Cardiogenic control of affective behavioural state.

Emotional states influence bodily physiology, as exemplified in the top-down process by which anxiety causes faster beating of the heart1-3. However, whether an increased heart rate might itself induce anxiety or fear responses is unclear3-8. Physiological theories of emotion, proposed over a century ago, have considered that in general, there could be an important and even dominant flow of information from the body to the brain9. Here, to formally test this idea, we developed a noninvasive optogenetic pacemaker for precise, cell-type-specific control of cardiac rhythms of up to 900 beats per minute in freely moving mice, enabled by a wearable micro-LED harness and the systemic viral delivery of a potent pump-like channelrhodopsin. We found that optically evoked tachycardia potently enhanced anxiety-like behaviour, but crucially only in risky contexts, indicating that both central (brain) and peripheral (body) processes may be involved in the development of emotional states. To identify potential mechanisms, we used whole-brain activity screening and electrophysiology to find brain regions that were activated by imposed cardiac rhythms. We identified the posterior insular cortex as a potential mediator of bottom-up cardiac interoceptive processing, and found that optogenetic inhibition of this brain region attenuated the anxiety-like behaviour that was induced by optical cardiac pacing. Together, these findings reveal that cells of both the body and the brain must be considered together to understand the origins of emotional or affective states. More broadly, our results define a generalizable approach for noninvasive, temporally precise functional investigations of joint organism-wide interactions among targeted cells during behaviour.

Network response of brain microvasculature to neuronal stimulation.

Neurovascular coupling (NVC), or the adjustment of blood flow in response to local increases in neuronal activity is a hallmark of healthy brain function, and the physiological foundation for functional magnetic resonance imaging (fMRI). However, it remains only partly understood due to the high complexity of the structure and function of the cerebrovascular network. Here we set out to understand NVC at the network level, i.e. map cerebrovascular network reactivity to activation of neighbouring neurons within a 500×500×500 µm3 cortical volume (∼30 high-resolution 3-nL fMRI voxels). Using 3D two-photon fluorescence microscopy data, we quantified blood volume and flow changes in the brain vessels in response to spatially targeted optogenetic activation of cortical pyramidal neurons. We registered the vessels in a series of image stacks acquired before and after stimulations and applied a deep learning pipeline to segment the microvascular network from each time frame acquired. We then performed image analysis to extract the microvascular graphs, and graph analysis to identify the branch order of each vessel in the network, enabling the stratification of vessels by their branch order, designating branches 1-3 as precapillary arterioles and branches 4+ as capillaries. Forty-five percent of all vessels showed significant calibre changes; with 85 % of responses being dilations. The largest absolute CBV change was in the capillaries; the smallest, in the venules. Capillary CBV change was also the largest fraction of the total CBV change, but normalized to the baseline volume, arterioles and precapillary arterioles showed the biggest relative CBV change. From linescans along arteriole-venule microvascular paths, we measured red blood cell velocities and hematocrit, allowing for estimation of pressure and local resistance along these paths. While diameter changes following neuronal activation gradually declined along the paths; the pressure drops from arterioles to venules increased despite decreasing resistance: blood flow thus increased more than local resistance decreases would predict. By leveraging functional volumetric imaging and high throughput deep learning-based analysis, our study revealed distinct hemodynamic responses across the vessel types comprising the microvascular network. Our findings underscore the need for large, dense sampling of brain vessels for characterization of neurovascular coupling at the network level in health and disease.

Longitudinal Alterations of Cerebral Blood Flow in High-Contact Sports.

OBJECTIVE: Repetitive head trauma is common in high-contact sports. Cerebral blood flow (CBF) can measure changes in brain perfusion that could indicate injury. Longitudinal studies with a control group are necessary to account for interindividual and developmental effects. We investigated whether exposure to head impacts causes longitudinal CBF changes. METHODS: We prospectively studied 63 American football (high-contact cohort) and 34 volleyball (low-contact controls) male collegiate athletes, tracking CBF using 3D pseudocontinuous arterial spin labeling magnetic resonance imaging for up to 4 years. Regional relative CBF (rCBF, normalized to cerebellar CBF) was computed after co-registering to T1-weighted images. A linear mixed effects model assessed the relationship of rCBF to sport, time, and their interaction. Within football players, we modeled rCBF against position-based head impact risk and baseline Standardized Concussion Assessment Tool score. Additionally, we evaluated early (1-5 days) and delayed (3-6 months) post-concussion rCBF changes (in-study concussion). RESULTS: Supratentorial gray matter rCBF declined in football compared with volleyball (sport-time interaction p = 0.012), with a strong effect in the parietal lobe (p = 0.002). Football players with higher position-based impact-risk had lower occipital rCBF over time (interaction p = 0.005), whereas players with lower baseline Standardized Concussion Assessment Tool score (worse performance) had relatively decreased rCBF in the cingulate-insula over time (interaction effect p = 0.007). Both cohorts showed a left-right rCBF asymmetry that decreased over time. Football players with an in-study concussion showed an early increase in occipital lobe rCBF (p = 0.0166). INTERPRETATION: These results suggest head impacts may result in an early increase in rCBF, but cumulatively a long-term decrease in rCBF. ANN NEUROL 2023;94:457-469.

Blood-brain barrier opening of the default mode network in Alzheimer's disease with magnetic resonance-guided focused ultrasound.

The blood-brain barrier (BBB) protects the brain but is also an important obstacle for the effective delivery of therapeutics in Alzheimer's disease and other neurodegenerative disorders. Transcranial magnetic resonance-guided focused ultrasound (MRgFUS) has been shown to reversibly disrupt the BBB. However, treatment of diffuse regions across the brain along with the effect on Alzheimer's disease relevant pathology need to be better characterized. This study is an open-labelled single-arm trial (NCT04118764) to investigate the feasibility of modulating BBB permeability in the default mode network and the impact on cognition, amyloid and tau pathology as well as BBB integrity. Nine participants [mean age 70.2 ± 7.2 years, mean Mini-Mental State Examination (MMSE) 21.9] underwent three biweekly procedures with follow-up visits up to 6 months. The BBB permeability of the bilateral hippocampi, anterior cingulate cortex and precuneus was transiently increased without grade 3 or higher adverse events. Participants did not experience worsening trajectory of cognitive decline (ADAS-cog11, MMSE). Whole brain vertex-based analysis of the 18F-florbetaben PET imaging demonstrated clusters of modest SUVR reduction in the right parahippocampal and inferior temporal lobe. However, CSF and blood biomarkers did not demonstrate any amelioration of Alzheimer's disease pathology (P-tau181, amyloid-ß42/40 ratio), nor did it show persistent BBB dysfunction (plasma PDGFRbeta and CSF-to-plasma albumin ratio). This study provides neuroimaging and fluid biomarker data to characterize the safety profile of MRgFUS BBB modulation in neurodegeneration as a potential strategy for enhanced therapeutic delivery.

Distinct spatial contributions of amyloid pathology and cerebral small vessel disease to hippocampal morphology.

INTRODUCTION: Cerebral small vessel disease (SVD) and amyloid beta (Aß) pathology frequently co-exist. The impact of concurrent pathology on the pattern of hippocampal atrophy, a key substrate of memory impacted early and extensively in dementia, remains poorly understood. METHODS: In a unique cohort of mixed Alzheimer's disease and moderate-severe SVD, we examined whether total and regional neuroimaging measures of SVD, white matter hyperintensities (WMH), and Aß, as assessed by 18F-AV45 positron emission tomography, exert additive or synergistic effects on hippocampal volume and shape. RESULTS: Frontal WMH, occipital WMH, and Aß were independently associated with smaller hippocampal volume. Frontal WMH had a spatially distinct impact on hippocampal shape relative to Aß. In contrast, hippocampal shape alterations associated with occipital WMH spatially overlapped with Aß-vulnerable subregions. DISCUSSION: Hippocampal degeneration is differentially sensitive to SVD and Aß pathology. The pattern of hippocampal atrophy could serve as a disease-specific biomarker, and thus guide clinical diagnosis and individualized treatment strategies for mixed dementia.

ROOD-MRI: Benchmarking the robustness of deep learning segmentation models to out-of-distribution and corrupted data in MRI.

Deep artificial neural networks (DNNs) have moved to the forefront of medical image analysis due to their success in classification, segmentation, and detection challenges. A principal challenge in large-scale deployment of DNNs in neuroimage analysis is the potential for shifts in signal-to-noise ratio, contrast, resolution, and presence of artifacts from site to site due to variances in scanners and acquisition protocols. DNNs are famously susceptible to these distribution shifts in computer vision. Currently, there are no benchmarking platforms or frameworks to assess the robustness of new and existing models to specific distribution shifts in MRI, and accessible multi-site benchmarking datasets are still scarce or task-specific. To address these limitations, we propose ROOD-MRI: a novel platform for benchmarking the Robustness of DNNs to Out-Of-Distribution (OOD) data, corruptions, and artifacts in MRI. This flexible platform provides modules for generating benchmarking datasets using transforms that model distribution shifts in MRI, implementations of newly derived benchmarking metrics for image segmentation, and examples for using the methodology with new models and tasks. We apply our methodology to hippocampus, ventricle, and white matter hyperintensity segmentation in several large studies, providing the hippocampus dataset as a publicly available benchmark. By evaluating modern DNNs on these datasets, we demonstrate that they are highly susceptible to distribution shifts and corruptions in MRI. We show that while data augmentation strategies can substantially improve robustness to OOD data for anatomical segmentation tasks, modern DNNs using augmentation still lack robustness in more challenging lesion-based segmentation tasks. We finally benchmark U-Nets and vision transformers, finding robustness susceptibility to particular classes of transforms across architectures. The presented open-source platform enables generating new benchmarking datasets and comparing across models to study model design that results in improved robustness to OOD data and corruptions in MRI.

Feasibility of diffusion-tensor and correlated diffusion imaging for studying white-matter microstructural abnormalities: Application in COVID-19.

There has been growing attention on the effect of COVID-19 on white-matter microstructure, especially among those that self-isolated after being infected. There is also immense scientific interest and potential clinical utility to evaluate the sensitivity of single-shell diffusion magnetic resonance imaging (MRI) methods for detecting such effects. In this work, the performances of three single-shell-compatible diffusion MRI modeling methods are compared for detecting the effect of COVID-19, including diffusion-tensor imaging, diffusion-tensor decomposition of orthogonal moments and correlated diffusion imaging. Imaging was performed on self-isolated patients at the study initiation and 3-month follow-up, along with age- and sex-matched controls. We demonstrate through simulations and experimental data that correlated diffusion imaging is associated with far greater sensitivity, being the only one of the three single-shell methods to demonstrate COVID-19-related brain effects. Results suggest less restricted diffusion in the frontal lobe in COVID-19 patients, but also more restricted diffusion in the cerebellar white matter, in agreement with several existing studies highlighting the vulnerability of the cerebellum to COVID-19 infection. These results, taken together with the simulation results, suggest that a significant proportion of COVID-19 related white-matter microstructural pathology manifests as a change in tissue diffusivity. Interestingly, different b-values also confer different sensitivities to the effects. No significant difference was observed in patients at the 3-month follow-up, likely due to the limited size of the follow-up cohort. To summarize, correlated diffusion imaging is shown to be a viable single-shell diffusion analysis approach that allows us to uncover opposing patterns of diffusion changes in the frontal and cerebellar regions of COVID-19 patients, suggesting the two regions react differently to viral infection.

Progressive White Matter Injury in Preclinical Dutch Cerebral Amyloid Angiopathy.

Autosomal-dominant, Dutch-type cerebral amyloid angiopathy (D-CAA) offers a unique opportunity to develop biomarkers for pre-symptomatic cerebral amyloid angiopathy (CAA). We hypothesized that neuroimaging measures of white matter injury would be present and progressive in D-CAA prior to hemorrhagic lesions or symptomatic hemorrhage. In a longitudinal cohort of D-CAA carriers and non-carriers, we observed divergence of white matter injury measures between D-CAA carriers and non-carriers prior to the appearance of cerebral microbleeds and >14 years before the average age of first symptomatic hemorrhage. These results indicate that white matter disruption measures may be valuable cross-sectional and longitudinal biomarkers of D-CAA progression. ANN NEUROL 2022;92:358-363.

MRI Assessment of Cerebral Blood Flow in Nonhospitalized Adults Who Self-Isolated Due to COVID-19.

BACKGROUND: Neurological symptoms associated with coronavirus disease 2019 (COVID-19), such as fatigue and smell/taste changes, persist beyond infection. However, little is known of brain physiology in the post-COVID-19 timeframe. PURPOSE: To determine whether adults who experienced flu-like symptoms due to COVID-19 would exhibit cerebral blood flow (CBF) alterations in the weeks/months beyond infection, relative to controls who experienced flu-like symptoms but tested negative for COVID-19. STUDY TYPE: Prospective observational. POPULATION: A total of 39 adults who previously self-isolated at home due to COVID-19 (41.9 ± 12.6 years of age, 59% female, 116.5 ± 62.2 days since positive diagnosis) and 11 controls who experienced flu-like symptoms but had a negative COVID-19 diagnosis (41.5 ± 13.4 years of age, 55% female, 112.1 ± 59.5 since negative diagnosis). FIELD STRENGTH AND SEQUENCES: A 3.0 T; T1-weighted magnetization-prepared rapid gradient and echo-planar turbo gradient-spin echo arterial spin labeling sequences. ASSESSMENT: Arterial spin labeling was used to estimate CBF. A self-reported questionnaire assessed symptoms, including ongoing fatigue. CBF was compared between COVID-19 and control groups and between those with (n = 11) and without self-reported ongoing fatigue (n = 28) within the COVID-19 group. STATISTICAL TESTS: Between-group and within-group comparisons of CBF were performed in a voxel-wise manner, controlling for age and sex, at a family-wise error rate of 0.05. RESULTS: Relative to controls, the COVID-19 group exhibited significantly decreased CBF in subcortical regions including the thalamus, orbitofrontal cortex, and basal ganglia (maximum cluster size = 6012 voxels and maximum t-statistic = 5.21). Within the COVID-19 group, significant CBF differences in occipital and parietal regions were observed between those with and without self-reported on-going fatigue. DATA CONCLUSION: These cross-sectional data revealed regional CBF decreases in the COVID-19 group, suggesting the relevance of brain physiology in the post-COVID-19 timeframe. This research may help elucidate the heterogeneous symptoms of the post-COVID-19 condition. EVIDENCE LEVEL: 2. TECHNICAL EFFICACY: Stage 3.

Deep brain stimulation of the nucleus accumbens in the treatment of severe alcohol use disorder: a phase I pilot trial.

Alcohol use disorder (AUD) is a highly prevalent, often refractory, medical illness. The symptoms of AUD are driven by dysfunction in several neurocircuits centered on the nucleus accumbens (NAc). Case reports and animal studies suggest NAc-DBS may be an effective harm-reduction treatment in severe AUD. Six patients with severe, refractory AUD underwent NAc-DBS. Safety metrics and clinical outcomes were recorded. Positron emission tomography (FDG-PET) was used to measure glucose metabolism in the NAc at baseline and 6 months. Functional magnetic resonance imaging (fMRI) was used to characterize postoperative changes in NAc functional connectivity to the rest of the brain, as well as NAc and dorsal striatal reactivity to alcoholic visual cues. This study was registered with ClinicalTrials.gov, NCT03660124. All patients experienced a reduction in craving. There was a significant reduction in alcohol consumption, alcohol-related compulsivity, and anxiety at 12 months. There was no significant change in depression. FDG-PET analysis demonstrated reduced NAc metabolism by 6 months, which correlated with improvements in compulsive drinking behaviors. Clinical improvement correlated with reduced functional connectivity between the NAc and the visual association cortex. Active DBS was associated with reduced activation of the dorsal striatum during passive viewing of alcohol-containing pictures. NAc-DBS is feasible and safe in patients with severe, otherwise refractory AUD. It is associated with a reduction in cravings and addictive behavior. A potential mechanism underlying this process is a down-regulation of the NAc, a disruption of its functional connectivity to the visual association cortex, and interference of cue-elicited dorsal striatum reactivity. Trial Registration NCT03660124 ( www.clinicaltrials.gov ).

Vascular burden and cognition: Mediating roles of neurodegeneration and amyloid PET.

It remains unclear to what extent cerebrovascular burden relates to amyloid beta (Aß) deposition, neurodegeneration, and cognitive dysfunction in mixed disease populations with small vessel disease and Alzheimer's disease (AD) pathology. In 120 subjects, we investigated the association of vascular burden (white matter hyperintensity [WMH] volumes) with cognition. Using mediation analyses, we tested the indirect effects of WMH on cognition via Aß deposition (18 F-AV45 positron emission tomography [PET]) and neurodegeneration (cortical thickness or 18 F fluorodeoxyglucose PET) in AD signature regions. We observed that increased total WMH volume was associated with poorer performance in all tested cognitive domains, with the strongest effects observed for semantic fluency. These relationships were mediated mainly via cortical thinning, particularly of the temporal lobe, and to a lesser extent serially mediated via Aß and cortical thinning of AD signature regions. WMH volumes differentially impacted cognition depending on lobar location and Aß status. In summary, our study suggests mainly an amyloid-independent pathway in which vascular burden affects cognitive function via localized neurodegeneration. HIGHLIGHTS: Alzheimer's disease often co-exists with vascular pathology. We studied a unique cohort enriched for high white matter hyperintensities (WMH). High WMH related to cognitive impairment of semantic fluency and executive function. This relationship was mediated via temporo-parietal atrophy rather than metabolism. This relationship was, to lesser extent, serially mediated via amyloid beta and atrophy.

Secondary thalamic atrophy related to brain infarction may contribute to post-stroke cognitive impairment.

BACKGROUND AND PURPOSE: The thalamus is a key brain hub that is globally connected to many cortical regions. Previous work highlights thalamic contributions to multiple cognitive functions, but few studies have measured thalamic volume changes or cognitive correlates. This study investigates associations between thalamic volumes and post-stroke cognitive function. METHODS: Participants with non-thalamic brain infarcts (3-42 months) underwent MRI and cognitive testing. Focal infarcts and thalami were traced manually. In cases with bilateral infarcts, the side of the primary infarct volume defined the hemisphere involved. Brain parcellation and volumetrics were extracted using a standardized and previously validated neuroimaging pipeline. Age and gender-matched healthy controls provided normal comparative thalamic volumes. Thalamic atrophy was considered when the volume exceeded 2 standard deviations greater than the controls. RESULTS: Thalamic volumes ipsilateral to the infarct in stroke patients (n=55) were smaller than left (4.4 ± 1.4 vs. 5.4 ± 0.5 cc, p < 0.001) and right (4.4 ± 1.4 vs. 5.5 ± 0.6 cc, p < 0.001) thalamic volumes in the controls. After controlling for head-size and global brain atrophy, infarct volume independently correlated with ipsilateral thalamic volume (ß= -0.069, p=0.024). Left thalamic atrophy correlated significantly with poorer cognitive performance (ß = 4.177, p = 0.008), after controlling for demographics and infarct volumes. CONCLUSIONS: Our results suggest that the remote effect of infarction on ipsilateral thalamic volume is associated with global post-stroke cognitive impairment.

Perivascular spaces mediate a relationship between diabetes and other cerebral small vessel disease markers in cerebrovascular and neurodegenerative diseases.

Type 2 diabetes mellitus (T2DM) and hypertension are risk factors for cerebral small vessel disease (SVD); however, few studies have characterised their relationships with MRI-visible perivascular spaces (PVS). MRI was used to quantify deep (d) and periventricular (p) white matter hyperintensities (WMH), lacunes, PVS in the white matter (wmPVS) or basal ganglia (bgPVS), and diffusion metrics in white matter. Patients with T2DM had greater wmPVS volume and there were greater wmPVS volumes in patients with T2DM and hypertension together. Counterfactual moderated mediation models found indirect effects of T2DM on volumes of other SVD and diffusion markers that were mediated by wmPVS: pWMH, dWMH, periventricular lacunes, and deep lacunes, and progression of deep lacunes over 1 year, in patients with hypertension, but not in patients without hypertension. Studying the regulation of cortical perivascular fluid dynamics may reveal mechanisms that mediate the impact of T2DM on cerebral small vessels.

Deep Bayesian networks for uncertainty estimation and adversarial resistance of white matter hyperintensity segmentation.

White matter hyperintensities (WMHs) are frequently observed on structural neuroimaging of elderly populations and are associated with cognitive decline and increased risk of dementia. Many existing WMH segmentation algorithms produce suboptimal results in populations with vascular lesions or brain atrophy, or require parameter tuning and are computationally expensive. Additionally, most algorithms do not generate a confidence estimate of segmentation quality, limiting their interpretation. MRI-based segmentation methods are often sensitive to acquisition protocols, scanners, noise-level, and image contrast, failing to generalize to other populations and out-of-distribution datasets. Given these concerns, we propose a novel Bayesian 3D convolutional neural network with a U-Net architecture that automatically segments WMH, provides uncertainty estimates of the segmentation output for quality control, and is robust to changes in acquisition protocols. We also provide a second model to differentiate deep and periventricular WMH. Four hundred thirty-two subjects were recruited to train the CNNs from four multisite imaging studies. A separate test set of 158 subjects was used for evaluation, including an unseen multisite study. We compared our model to two established state-of-the-art techniques (BIANCA and DeepMedic), highlighting its accuracy and efficiency. Our Bayesian 3D U-Net achieved the highest Dice similarity coefficient of 0.89 ± 0.08 and the lowest modified Hausdorff distance of 2.98 ± 4.40 mm. We further validated our models highlighting their robustness on "clinical adversarial cases" simulating data with low signal-to-noise ratio, low resolution, and different contrast (stemming from MRI sequences with different parameters). Our pipeline and models are available at: https://hypermapp3r.readthedocs.io.

Automated generation of cerebral blood flow and arterial transit time maps from multiple delay arterial spin-labeled MRI.

PURPOSE: Develop and evaluate a deep learning approach to estimate cerebral blood flow (CBF) and arterial transit time (ATT) from multiple post-labeling delay (PLD) ASL MRI. METHODS: ASL MRI were acquired with 6 PLDs on a 1.5T or 3T GE system in adults with and without cognitive impairment (N = 99). Voxel-level CBF and ATT maps were quantified by training models with distinct convolutional neural network architectures: (1) convolutional neural network (CNN) and (2) U-Net. Models were trained and compared via 5-fold cross validation. Performance was evaluated using mean absolute error (MAE). Model outputs were trained on and compared against a reference ASL model fitting after data cleaning. Minimally processed ASL data served as another benchmark. Model output uncertainty was estimated using Monte Carlo dropout. The better-performing neural network was subsequently re-trained on inputs with missing PLDs to investigate generalizability to different PLD schedules. RESULTS: Relative to the CNN, the U-Net yielded lower MAE on training data. On test data, the U-Net MAE was 8.4 ± 1.4 mL/100 g/min for CBF and 0.22 ± 0.09 s for ATT. A significant association was observed between MAE and Monte Carlo dropout-based uncertainty estimates. Neural network performance remained stable despite systematically reducing the number of input images (i.e., up to 3 missing PLD images). Mean processing time was 10.77 s for the U-Net neural network compared to 10 min 41 s for the reference pipeline. CONCLUSION: It is feasible to generate CBF and ATT maps from 1.5T and 3T multi-PLD ASL MRI with a fast deep learning image-generation approach.

Putaminal Recombinant Glucocerebrosidase Delivery with Magnetic Resonance-Guided Focused Ultrasound in Parkinson's Disease: A Phase I Study.

BACKGROUND: GBA1 mutation is the most common genetic risk factor for Parkinson's disease (PD). Replacement of the lysosomal enzyme glucocerebrosidase (GCase) slows neurodegeneration in PD models and may be a promising disease-modifying therapy in patients with PD. However, recombinant GCase has limited penetration through the blood-brain barrier (BBB). Microbubble-mediated magnetic resonance-guided focused ultrasound (MRgFUS) can reversibly disrupt the BBB for drug delivery. METHODS: This open-label phase I study investigated the safety and feasibility of MRgFUS putaminal delivery of intravenous GCase at escalating doses (15 to 30 to 60 IU/kg) every 2 weeks in four patients with PD with GBA1 mutations. RESULTS: BBB permeability was achieved and restored in all patients as quantified by dynamic contrast-enhanced magnetic resonance imaging after treatment. There were no serious adverse events. Two patients developed transient dyskinesia after treatment. Blinded Movement Disorder Society-Unified Parkinson's Disease Rating Scale motor scores off medication decreased by 12% at 6 months from baseline (from 26 ± 9 to 22 ± 6). Standardized uptake value ratio on fluorodeoxyglucose positron emission tomography imaging in the treated putamen reduced from 1.66 ± 0.14 to 1.27 ± 0.08. CONCLUSIONS: Results from this study demonstrate the safety and feasibility of MRgFUS GCase delivery in PD and support further investigation of this approach. © 2022 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Magnetic resonance-guided focused ultrasound capsulotomy for refractory obsessive compulsive disorder and major depressive disorder: clinical and imaging results from two phase I trials.

Obsessive compulsive disorder (OCD) and major depressive disorder (MDD) are common, often refractory, neuropsychiatric conditions for which new treatment approaches are urgently needed. Magnetic resonance-guided focused ultrasound (MRgFUS) is a novel surgical technique permitting incisionless ablative neurosurgery. We examined the safety profile, clinical response, and imaging correlates of MRgFUS bilateral anterior capsulotomy in patients with refractory obsessive compulsive disorder (OCD, N = 6) and major depressive disorder (MDD, n = 6). There were no serious adverse events. Nonserious adverse events included headaches and pin-site swelling in 7/12 patients. The response rate was 4/6 and 2/6 in the OCD and MDD cohorts respectively. To delineate the white-matter tracts impacted by capsulotomy, a normative diffusion MRI-based structural connectome was used, revealing tracts terminating primarily in the frontal pole, medial thalamus, striatum, and medial-temporal lobe. Positron emission tomography (PET) analysis (nine subjects) revealed widespread decreases in metabolism bilaterally in the cerebral hemispheres at 6 months post treatment, as well as in the right hippocampus, amygdala, and putamen. A pretreatment seed-to-voxel resting-state functional magnetic resonance imaging (rs-fMRI) analysis (12 subjects) revealed three voxel clusters significantly associated with eventual clinical response. MRgFUS capsulotomy appears to be safe, well tolerated, and according to these initial results, may be an important treatment option for patients with refractory OCD and MDD. MRgFUS capsulotomy results in both targeted and widespread changes in neural activity, and neuroimaging may hold potential for the prediction of outcome.

Longitudinal alteration of cortical thickness and volume in high-impact sports.

Collegiate football athletes are subject to repeated head impacts. The purpose of this study was to determine whether this exposure can lead to changes in brain structure. This prospective cohort study was conducted with up to 4 years of follow-up on 63 football (high-impact) and 34 volleyball (control) male collegiate athletes with a total of 315 MRI scans (after exclusions: football n â€‹= â€‹50, volleyball n â€‹= â€‹24, total scans â€‹= â€‹273) using high-resolution structural imaging. Volumetric and cortical thickness estimates were derived using FreeSurfer 5.3's longitudinal pipeline. A linear mixed-effects model assessed the effect of group (football vs. volleyball), time from baseline MRI, and the interaction between group and time. We confirmed an expected developmental decrement in cortical thickness and volume in our cohort (p â€‹< â€‹.001). Superimposed on this, total cortical gray matter volume (p â€‹= â€‹.03) and cortical thickness within the left hemisphere (p â€‹= â€‹.04) showed a group by time interaction, indicating less age-related volume reduction and thinning in football compared to volleyball athletes. At the regional level, sport by time interactions on thickness and volume were identified in the left orbitofrontal (p â€‹= â€‹.001), superior temporal (p â€‹= â€‹.001), and postcentral regions (p â€‹< â€‹.001). Additional cortical thickness interactions were found in the left temporal pole (p â€‹= â€‹.003) and cuneus (p â€‹= â€‹.005). At the regional level, we also found main effects of sport in football athletes characterized by reduced volume in the right hippocampus (p â€‹= â€‹.003), right superior parietal cortical gray (p â€‹< â€‹.001) and white matter (p â€‹< â€‹.001), and increased volume of the left pallidum (p â€‹= â€‹.002). Within football, cortical thickness was higher with greater years of prior play (left hemisphere p â€‹= â€‹.013, right hemisphere p â€‹= â€‹.005), and any history of concussion was associated with less cortical thinning (left hemisphere p â€‹= â€‹.010, right hemisphere p â€‹= â€‹.011). Additionally, both position-associated concussion risk (p â€‹= â€‹.002) and SCAT scores (p â€‹= â€‹.023) were associated with less of the expected volume decrement of deep gray structures. This prospective longitudinal study comparing football and volleyball athletes shows divergent age-related trajectories of cortical thinning, possibly reflecting an impact-related alteration of normal cortical development. This warrants future research into the underlying mechanisms of impacts to the head on cortical maturation.

Hippocampal segmentation for brains with extensive atrophy using three-dimensional convolutional neural networks.

Hippocampal volumetry is a critical biomarker of aging and dementia, and it is widely used as a predictor of cognitive performance; however, automated hippocampal segmentation methods are limited because the algorithms are (a) not publicly available, (b) subject to error with significant brain atrophy, cerebrovascular disease and lesions, and/or (c) computationally expensive or require parameter tuning. In this study, we trained a 3D convolutional neural network using 259 bilateral manually delineated segmentations collected from three studies, acquired at multiple sites on different scanners with variable protocols. Our training dataset consisted of elderly cases difficult to segment due to extensive atrophy, vascular disease, and lesions. Our algorithm, (HippMapp3r), was validated against four other publicly available state-of-the-art techniques (HippoDeep, FreeSurfer, SBHV, volBrain, and FIRST). HippMapp3r outperformed the other techniques on all three metrics, generating an average Dice of 0.89 and a correlation coefficient of 0.95. It was two orders of magnitude faster than some of the tested techniques. Further validation was performed on 200 subjects from two other disease populations (frontotemporal dementia and vascular cognitive impairment), highlighting our method's low outlier rate. We finally tested the methods on real and simulated "clinical adversarial" cases to study their robustness to corrupt, low-quality scans. The pipeline and models are available at: https://hippmapp3r.readthedocs.ioto facilitate the study of the hippocampus in large multisite studies.

Predicting response to psychiatric surgery: a systematic review of neuroimaging findings.

Background: Psychiatric surgery, including deep brain stimulation and stereotactic ablation, is an important treatment option in severe refractory psychiatric illness. Several large trials have demonstrated response rates of approximately 50%, underscoring the need to identify and select responders preoperatively. Recent advances in neuroimaging have brought this possibility into focus. We systematically reviewed the psychiatric surgery neuroimaging literature to assess the current state of evidence for preoperative imaging predictors of response. Methods: We performed this study in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) and Meta-analysis of Observational Studies in Epidemiology (MOOSE) frameworks, and preregistered it using PROSPERO. We systematically searched the Medline, Embase and Cochrane databases for studies reporting preoperative neuroimaging analyses correlated with clinical outcomes in patients who underwent psychiatric surgery. We recorded and synthesized the methodological details, imaging results and clinical correlations from these studies. Results: After removing duplicates, the search yielded 8388 unique articles, of which 7 met the inclusion criteria. The included articles were published between 2001 and 2018 and reported on the outcomes of 101 unique patients. Of the 6 studies that reported significant findings, all identified clusters of hypermetabolism, hyperconnectivity or increased size in the frontostriatal limbic circuitry. Limitations: The included studies were few and highly varied, spanning 2 decades. Conclusion: Although few studies have analyzed preoperative imaging for predictors of response to psychiatric surgery, we found consistency among the reported results: most studies implicated overactivity in the frontostriatal limbic network as being correlated with clinical response. Larger prospective studies are needed. Registration: www.crd.york.ac.uk/prospero/display_record.php?RecordID=131151.