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Background Behavioral symptoms, including mood disorders, substantially impact the quality of life of patients with inflammatory bowel disease (IBD), even when clinical remission is achieved. Here, we used multimodal magnetic resonance imaging (MRI) to determine if IBD is associated with changes in the structure and function of deep gray matter brain regions that regulate and integrate emotional, cognitive, and stress responses. Methods Thirty-five patients with ulcerative colitis (UC) or Crohn’s disease (CD) and 32 healthy controls underwent 3 Tesla MRIs to assess volume, neural activity, functional connection strength (connectivity), inflammation, and neurodegeneration of key deep gray matter brain regions (thalamus, caudate, pallidum, putamen, amygdala, hippocampus, and hypothalamus) involved in emotional, cognitive and stress processing. Associations with sex, presence of pain, disease activity, and C-reactive protein (CRP) concentration were examined. Results Significantly increased activity and functional connectivity were observed in cognitive and emotional processing brain regions, including parts of the limbic system, basal ganglia, and hypothalamus of IBD patients compared with healthy controls. Inflammatory bowel disease patients exhibited significantly increased volumes of the amygdala and hypothalamus, as well as evidence of neurodegeneration in the putamen and pallidum. Hippocampal neural activity was increased in IBD patients with active disease. The volume of the thalamus was positively correlated with CRP concentration and was increased in females experiencing pain. Conclusions Patients with IBD exhibit functional and structural changes in the limbic and striatal systems. These changes may be targets for assessing or predicting the response to therapeutic interventions aimed at improving comorbid emotional and cognitive symptoms.

In the largest sample studied to date, white matter microstructural trajectories and their relation to persistent symptoms were examined after pediatric mild traumatic brain injury (mTBI). This prospective, longitudinal cohort study recruited children aged 8-16.99 years with mTBI or mild orthopedic injury (OI) from five pediatric emergency departments. Children's pre-injury and 1-month post-injury symptom ratings were used to classify mTBI with or without persistent symptoms. Children completed diffusion-weighted imaging at post-acute (2-33 days post-injury) and chronic (3 or 6 months via random assignment) post-injury assessments. Mean diffusivity (MD) and fractional anisotropy (FA) were derived for 18 white matter tracts in 560 children (362 mTBI/198 OI), 407 with longitudinal data. Superior longitudinal fasciculus FA was higher in mTBI without persistent symptoms relative to OI, d (95% confidence interval) = 0.31 to 0.37 (0.02, 0.68), across time. In younger children, MD of the anterior thalamic radiations was higher in mTBI with persistent symptoms relative to both mTBI without persistent symptoms, 1.43 (0.59, 2.27), and OI, 1.94 (1.07, 2.81). MD of the arcuate fasciculus, -0.58 (-1.04, -0.11), and superior longitudinal fasciculus, -0.49 (-0.90, -0.09) was lower in mTBI without persistent symptoms relative to OI at 6 months post-injury. White matter microstructural changes suggesting neuroinflammation and axonal swelling occurred chronically and continued 6 months post injury in children with mTBI, especially in younger children with persistent symptoms, relative to OI. White matter microstructure appears more organized in children without persistent symptoms, consistent with their better clinical outcomes.

Sophisticated network‐based approaches such as structural connectomics may help to detect a biomarker of mild traumatic brain injury (mTBI) in children. This study compared the structural connectome of children with mTBI or mild orthopedic injury (OI) to that of typically developing (TD) children. Children aged 8–16.99 years with mTBI (n = 83) or OI (n = 37) were recruited from the emergency department and completed 3T diffusion MRI 2–20 days postinjury. TD children (n = 39) were recruited from the community and completed diffusion MRI. Graph theory metrics were calculated for the binarized average fractional anisotropy among 90 regions. Multivariable linear regression and linear mixed effects models were used to compare groups, with covariates age, hemisphere, and sex, correcting for multiple comparisons. The two injury groups did not differ on graph theory metrics, but both differed from TD children in global metrics (local network efficiency: TD > OI, mTBI, d = 0.49; clustering coefficient: TD < OI, mTBI, d = 0.49) and regional metrics for the fusiform gyrus (lower degree centrality and nodal efficiency: TD > OI, mTBI, d = 0.80 to 0.96; characteristic path length: TD < OI, mTBI, d = −0.75 to −0.90) and in the superior and middle orbital frontal gyrus, paracentral lobule, insula, and thalamus (clustering coefficient: TD > OI, mTBI, d = 0.66 to 0.68). Both mTBI and OI demonstrated reduced global and regional network efficiency and segregation as compared to TD children. Findings suggest a general effect of childhood injury that could reflect pre‐ and postinjury factors that can alter brain structure. An OI group provides a more conservative comparison group than TD children for structural neuroimaging research in pediatric mTBI., Children with mTBI and OI demonstrated reduced global and regional network efficiency and segregation as compared to TD children. Findings suggest a general effect of childhood injury that could reflect pre‐ and postinjury factors that can alter brain structure. An OI group provides a more conservative comparison group than TD children for pediatric mTBI research.

Brain imaging with a high-spatiotemporal resolution is crucial for accurate brain-function mapping. Electroencephalography (EEG) and functional Magnetic Resonance Imaging (fMRI) are two popular neuroimaging modalities with complementary features that record brain function with high temporal and spatial resolution, respectively. One popular non-invasive way to obtain data with both high spatial and temporal resolutions is to combine the fMRI activation map and EEG data to improve the spatial resolution of the EEG source localization. However, using the whole fMRI map may cause spurious results for the EEG source localization, especially for deep brain regions. Considering the head’s conductivity, deep regions’ dipoles with low activity are unlikely to be detected by the EEG electrodes at the scalp. In this study, we use fMRI’s high spatial-frequency component to identify the local high-intensity activations that are most likely to be captured by the EEG. The 3D Empirical Mode Decomposition (3D-EMD), a data-driven method, is used to decompose the fMRI map into its spatial-frequency components. Different validation measurements for EEG source localization show improved performance for the EEG inverse-modeling informed by the fMRI’s high-frequency spatial component compared to the fMRI-informed EEG source-localization methods. The level of improvement varies depending on the voxels’ intensity and their distribution.

Motion can compromise image quality and confound results, especially in pediatric research. This study evaluated qualitative and quantitative approaches to motion artifacts detection and correction, and whether motion artifacts relate to injury history, age, or sex in children with mild traumatic brain injury or orthopedic injury relative to typically developing children. The concordance between qualitative and quantitative motion ratings was also examined. Children aged 8-16 years with mild traumatic brain injury (n = 141) or orthopedic injury (n = 73) were recruited from the emergency department and completed an MRI scan roughly 2 weeks post-injury. Typically developing children (n = 41) completed a single MRI scan. T1- and diffusion-weighted images were visually inspected and rated for motion artifacts by trained examiners. Quantitative estimates of motion artifacts were derived from FreeSurfer and FSL. Age (younger > older) and sex (boys > girls) were significantly associated with motion artifacts on both T1- and diffusion-weighted images. Children with mild traumatic brain or orthopedic injury had significantly more motion-corrupted diffusion-weighted volumes than typically developing children, but mild traumatic brain injury and orthopedic injury groups did not differ from each other. The exclusion of motion-corrupted volumes did not significantly change diffusion tensor imaging metrics. Results indicate that automated quantitative estimates of motion artifacts, which are less labour-intensive than manual methods, are appropriate. Results have implications for the reliability of structural MRI research and highlight the importance of considering motion artifacts in studies of pediatric mild traumatic brain injury.

Background Identifying which EEG signals distinguish left from right leg movements in imagined lower limb movement is crucial to building an effective and efficient brain-computer interface (BCI). Past findings on this issue have been mixed, partly due to the difficulty in collecting and isolating the relevant information. The purpose of this study was to contribute to this new and important literature. Research Question Can left versus right imagined stepping be differentiated using the alpha, beta, and gamma frequencies of EEG data at four electrodes (C1, C2, PO3, and PO4)? Methods An experiment was conducted with a sample of 16 healthy male participants. They imagined left and right lower limb movements across 60 trials at two time periods separated by one week. Participants were fitted with a 64-electrode headcap, lay supine on a specially designed device and then completed the imagined task while observing a customized computer-generated image of a human walking to signify the left and right steps, respectively. Results Findings showed that eight of the twelve frequency bands from 4 EEG electrodes were significant in differentiating imagined left from right lower limb movement. Using these data points, a neural network analysis resulted in an overall participant average test classification accuracy of left versus right movements at 63 %. Significance Our study provides support for using the alpha, beta and gamma frequency bands at the sensorimotor areas (C1 and C2 electrodes) and incorporating information from the parietal/occipital lobes (PO3 and PO4 electrodes) for focused, real-time EEG signal processing to assist in creating a BCI for those with lower limb compromised mobility.

Mounting evidence has shown disrupted brain network architecture across the psychosis spectrum. However, whether these changes relate to the development of psychosis is unclear. Here, we used graph theoretical analysis to investigate longitudinal changes in resting-state brain networks in samples of 72 subjects at clinical high risk (including 8 cases who converted to full psychosis) and 48 healthy controls drawn from the North American Prodrome Longitudinal Study (NAPLS) consortium. We observed progressive reduction in global efficiency (P = 0.006) and increase in network diversity (P = 0.001) in converters compared with non-converters and controls. More refined analysis separating nodes into nine key brain networks demonstrated that these alterations were primarily driven by progressively diminished local efficiency in the default-mode network (P = 0.004) and progressively enhanced node diversity across all networks (P < 0.05). The change rates of network efficiency and network diversity were significantly correlated (P = 0.003), suggesting these changes may reflect shared underlying neural mechanisms. In addition, change rates of global efficiency and node diversity were significantly correlated with change rate of cortical thinning in the prefrontal cortex in converters (P < 0.03) and could be predicted by visuospatial memory scores at baseline (P < 0.04). These results provide preliminary evidence for longitudinal reconfiguration of resting-state brain networks during psychosis development and suggest that decreased network efficiency, reflecting an increase in path length between nodes, and increased network diversity, reflecting a decrease in the consistency of functional network organization, are implicated in the progression to full psychosis.

Progressive grey matter loss has been demonstrated among clinical high-risk (CHR) individuals who convert to psychosis, but it is unknown whether these changes occur prior to psychosis onset. Identifying illness-related neurobiological mechanisms that occur prior to conversion is essential for targeted early intervention. Among participants in the third wave of the North American Prodrome Longitudinal Study (NAPLS3), this report investigated if steeper cortical thinning was observable prior to psychosis onset among CHR individuals who ultimately converted (CHR-C) and assessed the shortest possible time interval in which rates of cortical thinning differ between CHR-C, CHR non-converters (CHR-NC), and health controls (HC). 338 CHR-NC, 42 CHR-C, and 62 HC participants (age 19.3±4.2, 44.8% female, 52.5% racial/ethnic minority) completed up to 5 MRI scans across 8 months. Accelerated thinning among CHR-C compared to CHR-NC and HC was observed in multiple prefrontal, temporal, and parietal cortical regions. CHR-NC also exhibited accelerated cortical thinning compared to HC in several of these areas. Greater percent decrease in cortical thickness was observed among CHR-C compared to other groups across 2.9±1.8 months, on average, in several cortical areas. ROC analyses discriminating CHR-C from CHR-NC by percent thickness change in a left hemisphere region of interest, scanner, age, age2, and sex had an AUC of 0.74, with model predictive power driven primarily by percent thickness change. Findings indicate that accelerated cortical thinning precedes psychosis onset and differentiates CHR-C from CHR-NC and HC across short time intervals. Mechanisms underlying cortical thinning may provide novel treatment targets prior to psychosis onset.

Impairments of cognitive function during alterations in arterial blood gases (e.g., high‐altitude hypoxia) may result from the disruption of neurovascular coupling; however, the link between changes in arterial blood gases, cognition, and cerebral blood flow (CBF) is poorly understood. To interrogate this link, we developed a multimodal empirical strategy capable of monitoring neural correlates of cognition and CBF simultaneously. Human participants performed a sustained attention task during hypoxia, hypercapnia, hypocapnia, and normoxia while electroencephalographic (EEG) activity and CBF (middle and posterior cerebral arteries; transcranial Doppler ultrasound) were simultaneously measured. The protocol alternated between rest and engaging in a visual target detection task that required participants to monitor a sequence of brief‐duration colored circles and detect infrequent, longer duration circles (targets). The target detection task was overlaid on a large, circular checkerboard that provided robust visual stimulation. Spectral decomposition and event‐related potential (ERP) analyses were applied to the EEG data to investigate spontaneous and task‐specific fluctuations in neural activity. There were three main sets of findings: (1) spontaneous alpha oscillatory activity was modulated as a function of arterial CO2 (hypocapnia and hypercapnia), (2) task‐related neurovascular coupling was disrupted by all arterial blood gas manipulations, and (3) changes in task‐related alpha and theta band activity and attenuation of the P3 ERP component amplitude were observed during hypocapnia. Since alpha and theta are linked with suppression of visual processing and executive control and P3 amplitude with task difficulty, these data suggest that transient arterial blood gas changes can modulate multiple stages of cognitive information processing., Changes in arterial blood gases (e.g., from high‐altitude exposure) are associated with impairments of a variety of cognitive functions, potentially via disruption of how blood flow responds to brain activity. We used a novel multimodal approach to simultaneously measure human brain electrical activity using electroencephalography and blood flow using transcranial Doppler ultrasound, while participants performed a demanding sustained attention task at varying levels of arterial oxygen and carbon dioxide (CO2). We observed changes in both spontaneous and task‐related brain activity as a function of changes in arterial CO2 as well as disruptions in neurovascular coupling, suggesting that transient changes in arterial CO2 can affect multiple stages of cognitive processing.

Background: Motion can compromise image quality and confound results, especially in pediatric research. This study evaluated qualitative and quantitative approaches to motion artifacts detection and correction, and whether motion artifacts relate to injury history, age, or sex in children with mild traumatic brain injury or orthopedic injury relative to typically developing children. The concordance between qualitative and quantitative motion ratings was also examined.Method: Children aged 8-16 years with mild traumatic brain injury (n=141) or orthopedic injury (n=73) were recruited from the emergency department and completed an MRI scan roughly 2 weeks post-injury. Typically developing children (n=41) completed a single MRI scan. T1- and diffusion-weighted images were visually inspected and rated for motion artifacts by trained examiners. Quantitative estimates of motion artifacts were derived from FreeSurfer and FSL. Results: Age (younger > older) and sex (boys > girls) were significantly associated with motion artifacts on both T1- and diffusion-weighted images. Children with mild traumatic brain or orthopedic injury had significantly more motion-corrupted diffusion-weighted volumes than typically developing children, but mild traumatic brain injury and orthopedic injury groups did not differ from each other. The exclusion of motion-corrupted volumes did not significantly change diffusion tensor imaging metrics. Discussion: Results indicate that automated quantitative estimates of motion artifacts, which are less labour-intensive than manual methods, are appropriate. Results have implications for the reliability of structural magnetic resonance imaging research and highlight the importance of considering motion artifacts in studies of pediatric mild traumatic brain injury.

BackgroundPediatric mild traumatic brain injury (mTBI) affects millions of children annually. Diffusion tensor imaging (DTI) is sensitive to axonal injuries and white matter microstructure and has been used to characterize the brain changes associated with mild traumatic brain injury (mTBI). Neurite orientation dispersion and density imaging (NODDI) is a diffusion model that can provide additional insight beyond traditional DTI metrics, but has not been examined in pediatric mTBI. The goal of this study was to employ DTI and NODDI to gain added insight into white matter alterations in children with mTBI compared to children with mild orthopedic injury (OI).MethodsChildren (mTBI n=320, OI n=176) aged 8-16.99 years (m 12.39 ± 2.32 years) were recruited from emergency departments at five hospitals across Canada and underwent 3T MRI on average 11 days post-injury. DTI and NODDI metrics were calculated for seven major white matter tracts and compared between groups using univariate analysis of covariance controlling for age, sex, and scanner type. False discovery rate (FDR) was used to correct for multiple comparisons.ResultsUnivariate analysis revealed no significant group main effects or interactions in DTI or NODDI metrics. Fractional anisotropy and neurite density index in all tracts exhibited a significant positive association with age and mean diffusivity in all tracts exhibited a significant negative association with age in the whole sample.ConclusionsOverall, there were no differences between mTBI and OI groups in brain white matter microstructure from either DTI or NODDI in the seven tracts. This indicates that mTBI is associated with relatively minor white matter differences, if any, at the post-acute stage. Brain differences may evolve at later stages of injury, so longitudinal studies with long-term follow-up are needed.

Highlights • We examined white matter microstructure after pediatric mTBI using NODDI and DTI. • Children with mTBI did not significantly differ from those with OI on any metrics. • Minor alterations, if any, may be present in children at the post-acute stage after mTBI. • Large longitudinal studies are needed to understand long-term brain changes post injury., Background Pediatric mild traumatic brain injury (mTBI) affects millions of children annually. Diffusion tensor imaging (DTI) is sensitive to axonal injuries and white matter microstructure and has been used to characterize the brain changes associated with mild traumatic brain injury (mTBI). Neurite orientation dispersion and density imaging (NODDI) is a diffusion model that can provide additional insight beyond traditional DTI metrics, but has not been examined in pediatric mTBI. The goal of this study was to employ DTI and NODDI to gain added insight into white matter alterations in children with mTBI compared to children with mild orthopedic injury (OI). Methods Children (mTBI n = 320, OI n = 176) aged 8–16.99 years (12.39 ± 2.32 years) were recruited from emergency departments at five hospitals across Canada and underwent 3 T MRI on average 11 days post-injury. DTI and NODDI metrics were calculated for seven major white matter tracts and compared between groups using univariate analysis of covariance controlling for age, sex, and scanner type. False discovery rate (FDR) was used to correct for multiple comparisons. Results Univariate analysis revealed no significant group main effects or interactions in DTI or NODDI metrics. Fractional anisotropy and neurite density index in all tracts exhibited a significant positive association with age and mean diffusivity in all tracts exhibited a significant negative association with age in the whole sample. Conclusions Overall, there were no significant differences between mTBI and OI groups in brain white matter microstructure from either DTI or NODDI in the seven tracts. This indicates that mTBI is associated with relatively minor white matter differences, if any, at the post-acute stage. Brain differences may evolve at later stages of injury, so longitudinal studies with long-term follow-up are needed.

Deficits in proprioception, the ability to discriminate the relative position and movement of our limbs, affect ~50% of stroke patients and reduce functional outcomes. Our lack of knowledge of the anatomical correlates of proprioceptive processing limits our understanding of the impact that such deficits have on recovery. This research investigated the relationship between functional impairment in brain activity and proprioception post-stroke. We developed a novel device and task for arm position matching during functional MRI (fMRI), and investigated 16 subjects with recent stroke and nine healthy age-matched controls. The stroke-affected arm was moved by an experimenter (passive arm), and subjects were required to match the position of this limb with the opposite arm (active arm). Brain activity during passive and active arm movements was determined, as well as activity in association with performance error. Passive arm movement in healthy controls was associated with activity in contralateral primary somatosensory (SI) and motor cortices (MI), bilateral parietal cortex, supplementary (SMA) and premotor cortices, secondary somatosensory cortices (SII), and putamen. Active arm matching was associated with activity in contralateral SI, MI, bilateral SMA, premotor cortex, putamen, and ipsilateral cerebellum. In subjects with stroke, similar patterns of activity were observed. However, in stroke subjects, greater proprioceptive error was associated with less activity in ipsilesional supramarginal and superior temporal gyri, and lateral thalamus. During active arm movement, greater proprioceptive error was associated with less activity in bilateral SMA and ipsilesional premotor cortex. Our results enhance our understanding of the correlates of proprioception within the temporal parietal cortex and supplementary/premotor cortices. These findings also offer potential targets for therapeutic intervention to improve proprioception in recovering stroke patients and thus improve functional outcome.

Repetitive transcranial magnetic stimulation (rTMS) shows promise in improving speech production in post-stroke aphasia. Limited evidence suggests pairing rTMS with speech therapy may result in greater improvements. Twenty stroke survivors (6 months post-stroke) were randomized to receive either sham rTMS plus multi-modality aphasia therapy (M-MAT) or rTMS plus M-MAT. For the first time, we demonstrate that rTMS combined with M-MAT is feasible, with zero adverse events and minimal attrition. Both groups improved significantly over time on all speech and language outcomes. However, improvements did not differ between rTMS or sham. We found that rTMS and sham groups differed in lesion location, which may explain speech and language outcomes as well as unique patterns of BOLD signal change within each group. We offer practical considerations for future studies and conclude that while combination therapy of rTMS plus M-MAT in chronic post-stroke aphasia is safe and feasible, personalized intervention may be necessary.

Objective: The long-term consequences of pediatric concussion on brain structure are poorly understood. This study aimed to evaluate the presence and clinical significance of cerebral microbleeds several years after pediatric concussion. Methods: Children and adolescents 8-19 years of age with either a history of concussion (n = 35), or orthopedic injury (n = 20) participated. Mean time since injury for the sample was 30.4 months (SD = 19.6). Participants underwent susceptibility-weighted imaging, rated their depression and postconcussion symptoms, and completed cognitive testing. Parents of participants also completed symptom ratings for their child. Hypointensities in susceptibility-weighted images indicative of cerebral microbleeds were calculated as a measure of hypointensity burden. Results: Hypointensity burden did not differ significantly between participants with a history of concussion and those with a history of orthopedic injury. Depression ratings (self and parent report), postconcussion symptom ratings (self and parent report), and cognitive performance did not significantly correlate with hypointensity burden in the concussion group. Conclusions: These findings suggest that at approximately 2.5 years postinjury, children and adolescents with prior concussion do not have a greater amount of cerebral microbleeds compared to those with orthopedic injury. Future research should use longitudinal study designs and investigate children with persistent postconcussive symptoms to gain better insight into the long-term effects of concussion on cerebral microbleeds.

While functional neuroimaging studies typically focus on a particular paradigm to investigate network connectivity, the human brain appears to possess an intrinsic ���trait��� architecture that is independent of any given paradigm. We have previously proposed the use of ���cross-paradigm connectivity (CPC)��� to quantify shared connectivity patterns across multiple paradigms and have demonstrated the utility of such measures in clinical studies. Here, using generalizability theory and connectome fingerprinting, we examined the reliability, stability, and individual identifiability of CPC in a group of highly-sampled healthy traveling subjects who received fMRI scans with a battery of five paradigms across multiple sites and days. Compared with single-paradigm connectivity matrices, the CPC matrices showed higher reliability in connectivity diversity, lower reliability in connectivity strength, higher stability, and higher individual identification accuracy. All of these assessments increased as a function of number of paradigms included in the CPC analysis. In comparisons involving different paradigm combinations and different brain atlases, we observed significantly higher reliability, stability, and identifiability for CPC matrices constructed from task-only data (versus those from both task and rest data), and higher identifiability but lower stability for CPC matrices constructed from the Power atlas (versus those from the AAL atlas). Moreover, we showed that multi-paradigm CPC matrices likely reflect the brain���s ���trait��� structure that cannot be fully achieved from single-paradigm data, even with multiple runs. The present results provide evidence for the feasibility and utility of CPC in the study of functional ���trait��� networks and offer some methodological implications for future CPC studies.

Objective Scalp electroencephalographic (EEG)-functional magnetic resonance imaging (fMRI) studies suggest that the maximum blood oxygen level-dependent (BOLD) response to an interictal epileptiform discharge (IED) identifies the area of IED generation. However, the maximum BOLD response has also been reported in distant, seemingly irrelevant areas. Given the poor postoperative outcomes associated with extra-temporal lobe epilepsy, we hypothesized this finding is more common when analyzing extratemporal IEDs as compared to temporal IEDs. We further hypothesized that a subjective, holistic assessment of other significant BOLD clusters to identify the most clinically relevant cluster could be used to overcome this limitation and therefore better identify the likely origin of an IED. Specifically, we also considered the second maximum cluster and the cluster closest to the electrode contacts where the IED was observed. Methods Maps of significant IED-related BOLD activation were generated for 48 different IEDs recorded from 33 patients who underwent intracranial EEG-fMRI. The locations of the maximum, second maximum, and closest clusters were identified for each IED. An epileptologist, blinded to these cluster assignments, selected the most clinically relevant BOLD cluster, taking into account all available clinical information. The distances between these BOLD clusters and their corresponding IEDs were then measured. Results The most clinically relevant cluster was the maximum cluster for 56% (27/48) of IEDs, the second maximum cluster for 13% (6/48) of IEDs, and the closest cluster for 31% (15/48) of IEDs. The maximum clusters were closer to IED contacts for temporal than for extratemporal IEDs (p = .022), whereas the most clinically relevant clusters were not significantly different (p = .056). Significance The maximum BOLD response to IEDs may not always be the most indicative of IED origin. We propose that available clinical information should be used in conjunction with EEG-fMRI data to identify a BOLD cluster representative of the IED origin.

Simultaneous EEG-fMRI is a growing and promising field, as it has great potential to further our understanding of the spatiotemporal dynamics of brain function in health and disease. In particular, there is much interest in understanding the fMRI correlates of brain activity in the gamma band (> 30 Hz), as these frequencies are thought to be associated with cognitive processes involving perception, attention, and memory, as well as with disorders such as schizophrenia and autism. However, progress in this area has been limited due to issues such as MR-induced artifacts in EEG recordings, which seem to be more problematic for gamma frequencies. This paper presents a noise removal method for the gamma band of EEG that is based on the Holo-Hilbert spectral analysis (HHSA), but with a new implementation strategy. HHSA uses a nested empirical mode decomposition (EMD) to identify amplitude and frequency modulations (AM and FM, respectively) by averaging over frequencies with high and significant powers. Our method examines gamma band by applying two layers of EMD to the FM and AM components, removing components with very low power based on the power-instantaneous frequency spectrum, and subsequently reconstructs the denoised gamma-band signal from the remaining components. Simulations demonstrate that our proposed method efficiently reduces artifacts while preserving the original gamma signal which is especially critical for simultaneous EEG/fMRI studies.

Perinatal stroke causes lifelong disability, particularly hemiparetic cerebral palsy. Arterial ischemic strokes (AIS) are large, cortical, and subcortical injuries acquired near birth due to acute occlusion of the middle cerebral artery. Periventricular venous infarctions (PVI) are smaller, subcortical strokes acquired prior to 34 weeks gestation involving injury to the periventricular white matter. Both stroke types can damage motor pathways, thus, we investigated resulting alterations in functional motor networks and probed function. We measured blood oxygen level dependent (BOLD) fluctuations at rest in 38 participants [10 arterial patients (age = 14.7 ± 4.1 years), 10 venous patients (age = 13.5 ± 3.7 years), and 18 typically developing controls (TDCs) (age = 15.3 ± 5.1 years)] and explored strength and laterality of functional connectivity in the motor network. Inclusion criteria included MRI‐confirmed, unilateral perinatal stroke, symptomatic hemiparetic cerebral palsy, and 6–19 years old at time of imaging. Seed‐based functional connectivity analyses measured temporal correlations in BOLD response over the whole brain using primary motor cortices as seeds. Laterality indices based on mean z‐scores in lesioned and nonlesioned hemispheres explored laterality. In AIS patients, significant differences in both strength and laterality of motor network connections were observed compared with TDCs. In PVI patients, motor networks largely resembled those of healthy controls, albeit slightly weaker and asymmetric, despite subcortical damage and hemiparesis. Functional connectivity strengths were not related to motor outcome scores for either stroke group. This study serves as a foundation to better understand how resting‐state fMRI can assess motor functional connectivity and potentially be applied to explore mechanisms of interventional therapies after perinatal stroke.

Understanding the fundamental alterations in brain functioning that lead to psychotic disorders remains a major challenge in clinical neuroscience. In particular, it is unknown whether any state-independent biomarkers can potentially predict the onset of psychosis and distinguish patients from healthy controls, regardless of paradigm. Here, using multi-paradigm fMRI data from the North American Prodrome Longitudinal Study consortium, we show that individuals at clinical high risk for psychosis display an intrinsic “trait-like” abnormality in brain architecture characterized as increased connectivity in the cerebello–thalamo–cortical circuitry, a pattern that is significantly more pronounced among converters compared with non-converters. This alteration is significantly correlated with disorganization symptoms and predictive of time to conversion to psychosis. Moreover, using an independent clinical sample, we demonstrate that this hyperconnectivity pattern is reliably detected and specifically present in patients with schizophrenia. These findings implicate cerebello–thalamo–cortical hyperconnectivity as a robust state-independent neural signature for psychosis prediction and characterization.

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 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.

While graph theoretical modeling has dramatically advanced our understanding of complex brain systems, the feasibility of aggregating connectomic data in large imaging consortia remains unclear. Here, using a battery of cognitive, emotional and resting fMRI paradigms, we investigated the generalizability of functional connectomic measures across sites and sessions. Our results revealed overall fair to excellent reliability for a majority of measures during both rest and tasks, in particular for those quantifying connectivity strength, network segregation and network integration. Processing schemes such as node definition and global signal regression (GSR) significantly affected resulting reliability, with higher reliability detected for the Power atlas (vs. AAL atlas) and data without GSR. While network diagnostics for default-mode and sensori-motor systems were consistently reliable independently of paradigm, those for higher-order cognitive systems were reliable predominantly when challenged by task. In addition, based on our present sample and after accounting for observed reliability, satisfactory statistical power can be achieved in multisite research with sample size of approximately 250 when the effect size is moderate or larger. Our findings provide empirical evidence for the generalizability of brain functional graphs in large consortia, and encourage the aggregation of connectomic measures using multisite and multisession data.

Darren L Clark,1–4 Nithya Konduru,1 Anne Kemp,5 Signe Bray,6–8 Elliot C Brown,1–4 Bradley Goodyear,1,2,4,6 Rajamannar Ramasubbu1–4 1Department of Psychiatry, 2Department of Clinical Neuroscience, 3Mathison Centre for Mental Health Research and Education, 4Hotchkiss Brain Institute, University of Calgary, Calgary, 5School of Medicine, University of Alberta, Edmonton, 6Department of Radiology, 7Department of Pediatrics, University of Calgary, 8Child and Adolescent Imaging Research Program, Alberta Children’s Hospital, Calgary, AB, Canada Background: Early-onset major depressive disorder (EO-MDD), beginning during childhood and adolescence, is associated with more illness burden and a worse prognosis than adult-onset MDD (AO-MDD), but little is known about the neural features distinguishing these subgroup phenotypes. Functional abnormalities of the amygdala are central to major depressive disorder (MDD) neurobiology; therefore, we examined whether amygdala intrinsic connectivity (IC) can differentiate EO-MDD from AO-MDD in a cohort of adult MDD patients.Subjects and methods: Twenty-one EO-MDD (age of onset ≤18 years), 31 AO-MDD patients (age of onset ≥19 years), and 19 healthy controls (HCs) underwent resting-state functional magnetic resonance imaging (7 minutes). Amygdala seed-based resting-state functional connectivity was compared between groups.Results: AO-MDD patients showed loss of inverse left amygdala–left dorsolateral prefrontal cortex IC and increased inverse left amygdala–left inferior parietal IC, compared to both HCs and EO-MDD. EO-MDD showed a switch from inverse to positive IC with right dorsomedial prefrontal cortex, compared to HCs and AO-MDD. This effect was removed when we controlled for illness burden.Conclusion: Alterations in amygdala IC with the default-mode network were specifically related to EO-MDD, whereas amygdala IC with executive cognitive control regions was preferentially disrupted in AO-MDD. Increased illness burden, an important clinical marker of EO-MDD, accounted for its specific effects on amygdala IC. Brain imaging has the potential for validation of clinical subtypes and can provide markers of prognostic value in MDD patients. Keywords: major depressive disorder, onset age, resting-state functional connectivity, functional magnetic resonance imaging, biomarker, amygdala

Objective Patients with brain tumors are known to have deficits in cognitive, motor, and emotional domains. Comprehensive evaluation of the patient with brain tumor includes taking into account all these domains at baseline and throughout treatment. Standard neuropsychological assessment methods, however, are lengthy, expensive, and often are variable. The authors appraised the feasibility of using a brief, inexpensive, comprehensive, and standardized neuropsychological battery, the National Institutes of Health (NIH) Toolbox, to assess these domains in patients with diffuse glioma. Methods Eighteen patients were recruited and completed the NIH Toolbox Cognitive Battery, 2 motor tests (Grip Strength and Grooved Pegboard), and the NIH Toolbox Emotional Battery. Fully corrected T scores are reported, as well as composite scores of fluid and crystallized cognition. Follow-up cognitive ( n = 13) and motor assessment ( n = 12) were performed at 1 month after surgery. Results The total time to complete the battery was approximately 60 minutes. A total of 78% of patients demonstrated significant impairment on one or more cognitive test, whereas 37% had impaired fluid cognition. Crystallized and overall composite cognitive scores were relatively intact, with 16% of patients showing significant impairment. A total of 22% of patients had impaired strength in the left hand, and 22% had impaired dexterity in both hands. In addition, 50% of patients showed impairment in one or more emotional domain. At 1 month after surgery, a significant decrease in crystallized cognition was observed. Conclusions The NIH Toolbox represents a feasible alternative to current neuropsychological batteries in the assessment of neurosurgical patients. It can be administered quickly, inexpensively, and will give the neurosurgical community a common currency when reporting neuropsychological results.

Resting-state functional magnetic resonance imaging (fMRI) uses spontaneous regional brain activity to identify functional networks. Transfer functions (TF) can evaluate the amplification of resting-state fMRI signal frequency components from one brain region to another, but are highly susceptible to noise spikes. Resting-state fMRI’s low-temporal resolution implies that the high frequency noise characteristics necessary to implement Weiner filtering are not available. We investigated new approaches that replace the standard Weiner filter noise parameter with an alternative outlier suppression parameter (OSP) to identify and remove inaccurate TF estimates. When compared to standard TF approaches, our new filtering approaches shows an improved ability to distinguish optic neuritis (ON) patients from healthy volunteers, as well as patients experiencing ON as a clinically isolated syndrome (CIS) from ON patients with relapsing-remitting multiple sclerosis (RRMS).

Highlights • PSMD, a marker of global white matter microstructure disruption, is increased in CAA. • PSMD in CAA participants is associated with processing speed. • Changes in PSMD were similar in CAA, NC, MCI, and AD over 1 year., Objectives To test the hypotheses that peak skeletonized mean diffusivity (PSMD), a measure of cerebral white matter microstructural disruption, is 1) increased in patients with cerebral amyloid angiopathy (CAA) compared to normal control (NC), mild cognitive impairment (MCI), and Alzheimer’s disease (AD); 2) associated with neuropsychological test performance among CAA patients; and 3) increased more quickly over one year in CAA than in AD, MCI, and NC. Methods Ninety-two participants provided a medical history, completed a neuropsychological assessment, and had a magnetic resonance (MR) exam including diffusion tensor imaging (DTI) from which PSMD was calculated. A 75-minute neuropsychological test battery was used to derive domain scores for memory, executive function, and processing speed. Multivariable analyses controlling for age and sex (and education, for cognitive outcomes) were used to test the study hypotheses. Results PSMD was higher in the CAA group (mean 4.97 × 10−4 mm2/s) compared to NC (3.25 × 10−4 mm2/s), MCI (3.62 × 10−4 mm2/s) and AD (3.89 × 10−4 mm2/s) groups (p

ObjectiveTo assess cerebrovascular reactivity in response to a visual task in participants with cerebral amyloid angiopathy (CAA), Alzheimer disease (AD), and mild cognitive impairment (MCI) using fMRI.MethodsThis prospective cohort study included 40 patients with CAA, 22 with AD, 27 with MCI, and 25 healthy controls. Each participant underwent a visual fMRI task using a contrast-reversing checkerboard stimulus. Visual evoked potentials (VEPs) were used to compare visual cortex neuronal activity in 83 participants. General linear models using least-squares means, adjusted for multiple comparisons with the Tukey test, were used to estimate mean blood oxygen level–dependent (BOLD) signal change during the task and VEP differences between groups.ResultsAfter adjustment for age and hypertension, estimated mean BOLD response amplitude was as follows: CAA 1.88% (95% confidence interval [CI] 1.60%–2.15%), AD 2.26% (1.91%–2.61%), MCI 2.15% (1.84%–2.46%), and control 2.65% (2.29%–3.00%). Only patients with CAA differed from controls (p = 0.01). In the subset with VEPs, group was not associated with prolonged latencies or lower amplitudes. Lower BOLD amplitude response was associated with higher white matter hyperintensity (WMH) volumes in CAA (for each 0.1% lower BOLD response amplitude, the WMH volume was 9.2% higher, 95% CI 6.0%–12.4%) but not other groups (p = 0.002 for interaction) when controlling for age and hypertension.ConclusionsMean visual BOLD response amplitude was lowest in participants with CAA compared to controls, without differences in VEP latencies and amplitudes. This suggests that the impaired visual BOLD response is due to reduced vascular reactivity in CAA. In contrast to participants with CAA, the visual BOLD response amplitude did not differ between those with AD or MCI and controls.

Spontaneous fluctuations of blood-oxygenation level-dependent functional magnetic resonance imaging (BOLD fMRI) signals are highly synchronous between brain regions that serve similar functions. This provides a means to investigate functional networks; however, most analysis techniques assume functional connections are constant over time. This may be problematic in the case of neurological disease, where functional connections may be highly variable. Recently, several methods have been proposed to determine moment-to-moment changes in the strength of functional connections over an imaging session (so called dynamic connectivity). Here a novel analysis framework based on a hierarchical observation modeling approach was proposed, to permit statistical inference of the presence of dynamic connectivity. A two-level linear model composed of overlapping sliding windows of fMRI signals, incorporating the fact that overlapping windows are not independent was described. To test this approach, datasets were synthesized whereby functional connectivity was either constant (significant or insignificant) or modulated by an external input. The method successfully determines the statistical significance of a functional connection in phase with the modulation, and it exhibits greater sensitivity and specificity in detecting regions with variable connectivity, when compared with sliding-window correlation analysis. For real data, this technique possesses greater reproducibility and provides a more discriminative estimate of dynamic connectivity than sliding-window correlation analysis. Hum Brain Mapp 37:4566-4580, 2016. © 2016 Wiley Periodicals, Inc.