Search

Abstract Chemical exchange saturation transfer (CEST) imaging may provide novel contrast for the diagnosis, prognosis, and monitoring of the progression or treatment of neurological applications. However, the reproducibility of prominent CEST contrasts like amide CEST and nuclear Overhauser enhancement (NOE) CEST must be characterized in healthy brain gray matter (GM) and white matter (GM) prior to clinical implementation. The objective of this study was to characterize the reproducibility of four different CEST contrasts in the healthy human brain. Using a 3T MRI scanner, two 3D CEST scans were acquired in 12 healthy subjects (7 females, mean age (± SD) 26 ± 4 years) approximately 10 days apart. Scan-rescan reproducibility was measured for four contrasts: amine/amide concentration-independent detection (AACID), Amide*, and inverse magnetization transfer ratio (MTRRex) contrast for amide and NOE. Reproducibility was evaluated between- and within-subjects using coefficients of variation (CV) and the percent difference between measurements. AACID and NOE-MTRRex contrasts demonstrated the lowest within-subject CVs (0.8–1.2% and 1.6-2.0%, respectively), between-subject CVs (1.2–2.1% and 3.4–4.2%, respectively), and percent difference (1.2–1.4% and 2.2–2.8%, respectively) for both GM and WM. AACID and NOE-MTRRex contrasts demonstrated the highest reproducibility and represented stable measurements suitable for characterizing changes in brain tissue caused by pathological processes.

Summary: The astrocyte-neuron lactate shuttle (ANLS) model posits that astrocyte-generated lactate is transported to neurons to fuel memory processes. However, neurons express high levels of lactate dehydrogenase A (LDHA), the rate-limiting enzyme of lactate production, suggesting a cognitive role for neuronally generated lactate. It was hypothesized that lactate metabolism in neurons is critical for learning and memory. Here transgenic mice were generated to conditionally induce or knockout (KO) the Ldha gene in CNS neurons of adult mice. High pattern separation memory was enhanced by neuronal Ldha induction in young females, and by neuronal Ldha KO in aged females. In older mice, Ldha induction caused cognitive deficits whereas Ldha KO caused cognitive improvements. Genotype-associated cognitive changes were often only observed in one sex or oppositely in males and females. Thus, neuronal-generated lactate has sex-specific cognitive effects, is largely indispensable at young age, and may be detrimental to learning and memory with aging.

Abstract Background Neuropsychiatric symptoms (NPS) are a core feature of most neurodegenerative and cerebrovascular diseases. White matter hyperintensities and brain atrophy have been implicated in NPS. We aimed to investigate the relative contribution of white matter hyperintensities and cortical thickness to NPS in participants across neurodegenerative and cerebrovascular diseases. Methods Five hundred thirteen participants with one of these conditions, i.e. Alzheimer’s Disease/Mild Cognitive Impairment, Amyotrophic Lateral Sclerosis, Frontotemporal Dementia, Parkinson’s Disease, or Cerebrovascular Disease, were included in the study. NPS were assessed using the Neuropsychiatric Inventory – Questionnaire and grouped into hyperactivity, psychotic, affective, and apathy subsyndromes. White matter hyperintensities were quantified using a semi-automatic segmentation technique and FreeSurfer cortical thickness was used to measure regional grey matter loss. Results Although NPS were frequent across the five disease groups, participants with frontotemporal dementia had the highest frequency of hyperactivity, apathy, and affective subsyndromes compared to other groups, whilst psychotic subsyndrome was high in both frontotemporal dementia and Parkinson’s disease. Results from univariate and multivariate results showed that various predictors were associated with neuropsychiatric subsyndromes, especially cortical thickness in the inferior frontal, cingulate, and insula regions, sex(female), global cognition, and basal ganglia-thalamus white matter hyperintensities. Conclusions In participants with neurodegenerative and cerebrovascular diseases, our results suggest that smaller cortical thickness and white matter hyperintensity burden in several cortical-subcortical structures may contribute to the development of NPS. Further studies investigating the mechanisms that determine the progression of NPS in various neurodegenerative and cerebrovascular diseases are needed.

Introduction: MRI-visible perivascular spaces (PVS) are a neuroimaging feature of cerebral small vessel disease and are commonly observed in patients with cerebrovascular and neurodegenerative disease. However, it is unclear whether PVS burden is associated with cognition. The aim of this study was to investigate the potential associations between PVS volumes, brain atrophy, white matter hyperintensities (WMH), and speeded executive function, in patients from the Ontario Neurodegenerative Disease Research Initiative (ONDRI). Methods: ONDRI participants (n=333) clinically diagnosed with Alzheimer's disease/mild cognitive impairment (ADMCI), frontotemporal dementia (FTD), and cerebrovascular diseases (CVD), with clinical, neuropsychological, MRI, plasma biomarkers (glial fibrillary acidic protein (GFAP); neurofilament light, (NfL); p-tau181; Aβ42/40), and available apolipoprotein E epsilon 4 (APOE E4) status, were included in this analysis. MRI-based measurements for brain parenchymal fraction (BPF), lacunes, PVS and WMH were extracted using the ONDRI imaging pipeline. The neuropsychological test scores were standardized (z-transformed), speeded executive function z- scores were computed as the mean of digit symbol modalities test and trail making test part B timed z-scores. The variables selected in bivariate analysis (p

Introduction: White matter hyperintensities (WMH) and MRI-visible perivascular spaces (PVS) are neuroimaging features of cerebral small vessel disease (CSVD). PVS are believed to play a role in cerebral metabolic waste clearance, particularly during sleep, and emerging evidence suggests that sleep disturbances are among the earliest symptoms of dementia. However, PVS quantification remains challenging and not accessible, particularly in complex patients with neurovascular and neurodegenerative disease. We developed and validated an automated deep learning-based PVS quantification method using multi-site patient MRI with varying degrees of CSVD burden. Additionally, we examined the correlation between PVS volumes and age in patients undergoing treatment for sleep apnea. Methods: MRI (training/validation data=141; testing=15) were obtained from various multi-site studies (ONDRI, CAHHM, Leducq SVD-PVS, CAIN). We developed a deep learning convolutional neural net (CNN) model with a U-net architecture for PVS segmentation using T1-weighted images. Ground truth data used for model training were generated by first applying the RORPO filter to extract small tubular structures, then refinement of the RORPO output around WMH, followed by removal of probable non-PVS objects from the RORPO-based output using Freesurfer regions of interest, and finally manually correcting the remaining errors. After ground truth generation and model training, the final CNN output was used to examine the association between PVS volumes and age in patients with sleep apnea (n=42). Results: After CNN training, our PVS tool completes full segmentation in under 3 minutes (NVIDIA RTX3090). It achieved excellent performance on the test data, with a mean Dice score of 0.95, indicating outstanding agreement with ground truth. When applied to the patients undergoing interventional sleep apnea treatment, a strong positive relationship was found between total PVS volume and age (r=0.57 [0.38 0.72]). Discussion: Our findings suggest that our automated PVS segmentation method can quickly and accurately quantify PVS in neurodegenerative and neurovascular patients with varying degrees of CSVD burden. The strong association between age and PVS in patients with sleep apnea further demonstrates the utility of our method and underscores the importance of further investigating the role of PVS in the context of sleep, aging, and neurodegenerative processes.

Background: Degenerative cervical myelopathy is a spinal disorder resulting in progressive cord compression and neurological deficits that are assessed using the modified Japanese Orthopedic Association (mJOA) questionnaire. It is difficult to predict which patients will recover neurological function after surgery, making it challenging for clinicians to set postoperative patient expectations. In this study, we used mJOA subscores to identify patterns of recovery and recovery timelines in patients with moderate and severe myelopathy. Methods: Fifty-three myelopathy patients were enrolled and completed the mJOA questionnaire both pre-surgery, and six weeks and six months post-surgery. Pearson chi-square tests were performed to assess relationships of both recovery patterns and recovery timelines with severity of disease. Results: Moderate myelopathy patients were significantly more likely than severe myelopathy patients to experience full recovery of upper extremity, lower extremity, and sensory domains. Disease severity did not significantly impact the timeline during which recovery occurs. Overall, >90% of patients experienced at least partial recovery by six months post surgery, 80% of which demonstrated it within the first six weeks. Conclusions: This study shows the more severe the disease experienced by myelopathy patients, the more likely they will be left with permanent disabilities despite surgery. Early identification and treatment are therefore necessary to prevent worsening quality of life and increased costs of functional dependence. The recovery timelines for each subscore are similar and provide new values to guide patient expectations in their potential post-operative recovery. The overall recovery timeline is more generalizable though potentially lacking the specificity patients seek.

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.

Each year in Canada, a substantial number of adults undergo limb amputation, with lower limb amputation (LLA) the most prevalent. Enhancing walking ability is crucial for optimizing rehabilitation outcomes, promoting participation, and facilitating community reintegration. Overcoming challenges during the acute post-amputation phase and sub-acute rehabilitation necessitates alternative approaches, such as motor imagery and mental practice, to maximize rehabilitation success. However, the current evidence on activation patterns using motor imagery in individuals with LLA is limited. The primary objective was to assess the feasibility of observing brain activation during imagined walking in individuals with LLA utilizing 3T functional magnetic resonance imaging (fMRI). Eight individuals with LLA and 11 control subjects participated. Consistent with representations of the lower limbs, both control and amputee groups demonstrated bilateral activation in the medial surface of the primary motor and somatosensory cortices. However, individuals with lower limb amputations exhibited significantly greater activation during imagined walking, particularly in frontal regions and the medial surface of the primary motor and supplementary motor cortices. Furthermore, the volume of activation in the bilateral primary motor cortices was higher for participants with amputations compared to controls. The protocol developed in this study establishes a foundation for evaluating the effects of a gait training program that incorporates mental imagery alongside conventional rehabilitation practices, in contrast to standard care alone. This pilot investigation holds potential to enhance our understanding of brain plasticity in individuals with LLA and pave the way for more effective rehabilitation strategies to optimize functional recovery and community reintegration.

Many pathological conditions lead to altered intracellular pH (pHi) disrupting normal cellular functions. The chemical exchange saturation transfer (CEST) method, known as Amine and Amide Concentration Independent Detection (AACID), can produce image contrast that is predominantly dependent on tissue intracellular pHi. The AACID value is linearly related to the ratio of the 3.5 ppm amide CEST effect and the 2.75 ppm amine CEST effect in the physiological range. However, the amine CEST effect at 2 ppm is often more clearly defined in vivo, and may provide greater sensitivity to pH changes. The purpose of the current study was to compare AACID measurement precision utilizing the 2.0 and 2.75 ppm amine CEST effects. We hypothesized that the 2.0 ppm amine CEST resonance would produce measurements with greater sensitivity to pH changes. In the current study, we compare the range of the AACID values obtained in 24 mice with brain tumors and in normal tissue using the 2 ppm and 2.75 ppm amine resonances. All CEST data were acquired on a 9.4T MRI scanner. The AACID measurement range increased by 39% when using the 2 ppm amine resonance compared to the 2.75 ppm resonance, with decreased measurement variability across the brain. These data indicate that in vivo pH measurements made using AACID CEST can be enhanced by incorporating the 2 ppm amine resonance. This approach should be considered for pH measurements made over short intervals when no changes are expected in the concentration of metabolites that contribute to the 2 ppm amine resonance.

Background Cerebral small vessel disease is associated with higher ratios of soluble‐epoxide hydrolase derived linoleic acid diols (12,13‐dihydroxyoctadecenoic acid [DiHOME] and 9,10‐DiHOME) to their parent epoxides (12(13)‐epoxyoctadecenoic acid [EpOME] and 9(10)‐EpOME); however, the relationship has not yet been examined in stroke. Methods and Results Participants with mild to moderate small vessel stroke or large vessel stroke were selected based on clinical and imaging criteria. Metabolites were quantified by ultra‐high‐performance liquid chromatography–mass spectrometry. Volumes of stroke, lacunes, white matter hyperintensities, magnetic resonance imaging visible perivascular spaces, and free water diffusion were quantified from structural and diffusion magnetic resonance imaging (3 Tesla). Adjusted linear regression models were used for analysis. Compared with participants with large vessel stroke (n=30), participants with small vessel stroke (n=50) had a higher 12,13‐DiHOME/12(13)‐EpOME ratio (β=0.251, P=0.023). The 12,13‐DiHOME/12(13)‐EpOME ratio was associated with more lacunes (β=0.266, P=0.028) but not with large vessel stroke volumes. Ratios of 12,13‐DiHOME/12(13)‐EpOME and 9,10‐DiHOME/9(10)‐EpOME were associated with greater volumes of white matter hyperintensities (β=0.364, P

Acidification of cancerous tissue induced pharmacologically may slow tumor growth and can be detected using magnetic resonance imaging. Numerous studies have shown that pharmacologically inhibiting specific transporters, such as the Na+/H+ exchanger 1 (NHE1), can alter glycolitic metabolism and affect tumor acidosis. The sodium proton exchanger inhibitor Cariporide can acidify U87MG gliomas in mice. This study aimed to determine whether Cariporide could acidify C6 glioma tumors in rats with an intact immune system. C6 glioma cells were implanted in the right brain hemisphere of ten rats. Chemical exchange saturation transfer (CEST) MRI (9.4T) was acquired on days 7–8 and 14–15 after implantation to measure in vivo tissue intracellular pH (pHi) within the tumors and on the contralateral side. pHi was basic relative to contralateral tissue at both time points assessed using the amine and amide concentration-independent detection (AACID) value. On day 14–15, measurements were made before and up to 160 min after Cariporide injection (N = 6). Twenty minutes after drug injection, the average AACID value in the tumor significantly increased by ∼6.4% compared to pre-injection, corresponding to 0.31 ± 0.20 lower pHi, while in contralateral tissue, AACID value increased significantly by ∼4.3% compared to pre-injection, corresponding to 0.22 ± 0.19 lower pHi. Control rats without tumors showed no changes following injection of Cariporide dissolved in 10% or 1% DMSO and diluted in PBS. This study demonstrates the sensitivity of CEST-based pH-weighted imaging for monitoring the response of tumors to pharmacologically induced acidification.

Abstract We have previously reported long-term changes in the brains of non-concussed varsity rugby players using magnetic resonance spectroscopy (MRS), diffusion tensor imaging (DTI) and functional magnetic imaging (fMRI). Others have reported cognitive deficits in contact sport athletes that have not met the diagnostic criteria for concussion. These results suggest that repetitive mild traumatic brain injuries (rmTBIs) that are not severe enough to meet the diagnostic threshold for concussion, produce long-term consequences. We sought to characterize the neuroimaging, cognitive, pathological and metabolomic changes in a mouse model of rmTBI. Using a closed-skull model of mTBI that when scaled to human leads to rotational and linear accelerations far below what has been reported for sports concussion athletes, we found that 5 daily mTBIs triggered two temporally distinct types of pathological changes. First, during the first days and weeks after injury, the rmTBI produced diffuse axonal injury, a transient inflammatory response and changes in diffusion tensor imaging (DTI) that resolved with time. Second, the rmTBI led to pathological changes that were evident months after the injury including: changes in magnetic resonance spectroscopy (MRS), altered levels of synaptic proteins, behavioural deficits in attention and spatial memory, accumulations of pathologically phosphorylated tau, altered blood metabolomic profiles and white matter ultrastructural abnormalities. These results indicate that exceedingly mild rmTBI, in mice, triggers processes with pathological consequences observable months after the initial injury.

Alterations in tissue microstructure in normal-appearing white matter (NAWM), specifically measured by diffusion tensor imaging (DTI) fractional anisotropy (FA), have been associated with cognitive outcomes following stroke. The purpose of this study was to comprehensively compare conventional DTI measures of tissue microstructure in NAWM to diverse vascular brain lesions in people with cerebrovascular disease (CVD) and to examine associations between FA in NAWM and cerebrovascular risk factors. DTI metrics including fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) were measured in cerebral tissues and cerebrovascular anomalies from 152 people with CVD participating in the Ontario Neurodegenerative Disease Research Initiative (ONDRI). Ten cerebral tissue types were segmented including NAWM, and vascular lesions including stroke, periventricular and deep white matter hyperintensities, periventricular and deep lacunar infarcts, and perivascular spaces (PVS) using T1-weighted, proton density-weighted, T2-weighted, and fluid attenuated inversion recovery MRI scans. Mean DTI metrics were measured in each tissue region using a previously developed DTI processing pipeline and compared between tissues using multivariate analysis of covariance. Associations between FA in NAWM and several CVD risk factors were also examined. DTI metrics in vascular lesions differed significantly from healthy tissue. Specifically, all tissue types had significantly different MD values, while FA was also found to be different in most tissue types. FA in NAWM was inversely related to hypertension and modified Rankin scale (mRS). This study demonstrated the differences between conventional DTI metrics, FA, MD, AD, and RD, in cerebral vascular lesions and healthy tissue types. Therefore, incorporating DTI to characterize the integrity of the tissue microstructure could help to define the extent and severity of various brain vascular anomalies. The association between FA within NAWM and clinical evaluation of hypertension and disability provides further evidence that white matter microstructural integrity is impacted by cerebrovascular function.

Sport concussions can be difficult to diagnose and if missed, they can expose athletes to greater injury risk and long-lasting neurological disabilities. Discovery of objective biomarkers to aid concussion diagnosis is critical to protecting athlete brain health. To this end, we performed targeted proteomics on plasma obtained from adolescent athletes suffering a sports concussion. A total of 11 concussed male athletes were enrolled at our academic Sport Medicine Concussion Clinic, as well as 24 sex-, age- and activity-matched healthy control subjects. Clinical evaluation was performed and blood was drawn within 72 h of injury. Proximity extension assays were performed for 1,472 plasma proteins; a total of six proteins were considered significantly different between cohorts (P < 0.01; five proteins decreased and one protein increased). Receiver operating characteristic curves on the six individual protein biomarkers identified had areas-under-the-curves (AUCs) for concussion diagnosis ≥0.78; antioxidant 1 copper chaperone (ATOX1; AUC 0.81, P = 0.003), secreted protein acidic and rich in cysteine (SPARC; AUC 0.81, P = 0.004), cluster of differentiation 34 (CD34; AUC 0.79, P = 0.006), polyglutamine binding protein 1 (PQBP1; AUC 0.78, P = 0.008), insulin-like growth factor-binding protein-like 1 (IGFBPL1; AUC 0.78, P = 0.008) and cytosolic 5'-nucleotidase 3A (NT5C3A; AUC 0.78, P = 0.009). Combining three of the protein biomarkers (ATOX1, SPARC and NT5C3A), produced an AUC of 0.98 for concussion diagnoses (P < 0.001; 95% CI: 0.95, 1.00). Despite a paucity of studies on these three identified proteins, the available evidence points to their roles in modulating tissue inflammation and regulating integrity of the cerebral microvasculature. Taken together, our exploratory data suggest that three or less novel proteins, which are amenable to a point-of-care immunoassay, may be future candidate biomarkers for screening adolescent sport concussion. Validation with protein assays is required in larger cohorts.

Despite the wide application of the magnetic resonance imaging (MRI) technique, there are no widely used standards on naming and describing MRI sequences. The absence of consistent naming conventions presents a major challenge in automating image processing since most MRI software require a priori knowledge of the type of the MRI sequences to be processed. This issue becomes increasingly critical with the current efforts toward open-sharing of MRI data in the neuroscience community. This manuscript reports an MRI sequence detection method using imaging metadata and a supervised machine learning technique. Three datasets from the Brain Center for Ontario Data Exploration (Brain-CODE) data platform, each involving MRI data from multiple research institutes, are used to build and test our model. The preliminary results show that a random forest model can be trained to accurately identify MRI sequence types, and to recognize MRI scans that do not belong to any of the known sequence types. Therefore the proposed approach can be used to automate processing of MRI data that involves a large number of variations in sequence names, and to help standardize sequence naming in ongoing data collections. This study highlights the potential of the machine learning approaches in helping manage health data.

Quality assurance (QA) is crucial in longitudinal and/or multi-site studies, which involve the collection of data from a group of subjects over time and/or at different locations. It is important to regularly monitor the performance of the scanners over time and at different locations to detect and control for intrinsic differences (e.g., due to manufacturers) and changes in scanner performance (e.g., due to gradual component aging, software and/or hardware upgrades, etc.). As part of the Ontario Neurodegenerative Disease Research Initiative (ONDRI) and the Canadian Biomarker Integration Network in Depression (CAN-BIND), QA phantom scans were conducted approximately monthly for three to four years at 13 sites across Canada with 3T research MRI scanners. QA parameters were calculated for each scan using the functional Biomarker Imaging Research Network's (fBIRN) QA phantom and pipeline to capture between- and within-scanner variability. We also describe a QA protocol to measure the full-width-at-half-maximum (FWHM) of slice-wise point spread functions (PSF), used in conjunction with the fBIRN QA parameters. Variations in image resolution measured by the FWHM are a primary source of variance over time for many sites, as well as between sites and between manufacturers. We also identify an unexpected range of instabilities affecting individual slices in a number of scanners, which may amount to a substantial contribution of unexplained signal variance to their data. Finally, we identify a preliminary preprocessing approach to reduce this variance and/or alleviate the slice anomalies, and in a small human data set show that this change in preprocessing can have a significant impact on seed-based connectivity measurements for some individual subjects. We expect that other fMRI centres will find this approach to identifying and controlling scanner instabilities useful in similar studies.

Abstract Although it has been suggested that the co-expression of multiple pathological proteins associated with neurodegeneration may act synergistically to induce more widespread neuropathology, experimental evidence of this is sparse. We have previously shown that the expression of Thr175Asp-tau (tauT175D) using somatic gene transfer with a stereotaxically-injected recombinant adeno-associated virus (rAAV9) vector induces tau pathology in rat hippocampus. In this study, we have examined whether the co-expression of human tauT175D with mutant human TDP-43 (TDP-43M337V) will act synergistically. Transgenic female Sprague-Dawley rats that inducibly express mutant human TDP-43M337V using the choline acetyltransferase (ChAT) tetracycline response element (TRE) driver with activity modulating tetracycline-controlled transactivator (tTA) were utilized in these studies. Adult rats were injected with GFP-tagged tau protein constructs in a rAAV9 vector through bilateral stereotaxic injection into the hippocampus. Injected tau constructs were: wild-type GFP-tagged 2N4R human tau (tauWT; n = 8), GFP-tagged tauT175D 2N4R human tau (tauT175D, pseudophosphorylated, toxic variant, n = 8), and GFP (control, n = 8). Six months post-injection, mutant TDP-43M337V expression was induced for 30 days. Behaviour testing identified motor deficits within 3 weeks after TDP-43 expression irrespective of tau expression, though social behaviour and sensorimotor gating remained unchanged. Increased tau pathology was observed in the hippocampus of both tauWT and tauT175D expressing rats and tauT175D pathology was increased in the presence of cholinergic neuronal expression of human TDP-43M337V. These data indicate that co-expression of pathological TDP-43 and tau protein exacerbate the pathology associated with either individual protein.

With improvements to both scan quality and facial recognition software, there is an increased risk of participants being identified by a 3D render of their structural neuroimaging scans, even when all other personal information has been removed. To prevent this, facial features should be removed before data are shared or openly released, but while there are several publicly available software algorithms to do this, there has been no comprehensive review of their accuracy within the general population. To address this, we tested multiple algorithms on 300 scans from three neuroscience research projects, funded in part by the Ontario Brain Institute, to cover a wide range of ages (3–85 years) and multiple patient cohorts. While skull stripping is more thorough at removing identifiable features, we focused mainly on defacing software, as skull stripping also removes potentially useful information, which may be required for future analyses. We tested six publicly available algorithms (afni_refacer, deepdefacer, mri_deface, mridefacer, pydeface, quickshear), with one skull stripper (FreeSurfer) included for comparison. Accuracy was measured through a pass/fail system with two criteria; one, that all facial features had been removed and two, that no brain tissue was removed in the process. A subset of defaced scans were also run through several preprocessing pipelines to ensure that none of the algorithms would alter the resulting outputs. We found that the success rates varied strongly between defacers, with afni_refacer (89%) and pydeface (83%) having the highest rates, overall. In both cases, the primary source of failure came from a single dataset that the defacer appeared to struggle with - the youngest cohort (3–20 years) for afni_refacer and the oldest (44–85 years) for pydeface, demonstrating that defacer performance not only depends on the data provided, but that this effect varies between algorithms. While there were some very minor differences between the preprocessing results for defaced and original scans, none of these were significant and were within the range of variation between using different NIfTI converters, or using raw DICOM files.

BackgroundRegional changes to cortical thickness in individuals with neurodegenerative and cerebrovascular diseases (CVD) can be estimated using specialized neuroimaging software. However, the presence of cerebral small vessel disease, focal atrophy, and cortico-subcortical stroke lesions, pose significant challenges that increase the likelihood of misclassification errors and segmentation failures.PurposeThe main goal of this study was to examine a correction procedure developed for enhancing FreeSurfer’s (FS’s) cortical thickness estimation tool, particularly when applied to the most challenging MRI obtained from participants with chronic stroke and CVD, with varying degrees of neurovascular lesions and brain atrophy.MethodsIn 155 CVD participants enrolled in the Ontario Neurodegenerative Disease Research Initiative (ONDRI), FS outputs were compared between a fully automated, unmodified procedure and a corrected procedure that accounted for potential sources of error due to atrophy and neurovascular lesions. Quality control (QC) measures were obtained from both procedures. Association between cortical thickness and global cognitive status as assessed by the Montreal Cognitive Assessment (MoCA) score was also investigated from both procedures.ResultsCorrected procedures increased “Acceptable” QC ratings from 18 to 76% for the cortical ribbon and from 38 to 92% for tissue segmentation. Corrected procedures reduced “Fail” ratings from 11 to 0% for the cortical ribbon and 62 to 8% for tissue segmentation. FS-based segmentation of T1-weighted white matter hypointensities were significantly greater in the corrected procedure (5.8 mL vs. 15.9 mL, p < 0.001). The unmodified procedure yielded no significant associations with global cognitive status, whereas the corrected procedure yielded positive associations between MoCA total score and clusters of cortical thickness in the left superior parietal (p = 0.018) and left insula (p = 0.04) regions. Further analyses with the corrected cortical thickness results and MoCA subscores showed a positive association between left superior parietal cortical thickness and Attention (p < 0.001).ConclusionThese findings suggest that correction procedures which account for brain atrophy and neurovascular lesions can significantly improve FS’s segmentation results and reduce failure rates, thus maximizing power by preventing the loss of our important study participants. Future work will examine relationships between cortical thickness, cerebral small vessel disease, and cognitive dysfunction due to neurodegenerative disease in the ONDRI study.

White matter tracts are known to be susceptible to injury following concussion. The objective of this study was to determine whether contact play in sport could alter white matter metabolite levels in female varsity athletes independent of changes induced by long-term exercise. Metabolite levels were measured by single voxel proton magnetic resonance spectroscopy (MRS) in the prefrontal white matter at the beginning (In-Season) and end (Off-Season) of season in contact (N = 54, rugby players) and non-contact (N = 23, swimmers and rowers) varsity athletes. Sedentary women (N = 23) were scanned once, at a time equivalent to the Off-Season time point. Metabolite levels in non-contact athletes did not change over a season of play, or differ from age matched sedentary women except that non-contact athletes had a slightly lower myo-inositol level. The contact athletes had lower levels of myo-inositol and glutamate, and higher levels of glutamine compared to both sedentary women and non-contact athletes. Lower levels of myo-inositol in non-contact athletes compared to sedentary women indicates long-term exercise may alter glial cell profiles in these athletes. The metabolite differences observed between contact and non-contact athletes suggest that non-contact athletes should not be used as controls in studies of concussion in high-impact sports because repetitive impacts from physical contact can alter white matter metabolite level profiles. It is imperative to use athletes engaged in the same contact sport as controls to ensure a matched metabolite profile at baseline.

Acetylcholine (ACh) has been suggested to facilitate plasticity and improve functional recovery after different types of brain lesions. Interestingly, numerous studies have shown that striatal cholinergic interneurons are relatively resistant to acute ischemic insults, but whether ACh released by these neurons enhances functional recovery after stroke is unknown. We investigated the role of endogenous striatal ACh in stroke lesion volume and functional outcomes following middle cerebral artery occlusion to induce focal ischemia in striatum-selective vesicular acetylcholine transporter-deficient mice (stVAChT-KO). As transporter expression is almost completely eliminated in the striatum of stVAChT-KO mice, ACh release is nearly abolished in this area. Conversely, in other brain areas, VAChT expression and ACh release are preserved. Our results demonstrate a larger infarct size after ischemic insult in stVAChT-KO mice, with more pronounced functional impairments and increased mortality than in littermate controls. These changes are associated with increased activation of GSK-3, decreased levels of β-catenin, and a higher permeability of the blood–brain barrier in mice with loss of VAChT in striatum neurons. These results support a framework in which endogenous ACh secretion originating from cholinergic interneurons in the striatum helps to protect brain tissue against ischemia-induced damage and facilitates brain recovery by supporting blood–brain barrier function.

The Ontario Neurodegenerative Research Initiative (ONDRI) is a 3 years multi-site prospective cohort study that has acquired comprehensive multiple assessment platform data, including 3T structural MRI, from neurodegenerative patients with Alzheimer's disease, mild cognitive impairment, Parkinson's disease, amyotrophic lateral sclerosis, frontotemporal dementia, and cerebrovascular disease. This heterogeneous cross-section of patients with complex neurodegenerative and neurovascular pathologies pose significant challenges for standard neuroimaging tools. To effectively quantify regional measures of normal and pathological brain tissue volumes, the ONDRI neuroimaging platform implemented a semi-automated MRI processing pipeline that was able to address many of the challenges resulting from this heterogeneity. The purpose of this paper is to serve as a reference and conceptual overview of the comprehensive neuroimaging pipeline used to generate regional brain tissue volumes and neurovascular marker data that will be made publicly available online.

Open Science has changed research by making data accessible and shareable, contributing to replicability to accelerate and disseminate knowledge. However, for rodent cognitive studies the availability of tools to share and disseminate data is scarce. Automated touchscreen-based tests enable systematic cognitive assessment with easily standardised outputs that can facilitate data dissemination. Here we present an integration of touchscreen cognitive testing with an open-access database public repository (mousebytes.ca), as well as a Web platform for knowledge dissemination (https://touchscreencognition.org). We complement these resources with the largest dataset of age-dependent high-level cognitive assessment of mouse models of Alzheimer’s disease, expanding knowledge of affected cognitive domains from male and female mice of three strains. We envision that these new platforms will enhance sharing of protocols, data availability and transparency, allowing meta-analysis and reuse of mouse cognitive data to increase the replicability/reproducibility of datasets.

The processing of brain diffusion tensor imaging (DTI) data for large cohort studies requires fully automatic pipelines to perform quality control (QC) and artifact/outlier removal procedures on the raw DTI data prior to calculation of diffusion parameters. In this study, three automatic DTI processing pipelines, each complying with the general ENIGMA framework, were designed by uniquely combining multiple image processing software tools. Different QC procedures based on the RESTORE algorithm, the DTIPrep protocol, and a combination of both methods were compared using simulated ground truth and artifact containing DTI datasets modeling eddy current induced distortions, various levels of motion artifacts, and thermal noise. Variability was also examined in 20 DTI datasets acquired in subjects with vascular cognitive impairment (VCI) from the multi-site Ontario Neurodegenerative Disease Research Initiative (ONDRI). The mean fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) were calculated in global brain grey matter (GM) and white matter (WM) regions. For the simulated DTI datasets, the measure used to evaluate the performance of the pipelines was the normalized difference between the mean DTI metrics measured in GM and WM regions and the corresponding ground truth DTI value. The performance of the proposed pipelines was very similar, particularly in FA measurements. However, the pipeline based on the RESTORE algorithm was the most accurate when analyzing the artifact containing DTI datasets. The pipeline that combined the DTIPrep protocol and the RESTORE algorithm produced the lowest standard deviation in FA measurements in normal appearing WM across subjects. We concluded that this pipeline was the most robust and is preferred for automated analysis of multisite brain DTI data.

PurposeNeurite Orientation Dispersion and Density Imaging (NODDI) is a diffusion MRI (dMRI) technique used to characterize tissue microstructure by compartmental modelling of neural water fractions. Intra-neurite, extra-neurite, and cerebral spinal fluid volume fractions are measured. The purpose of this study was to determine the reproducibility of NODDI in the rat brain at 9.4 Tesla.MethodsEight data sets were successfully acquired on adult male Sprague Dawley rats. Each rat was scanned twice on a 9.4T Agilent MRI with a 7 ± 1 day separation between scans. A multi-shell diffusion protocol was implemented consisting of 108 total directions varied over two shells (b-values of 1000 s/mm2 and 2000 s/mm2). Three techniques were used to analyze the NODDI scalar maps: mean region of interest (ROI) analysis, whole brain voxel-wise analysis, and targeted ROI analyses (voxel-wise within a given ROI). The coefficient of variation (CV) was used to assess the reproducibility of NODDI and provide insight into necessary sample sizes and minimum detectable effect size.ResultsCV maps for orientation dispersion index (ODI) and neurite density index (NDI) showed high reproducibility both between and within subjects. Furthermore, it was found that small biological changes ( 50) for biological changes to be detected.ConclusionsThe ODI and NDI measured by NODDI in the rat brain at 9.4T are highly reproducible and may be sensitive to subtle changes in tissue microstructure.

Background: Mild Cognitive Impairment (MCI) is an intermediate state between normal cognition and dementia that is associated with twice the risk of falls. It is unknown whether white matter integrity (WMI) is associated with increased risk of falls in MCI. The purpose of this study was to evaluate if early changes in WMI were associated with gait impairment and falls. Methods: Forty-three participants with MCI from the Gait and Brain Study underwent standardized assessment of cognition, gait performance under single and dual-task conditions (walking while talking), and WMI using 3 Tesla diffusion tensor imaging (DTI). Macro-structural imaging characteristics (white and grey matter morphology) as well as microstructural WMI parameters were examined for associations with falls and gait performance. Significantly associated WM tracts were then used to test the interplay between WMI and history of falls, after adjusting for other important covariates. Results: Multiple WM tracts (corpus callosum, forceps minor, and the left inferior fronto-occipital fasciculus) were significantly associated with history of falls and lower dual-task gait performance. A multivariable regression model showed that fall history was associated with the radial diffusivity in the forceps minor, even after adjusting for education, sex, BMI, MMSE scores, comorbidities, gait velocity and WMH volume as covariates. Conclusions: Multiple WM tracts that are known to be involved in executive and visuospatial functions were preferentially affected in MCI individuals with history of falls. Our preliminary findings support the notion that WMI in key brain regions may increase risk of falls in older adults with MCI. Keywords: Diffusion tensor imaging, Falls, Gait, Executive function, White matter, Microstructure, MCI

The harmonized Canadian Dementia Imaging Protocol (CDIP) has been developed to suit the needs of a number of co-occurring Canadian studies collecting data on brain changes across adulthood and neurodegeneration. In this study, we verify the impact of CDIP parameters compliance on total brain volume variance using 86 scans of the same individual acquired on various scanners. Data included planned data collection acquired within the Consortium pour l'identification précoce de la maladie Alzheimer - Québec (CIMA-Q) and Canadian Consortium on Neurodegeneration in Aging (CCNA) studies, as well as opportunistic data collection from various protocols. For images acquired from Philips scanners, lower variance in brain volumes were observed when the stated CDIP resolution was set. For images acquired from GE scanners, lower variance in brain volumes were noticed when TE/TR values were within 5% of the CDIP protocol, compared to values farther from that criteria. Together, these results suggest that a harmonized protocol like the CDIP may help to reduce neuromorphometric measurement variability in multi-centric studies. Keywords: Neuroimaging, Magnetic resonance imaging, Standardization, Morphometry, Multi-centric studies

Acute brain changes are expected after concussion, yet there is growing evidence of persistent abnormalities well beyond clinical recovery and clearance to return to play. Multiparametric MRI is a powerful approach to non-invasively study structure-function relationships in the brain, however it remains challenging to interpret the complex and heterogeneous cascade of brain changes that manifest after concussion. Emerging conjunctive, data-driven analysis approaches like linked independent component analysis can integrate structural and functional imaging data to produce linked components that describe the shared inter-subject variance across images. These linked components not only offer the potential of a more comprehensive understanding of the underlying neurobiology of concussion, but can also provide reliable information at the level of an individual athlete. In this study, we analyzed resting-state functional MRI (rs-fMRI) and diffusion tensor imaging (DTI) within a cohort of female varsity rugby players (n = 52) through the in- and off-season, including concussed athletes (n = 21) who were studied longitudinally at three days, three months and six months after a diagnosed concussion. Linked components representing co-varying white matter microstructure and functional network connectivity characterized (a) the brain's acute response to concussion and (b) persistent alterations beyond clinical recovery. Furthermore, we demonstrate that these long-term brain changes related to specific aspects of a concussion history and allowed us to monitor individual athletes before and longitudinally after a diagnosed concussion. Keywords: Functional MRI, Resting state connectivity, Diffusion weighted imaging, Concussion, Linked independent component analysis, Mild traumatic brain injury

Transcranial direct current stimulation (tDCS) is a form of non-invasive brain stimulation that may modulate cortical excitability, metabolite concentration, and human behaviour. The supplementary motor area (SMA) has been largely ignored as a potential target for tDCS neurorehabilitation but is an important region in motor compensation after brain injury with strong efferent connections to the primary motor cortex (M1). The objective of this work was to measure tissue metabolite changes in the human motor cortex immediately following tDCS. We hypothesized that bihemispheric tDCS would change levels of metabolites involved in neuromodulation including N-acetylaspartate (NAA), glutamate (Glu), and creatine (tCr). In this single-blind, randomized, cross-over study, fifteen healthy adults aged 21-60 participated in two 7T MRI sessions, to identify changes in metabolite concentrations by magnetic resonance spectroscopy. Immediately after 20 minutes of tDCS, there were no significant changes in metabolite levels or metabolite ratios comparing tDCS to sham. However there was a trend toward increased NAA/tCr concentration (p = 0.08) in M1 under the stimulating cathode. There was a strong, positive correlation between the change in the absolute concentration of NAA and the change in the absolute concentration of tCr (p

Previous studies have demonstrated altered brain activity in Alzheimer's disease using task based functional MRI (fMRI), network based resting-state fMRI, and glucose metabolism from 18F fluorodeoxyglucose-PET (FDG-PET). Our goal was to define a novel indicator of neuronal activity based on a first-order textural feature of the resting state functional MRI (RS-fMRI) signal. Furthermore, we examined the association between this neuronal activity metric and glucose metabolism from 18F FDG-PET. We studied 15 normal elderly controls (NEC) and 15 probable Alzheimer disease (AD) subjects from the AD Neuroimaging Initiative. An independent component analysis was applied to the RS-fMRI, followed by template matching to identify neuronal components (NC). A regional brain activity measurement was constructed based on the variation of the RS-fMRI signal of these NC. The standardized glucose uptake values of several brain regions relative to the cerebellum (SUVR) were measured from partial volume corrected FDG-PET images. Comparing the AD and NEC groups, the mean brain activity metric was significantly lower in the accumbens, while the glucose SUVR was significantly lower in the amygdala and hippocampus. The RS-fMRI brain activity metric was positively correlated with cognitive measures and amyloid β1-42 cerebral spinal fluid levels; however, these did not remain significant following Bonferroni correction. There was a significant linear correlation between the brain activity metric and the glucose SUVR measurements. This proof of concept study demonstrates that this novel and easy to implement RS-fMRI brain activity metric can differentiate a group of healthy elderly controls from a group of people with AD.

The clinical utility of the Mini-Mental State Examination (MMSE) and its shorter version (SMMSE) is still debated. There is a need to better understand the neuroanatomical correlates of these cognitive tests. The objective of this cross-sectional study was to determine whether lower MMSE and SMMSE scores correlated with focal brain volume reduction in older adults. Participants from the GAIT study (n = 207; mean, 70.9±5.9 years; 57% female; mean MMSE 26.2±3.9; mean SMMSE 5.1±1.1) were evaluated using the MMSE and SMMSE and received a 1.5-Tesla MRI scan of the brain. Cortical gray and white matter subvolumes were automatically segmented using Statistical Parametric Mapping. Age, gender, education level, and total intracranial volume were included as potential confounders. We found correlations between the MMSE score and specific cortical regions of the limbic system including the hippocampus, amygdala, cingulate gyrus, and parahippocampal gyrus, independently of the diagnostic category (i.e., mild cognitive impairment or Alzheimer disease or controls). Regarding correlations with the SMMSE score, only one cluster in the left hippocampus was identified, which overlapped with the cluster that was positively correlated with the MMSE score. There were no correlations with the volume of white matter. In conclusion, worse MMSE and SMMSE scores were associated with gray matter atrophy mainly in the limbic system. This finding highlights that atrophy of specific brain regions are related to performance on the MMSE and the SMMSE tests, and provides new insights into the cognitive function probed by these tests.