) and gray matter (GM) volume within the regions belonging to the fronto-parietal circuit in people with PD (pwPD) without dementia, and to assess their association with cognitive performance. Twenty-seven pwPD without dementia (mean [SD] age = 67.4 [8.1] years, 20 males, mean [SD] Montreal Cognitive Assessment, MoCA score = 24.2 [2.9], median [IQR] Hoehn and Yahr scale = 1.5 [1–2]) and twenty-six age- and sex-matched healthy controls (HC) were scanned with arterial spin labeling (ASL) and T1-weighted magnetic resonance imaging (MRI) sequences to investigate CBF and GM volume, respectively. The cognitive performance of the enrolled pwPD was assessed with MoCA, Trail Making Test (TMT, part A, B, B-A), phonemic fluency and semantic fluency tests. The scores were adjusted for age and education. After standard preprocessing, CBF differences between pwPD and HC were tested with a voxel-wise approach. Voxel-based morphometry was used to compare pwPD and HC in terms of GM volume. Both voxel-wise comparisons between pwPD and HC were restricted to regions of the fronto-parietal circuit. The following additional voxel-wise analyses were performed within regions showing either perfusion or GM volume alterations: (1) correlation with neuropsychological test scores; (2) subgroup comparison after median split on each neuropsychological test score. Family-wise error-corrected (FWE) p-values lower than 0.05 were considered significant. Significant hypoperfusion was identified in the left inferior parietal lobule (IPL, p_peak = 0.037) and in the bilateral superior parietal lobule (SPL, left hemisphere: p_peak = 0.037; right hemisphere: p_peak = 0.049) of pwPD when compared to HC. No significant GM atrophy was observed. Local hypoperfusion did not correlate with any neuropsychological test scores. However, significantly lower CBF was observed in the left SPL and IPL of the pwPD subgroup who performed poorer on TMT part A in comparison with the pwPD subgroup that performed better. Perfusion alterations may occur in parietal regions of pwPD without dementia, and may be associated with lower visuomotor skills. Parietal CBF may be considered as a suitable early biomarker for longitudinal studies investigating cognitive decline in PD.

A systematic investigation of the association between network dynamics in the human brain and the state of consciousness

An increasing amount of studies suggest that brain dynamics measured with resting-state functional magnetic resonance imaging (fMRI) are related to the state of consciousness. However, the challenge of investigating neuronal correlates of consciousness is the confounding interference between (recovery of) consciousness and behavioral responsiveness. To address this issue, and validate the interpretation of prior work linking brain dynamics and consciousness, we performed a longitudinal fMRI study in patients recovering from coma. Patients were assessed twice, 6 months apart, and assigned to one of two groups. One group included patients who were unconscious at the first assessment but regained consciousness and improved behavioral responsiveness by the second assessment. The other group included patients who were already conscious and improved only behavioral responsiveness. While the two groups were matched in terms of the average increase in behavioral responsiveness, only one group experienced a categorical change in their state of consciousness allowing us to partially dissociate consciousness and behavioral responsiveness. We find the variance in network metrics to be systematically different across states of consciousness, both within and across groups. Specifically, at the first assessment, conscious patients exhibited significantly greater variance in network metrics than unconscious patients, a difference that disappeared once all patients had recovered consciousness. Furthermore, we find a significant increase in dynamics for patients who regained consciousness over time, but not for patients who only improved responsiveness. These findings suggest that changes in brain dynamics are indeed linked to the state of consciousness and not just to a general level of behavioral responsiveness.

Evaluating the granularity and statistical structure of lesions and behaviour in post-stroke aphasia

The pursuit of relating the location of neural damage to the pattern of acquired language and general cognitive deficits post-stroke stems back to the 19th century behavioural neurology. While spatial specificity has improved dramatically over time, from the large areas of damage specified by post-mortem investigation to the millimetre precision of modern MRI, there is an underlying issue that is rarely addressed, which relates to the fact that damage to a given area of the brain is not random but constrained by the brain’s vasculature. Accordingly, the aim of this study was to uncover the statistical structure underlying the lesion profile in chronic aphasia post-stroke. By applying varimax-rotated principal component analysis to the lesions of 70 patients with chronic post-stroke aphasia, we identified 17 interpretable clusters, largely reflecting the vascular supply of middle cerebral artery sub-branches and other sources of individual variation in vascular supply as shown in classical angiography studies. This vascular parcellation produced smaller displacement error in simulated lesion–symptom analysis compared with individual voxels and Brodmann regions. A second principal component analysis of the patients’ detailed neuropsychological data revealed a four-factor solution reflecting phonological, semantic, executive-demand and speech fluency abilities. As a preliminary exploration, stepwise regression was used to relate behavioural factor scores to the lesion principal components. Phonological ability was related to two components, which covered the posterior temporal region including the posterior segment of the arcuate fasciculus, and the inferior frontal gyrus. Three components were linked to semantic ability and were located in the white matter underlying the anterior temporal lobe, the supramarginal gyrus and angular gyrus. Executive-demand related to two components covering the dorsal edge of the middle cerebral artery territory, while speech fluency was linked to two components that were located in the middle frontal gyrus, precentral gyrus and subcortical regions (putamen and thalamus). Future studies can explore in formal terms the utility of these principal component analysis-derived lesion components for relating post-stroke lesions and symptoms.

Correcting Task fMRI Signals for Variability in Baseline CBF Improves BOLD-Behavior Relationships: A Feasibility Study in an Aging Model

Blood Oxygen Level Dependent (BOLD) functional MRI is a complex neurovascular signal whose magnitude depends on baseline physiological factors such as cerebral blood flow (CBF). Because baseline CBF varies across the brain and is altered with aging, the interpretation of stand-alone aging-related BOLD changes can be misleading. The primary objective of this study was to develop a methodology that combines task fMRI and arterial spin labeling (ASL) techniques to sensitize task-induced BOLD activity by covarying out the baseline physiology (i.e., CBF) in an aging model. We recruited 11 younger and 13 older healthy participants who underwent ASL and an overt language fMRI task (semantic category member generation). We measured in-scanner language performance to investigate the effect of BOLD sensitization on BOLD-behavior relationships. The results demonstrate that our correction approach is effective at enhancing the specificity and sensitivity of the BOLD signal in both groups. In addition, the correction strengthens the statistical association between task BOLD activity and behavioral performance. Although CBF has inherent age dependence, our results show that retaining the age factor within CBF aides in greater sensitization of task fMRI signals. From a cognitive standpoint, compared to young adults, the older participants showed a delayed domain-general language-related task activity possibly due to compromised vessel compliance. Further, assessment of functional evolution of corrected BOLD activity revealed biphasic BOLD dynamics in both groups where BOLD deactivation may reflect greater semantic demand or increased premium on domain general executive functioning in response to task difficulty. Although it was promising to note that the predictability of behavior using the proposed methodology outperforms other methodologies (i.e., no correction and normalization by division), and provides moderate stability and adequate power, further work with a larger cohort and other task designs is necessary to improve the stability of predicting associated behavior. In summary, we recommend correction of task fMRI signals by covarying out baseline CBF especially when comparing groups with different neurovascular properties. Given that ASL and BOLD fMRI are well established and widely employed techniques, our proposed multi-modal methodology can be readily implemented into data processing pipelines to obtain more accurate BOLD activation maps.

White matter characteristics of motor, sensory and interhemispheric tracts underlying impaired upper limb function in children with unilateral cerebral palsy

This study explored the role of lesion timing (periventricular white matter versus cortical and deep grey matter lesions) and type of corticospinal tract (CST) wiring pattern (contralateral, bilateral, ipsilateral) on white matter characteristics of the CST, medial lemniscus, superior thalamic radiations and sensorimotor transcallosal fibers in children with unilateral cerebral palsy (CP), and examined the association with upper limb function. Thirty-four children (mean age 10 years 7 months ± 2 years 3 months) with unilateral CP underwent a comprehensive upper limb evaluation and diffusion weighted imaging (75 directions, b value 2800). Streamline count, fractional anisotropy and mean diffusivity were extracted from the targeted tracts and asymmetry indices were additionally calculated. Transcranial magnetic stimulation was applied to assess the CST wiring pattern. Results showed a more damaged CST in children with cortical and deep grey matter lesions (N = 10) and ipsilateral CST projections (N = 11) compared to children with periventricular white matter lesions (N = 24; p < 0.02) and contralateral CST projections (N = 9; p < 0.025), respectively. Moderate to high correlations were found between diffusion metrics of the targeted tracts and upper limb function (r = 0.45-0.72; p < 0.01). Asymmetry indices of the CST and sensory tracts could best explain bimanual performance (74%, p < 0.0001) and unimanual capacity (50%, p = 0.004). Adding lesion timing and CST wiring pattern did not further improve the model of bimanual performance, while for unimanual capacity lesion timing was additionally retained (58%, p = 0.0002). These results contribute to a better understanding of the underlying neuropathology of upper limb function in children with unilateral CP and point towards a clinical potential of tractography.

Lexical frequency affects functional activation and accuracy in picture naming among older and younger adults

As individuals age, they experience increased difficulties producing speech, especially with infrequent words. Older adults report that word retrieval difficulties frequently occur and are highly frustrating. However, little is known about how age affects the neural basis of language production. Moreover, age-related increases in brain activation are often observed, yet there is disagreement about whether such increases represent a form of neural compensation or dedifferentiation. We used functional magnetic resonance imaging (fMRI) to determine if there are age-related differences in functional activation during picture naming and whether such differences are consistent with a compensatory, dedifferentiation, or hybrid account that factors in difficulty. Healthy younger and older adults performed a picture-naming task with stimuli that varied in lexical frequency-our proxy for difficulty. Both younger and older adults were sensitive to lexical frequency behaviorally and neurally. However, younger adults performed more accurately overall and engaged both language (bilateral insula and temporal pole) and cognitive control (bilateral superior frontal gyri and left cingulate) regions to a greater extent than older adults when processing lower frequency items. In both groups, poorer performance was associated with increases in functional activation consistent with dedifferentiation. Moreover, there were age-related differences in the strength of these correlations, with better performing younger adults modulating the bilateral insula and temporal pole and better performing older adults modulating bilateral frontal pole and precuneus. Overall, these findings highlight the influence of task difficulty on fMRI activation in older adults and suggest that as task difficulty increases, older and younger adults rely on different neural resources. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Conventional and Deep Learning Methods for Skull Stripping in Brain MRI

Skull stripping in brain magnetic resonance volume has recently been attracting attention due to an increased demand to develop an efficient, accurate, and general algorithm for diverse datasets of the brain. Accurate skull stripping is a critical step for neuroimaging diagnostic systems because neither the inclusion of non-brain tissues nor removal of brain parts can be corrected in subsequent steps, which results in unfixed error through subsequent analysis. The objective of this review article is to give a comprehensive overview of skull stripping approaches, including recent deep learning-based approaches. In this paper, the current methods of skull stripping have been divided into two distinct groups—conventional or classical approaches, and convolutional neural networks or deep learning approaches. The potentials of several methods are emphasized because they can be applied to standard clinical imaging protocols. Finally, current trends and future developments are addressed giving special attention to recent deep learning algorithms.

Functional connectivity underpinning changes in life-space mobility in older adults with mild cognitive impairment: A 12-month prospective study

Subtle changes in mobility exist among older adults with mild cognitive impairment (MCI). Life-space mobility defines the frequency and extent of movements in the environment, and lower life-space mobility is associated with adverse health outcomes and MCI. Currently, the underlying mechanism of this association is not well understood. This study examined the functional neural correlates of life-space mobility in community-dwelling older adults with MCI. We first conducted a cross-sectional investigation of the association between resting-state default mode network (DMN) and sensori-motor network (SMN) connectivity and life-space mobility (assessed by the Life-Space Assessment (LSA)) among 60 community-dwelling older adults with MCI using aggregated data from two studies - baseline data from a randomized controlled trial (n = 20) and baseline data from a 12-month prospective study (n = 40). Using data from the 12-month prospective study (n = 35), we then examined whether baseline internetwork connectivity predicts reduced life-space mobility over 12 months. The cross-sectional analysis showed higher DMN-SMN connectivity was associated with lower LSA scores after adjusting for baseline global cognitive function and baseline age (p < 0.01). A significant reduction in LSA scores was observed in the 35 participants of the 12-month prospective study (paired sample t-test mean change = -6.53, p = 0.01). Greater baseline DMN-SMN connectivity was associated with greater reduction in life-space mobility at 12 months (p = 0.04) after adjusting for baseline age, global cognitive function, and LSA score. Our findings suggest that lower and reduced life-space mobility in older adults with MCI may be due to altered functional architecture of the brain such that normal neuro-cognitive motor behaviours may be disrupted.

Sex Effect on Presurgical Language Mapping in Patients With a Brain Tumor

Differences between males and females in brain development and in the organization and hemispheric lateralization of brain functions have been described, including in language. Sex differences in language organization may have important implications for language mapping performed to assess, and minimize neurosurgical risk to, language function. This study examined the effect of sex on the activation and functional connectivity of the brain, measured with presurgical functional magnetic resonance imaging (fMRI) language mapping in patients with a brain tumor. We carried out a retrospective analysis of data from neurosurgical patients treated at our institution who met the criteria of pathological diagnosis (malignant brain tumor), tumor location (left hemisphere), and fMRI paradigms [sentence completion (SC); antonym generation (AG); and resting-state fMRI (rs-fMRI)]. Forty-seven patients (22 females, mean age = 56.0 years) were included in the study. Across the SC and AG tasks, females relative to males showed greater activation in limited areas, including the left inferior frontal gyrus classically associated with language. In contrast, males relative to females showed greater activation in extended areas beyond the classic language network, including the supplementary motor area (SMA) and precentral gyrus. The rs-fMRI functional connectivity of the left SMA in the females was stronger with inferior temporal pole (TP) areas, and in the males with several midline areas. The findings are overall consistent with theories of greater reliance on specialized language areas in females relative to males, and generalized brain areas in males relative to females, for language function. Importantly, the findings suggest that sex could affect fMRI language mapping. Thus, considering sex as a variable in presurgical language mapping merits further investigation.

Mapping psycholinguistic features to the neuropsychological and lesion profiles in aphasia

Naming and word retrieval deficits are two of the most persistent symptoms in chronic post-stroke aphasia. Naming success or failure on specific words can sometimes be predicted by the psycholinguistic properties of the word. Despite a wealth of literature investigating the influence of psycholinguistic properties in neuro-typical and clinical language processing, the underlying structure of these properties and their relation to the fundamental language components and neural correlates are unexplored. In this study, a multivariate data-decomposition approach was used to identify the underlying structure within a collection of psycholinguistic properties (word imageability, frequency, age-of-acquisition, familiarity, length, semantic diversity and phonological neighbourhood density) and their influence on naming accuracy was explored in a cohort of 42 participants with a diverse range of chronic post-stroke aphasia classifications and severities. The results extracted three principal psycholinguistic factors, which were best described as 'lexical usage', 'semantic clarity' and 'phonological complexity'. Furthermore, a novel approach was used to systematically relate the influence of these psycholinguistic properties to participants' neuropsychological and lesion profiles. The findings did not show a one-to-one mapping between psycholinguistic features and core language components. 'Lexical usage' was the only factor that showed a significant difference between fluent versus non-fluent aphasia groups in terms of the influence of this lexical factor on successful naming, and it was the only factor that was related to the pattern of patients' brain lesions. Voxel-wise whole brain lesion-symptom mapping identified left frontal regions, aligning with previous evidence that these regions are related to language production functions, including word retrieval and repetition. The evidence from the current study suggests that the functional locus of psycholinguistic properties is distributed across multiple language components rather than being localised to a single language element.

Automated brain extraction of multisequence MRI using artificial neural networks

Brain extraction is a critical preprocessing step in the analysis of neuroimaging studies conducted with magnetic resonance imaging (MRI) and influences the accuracy of downstream analyses. The majority of brain extraction algorithms are, however, optimized for processing healthy brains and thus frequently fail in the presence of pathologically altered brain or when applied to heterogeneous MRI datasets. Here we introduce a new, rigorously validated algorithm (termed HD-BET) relying on artificial neural networks that aim to overcome these limitations. We demonstrate that HD-BET outperforms six popular, publicly available brain extraction algorithms in several large-scale neuroimaging datasets, including one from a prospective multicentric trial in neuro-oncology, yielding state-of-the-art performance with median improvements of +1.16 to +2.50 points for the Dice coefficient and -0.66 to -2.51 mm for the Hausdorff distance. Importantly, the HD-BET algorithm, which shows robust performance in the presence of pathology or treatment-induced tissue alterations, is applicable to a broad range of MRI sequence types and is not influenced by variations in MRI hardware and acquisition parameters encountered in both research and clinical practice. For broader accessibility, the HD-BET prediction algorithm is made freely available (www.neuroAI-HD.org) and may become an essential component for robust, automated, high-throughput processing of MRI neuroimaging data.

Limited One-time Sampling Irregularity Map (LOTS-IM) for Automatic Unsupervised Assessment of White Matter Hyperintensities and Multiple Sclerosis Lesions in Structural Brain Magnetic Resonance Images

We present the application of limited one-time sampling irregularity map (LOTS-IM): a fully automatic unsupervised approach to extract brain tissue irregularities in magnetic resonance images (MRI), for quantitatively assessing white matter hyperintensities (WMH) of presumed vascular origin, and multiple sclerosis (MS) lesions and their progression. LOTS-IM generates an irregularity map (IM) that represents all voxels as irregularity values with respect to the ones considered "normal". Unlike probability values, IM represents both regular and irregular regions in the brain based on the original MRI's texture information. We evaluated and compared the use of IM for WMH and MS lesions segmentation on T2-FLAIR MRI with the state-of-the-art unsupervised lesions' segmentation method, Lesion Growth Algorithm from the public toolbox Lesion Segmentation Toolbox (LST-LGA), with several well established conventional supervised machine learning schemes and with state-of-the-art supervised deep learning methods for WMH segmentation. In our experiments, LOTS-IM outperformed unsupervised method LST-LGA on WMH segmentation, both in performance and processing speed, thanks to the limited one-time sampling scheme and its implementation on GPU. Our method also outperformed supervised conventional machine learning algorithms (i.e., support vector machine (SVM) and random forest (RF)) and deep learning algorithms (i.e., deep Boltzmann machine (DBM) and convolutional encoder network (CEN)), while yielding comparable results to the convolutional neural network schemes that rank top of the algorithms developed up to date for this purpose (i.e., UResNet and UNet). LOTS-IM also performed well on MS lesions segmentation, performing similar to LST-LGA. On the other hand, the high sensitivity of IM on depicting signal change deems suitable for assessing MS progression, although care must be taken with signal changes not reflective of a true pathology.

Cortical Microinfarcts on 3T Magnetic Resonance Imaging in Cerebral Amyloid Angiopathy

Background and Purpose- Cerebral microinfarcts are small ischemic lesions that are found in cerebral amyloid angiopathy (CAA) patients at autopsy. The current study aimed to detect cortical microinfarcts (CMI) on in vivo 3 Tesla (3T) magnetic resonance imaging (MRI) in CAA patients, to study the progression of CMI over a 1-year period, and to correlate CMI with markers of CAA-related vascular brain injury and cognitive functioning. Methods- Thirty-five CAA patients (mean age, 74.2±7.6 years), 13 Alzheimer disease (AD) patients (67.0±5.8 years), and 26 healthy controls (67.2±9.5 years) participated in the study. All participants underwent a standardized clinical and neuropsychological assessment as well as 3T MRI. CMI were rated according to standardized criteria. Results- CMI were present in significantly more CAA patients (57.1%; median number: 1, range 1-9) than in Alzheimer disease (7.7%) or in healthy controls (11.5%; P<0.001). Incident CMI were observed after a 1-year follow-up. CMI did not correlate with any other MRI marker of CAA nor with cognitive function. Conclusions- In vivo CMI are a frequent finding on 3T MRI in CAA patients, and incident CMI are observable after 1-year follow-up. CMI can be regarded as a new MRI marker of CAA, potentially distinct from other well-established markers. Future larger cohort studies with longitudinal follow-up are needed to elucidate the relationship between CMI and possible causes and clinical outcomes in CAA.