Gilbert Bruce Pike

Understanding how the human brain matures from infancy through adolescence is essential for identifying factors that influence health outcomes. Magnetic resonance imaging (MRI) provides powerful tools for characterizing structural, microstructural, functional, and metabolic aspects of brain development; however, many pediatric imaging studies are constrained by small sample sizes, limited geographic diversity, lack of longitudinal measurement, restricted age ranges, and heterogeneous acquisition protocols. While recent large-scale neuroimaging consortia have begun to address these challenges, such resources remain limited in the Canadian context, creating a critical gap. The Canadian Pediatric Imaging Platform (C-PIP) has developed a multisite neuroimaging framework across three of Canada’s largest pediatric hospitals: Alberta Children’s Hospital (Calgary), Centre Hospitalier Universitaire Sainte-Justine (Montréal), and the Hospital for Sick Children (Toronto). These three sites were united through a national initiative, leveraging high-performance MRI systems to accelerate Canadian neurodevelopmental research. In addition, C-PIP has implemented a centralized data sharing infrastructure to facilitate open science and collaborative research across institutions. Here, we describe the development and implementation of a multimodal MRI acquisition protocol designed to support multimodal longitudinal studies of typical and atypical brain development spanning infancy through adolescence. Within-site stability and cross-site stability of C-PIP core MRI sequences were evaluated using a travelling-participant study. The C-PIP MRI protocol establishes a harmonized, multisite imaging framework that supports large-scale investigations of pediatric brain development and disorders. Highlights: The Canadian Pediatric Imaging Platform (C-PIP) is a national initiative acquiring longitudinal phenotyping and neuroimaging data from 820 children aged 0-18 years across three Canadian provinces (Alberta, Ontario and Québec). C-PIP has developed a harmonized, developmentally sensitive neuroimaging protocol to collect high-quality, comprehensive imaging data across multiple modalities. This paper outlines the development, considerations and implementation of the C-PIP neuroimaging protocol across sites. Preliminary cross-site validation analyses demonstrate good within- and between-site stability in neuroimaging measures. Graphical

Accurate measurement of physiological signals such as respiration and cardiac activity is essential for modeling physiological confounds in BOLD-fMRI data. However, external monitoring devices such as respiratory belts and photoplethysmographs often suffer from signal loss, noise, or incomplete recordings, and many fMRI datasets lack physiological measurements altogether due to practical constraints. In this work, we propose a data-driven framework to reconstruct respiratory variation (RV) and heart rate variation (HRV) directly from resting-state fMRI data using a hybrid machine learning architecture that combines one-dimensional convolutional neural networks (1D-CNN) with gated recurrent units (GRUs). The model processes BOLD signals from 630 regions of interest (ROIs), spanning cortical and subcortical gray matter, white matter, and cerebrospinal fluid. For RV estimation, six head motion parameters were included as additional inputs to capture motion-related physiological information. The proposed method was trained and evaluated on three cohorts across the lifespan from the Human Connectome Project (HCP), including HCP in Development (HCP-D, ages 5–21 years), HCP in Young Adults (HCP-YA, ages 22–35 years), and HCP in Aging (HCP-A, ages 36–100 years). Age-specific architectural tuning was applied to accommodate developmental, neurovascular, and physiological variability across these populations. When compared against established CNN- and RNN-based baselines under matched preprocessing and native temporal resolution, the proposed architecture achieved consistent performance gains across all evaluation metrics. The largest relative improvements were observed for error-based and temporal alignment metrics, with MAE, MSE, and DTW reduced by approximately 7–10%, indicating lower absolute reconstruction error and improved temporal fidelity, while correlation-based improvements were more modest (approximately 5–7%). These results demonstrate the feasibility and effectiveness of reconstructing RV and HRV directly from fMRI time series using spatially and temporally enriched neural architectures. Building on earlier efforts in this domain, the proposed framework extends prior work through evaluation across multiple cohorts, broader anatomical coverage, and enhanced modeling capacity, offering a robust alternative for physiological modeling in the absence of external recordings.

Objective . We present and validate an integrated workflow for accurate and stable targeting of a TUS transducer inside the MRI scanner using subject-specific imaging, neuronavigation, acoustic-thermal modeling, transducer positioning and fixation, and in-scanner verification. Approach . A stimulation target was defined in the left primary motor cortex (M1) hand knob using structural imaging and task-based functional magnetic resonance imaging (fMRI) collected during a planning session, then transferred to neuronavigation for trajectory planning and scalp marking. Acoustic and thermal simulations characterized the acoustic beam geometry and adjusted ultrasound power to achieve the desired acoustic field at the target within safety constraints. During the experiment scanning session, low-resolution T ₁ -weighted localizer images visualized transducer fiducials and the planned target directly on the scanner console, enabling iterative in-bore refinement. Main results . Coupled with a custom MRI-compatible transducer fixation cap enabling controlled multi-axis adjustments, this workflow achieved a mean post-refinement off-target distance of 1.4 ± 1.0 mm across five participants over 19 sessions, falling within one fMRI voxel (2.5 mm) of the planned target in 17 of 19 sessions (89%). Repeat transducer position verification approximately 40 min after initial setup showed minimal positional drift (0.2 mm). In an expanded cohort of 12 participants with balanced sex distribution (six males, six females), mean post-adjustment off-target distance was 2.18 ± 0.78 mm in males and 1.93 ± 0.69 mm in females, with comparable performance between sexes (Mann-Whitney U = 22; descriptive), and an overall success rate of nine of 12 sessions (75%). fMRI tSNR in M1 was preserved with the passive transducer (left-to-right M1 ratio 0.90 ± 0.12) and during active sonication (0.90 ± 0.09) across the same 12 participants, indicating that the 20 mm coupling gel pad effectively mitigated susceptibility-related signal dropout and that active sonication introduced no additional signal degradation. Significance . This workflow provides a practical experimental foundation for in-scanner targeting and verification for simultaneous human TUS-fMRI studies, with MRI signal quality preserved during active sonication and consistent targeting performance across male and female participants.

Background: Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition with increasing prevalence and no approved therapies targeting its core social and behavioral deficits. Transcranial ultrasound stimulation (TUS) has emerged as a promising noninvasive, spatially precise, and deep-penetrating neuromodulatory approach for various neurological and neuropsychiatric disorders, yet its potential in ASD remains largely unexplored. Methods: This study investigated whether TUS could modulate ASD-related phenotypes in the Black and Tan BRachyury (BTBR) mouse model of autism (BTBR: n = 33; B6: n = 23). TUS was administered to the medial prefrontal cortex for five consecutive days, followed by behavioral assessments. Findings: TUS successfully reduced repetitive behaviors (p < 0·001) and improved measures of sociability, specifically: a reversal of abnormal non-social preference (pre-stim p < 0·05; post-stim p > 0·05); communication with increased vocalization frequency jumps (p < 0·05), step down calls (p < 0·05) and decreased flat calls (p < 0·05). These behavioral effects were accompanied by increased cFOS-positive cell density in the targeted brain region, indicating region-specific neuronal activation, with no detectable histological damage. Interpretation: These findings provide the first preclinical evidence that TUS can beneficially modulate ASD-like behavioral phenotypes in a validated mouse model through targeted engagement of a disease-relevant neural circuit. This establishes a framework for future mechanistic and translational investigations, highlighting the therapeutic potential of TUS for ASD.

Objective. Transcranial ultrasound stimulation (TUS) is a noninvasive neuromodulation technique offering millimeter-scale precision and deep targeting. However, the trade-off between fundamental frequency, focal size, and efficacy in humans remains unclear. This study aimed to compare the effects of broad versus narrow acoustic focus on corticospinal excitability using 250 kHz and 825 kHz TUS, and to evaluate whether multi-focal stimulation mitigates limitations of narrow beams. Approach. Twenty healthy adults underwent four randomized, double-blind sessions: 250 kHz unifocal, 825 kHz unifocal, 825 kHz multi-focal, and sham. TUS was delivered to the left primary motor cortex using a 128-element phased-array transducer integrated with neuronavigation. Corticospinal excitability was assessed using motor-evoked potentials (MEPs) elicited by transcranial magnetic stimulation at baseline and 5-, 30-, and 60 min post-stimulation. Reaction (RT) time and accuracy were measured using a Go/No-Go task. Main results. Linear mixed-effects modeling revealed a significant inhibition of corticospinal excitability, characterized by decreased MEP amplitudes, for both the 250 kHz unifocal and 825 kHz multi-focal conditions compared to sham (both with p = 0.018). The 825 kHz unifocal stimulation demonstrated only a non-significant trend toward inhibition ( p = 0.071). Effects were transient, most evident at 5- and 30 min post-TUS. Behavioral performance (measured via RT and task accuracy) remained unchanged across all conditions Significance. Findings suggest that spatial coverage, achieved through broader 250 kHz beams or multi-focal cycling, is critical for effective TUS-induced neuromodulation of the motor cortex. Future studies should optimize dosing and targeting strategies to balance precision and efficacy.

1 Background: Autosomal recessive spastic ataxia of Charlevoix-Saguenay (ARSACS) is a genetic disease characterized by spasticity and ataxia which reflects involvement of the corticospinal tracts (CST) and cerebellum. The primary involvement of the middle cerebellar peduncles (MCP) and transverse pontine fibers (TPF) at the crossing with the CST, and their role in the pathophysiology of the disease, is currently debated. Objectives: Advanced MRI techniques capable of isolating sub-voxel microstructural parameters can test the hypothesis that the MCP and TPF are abnormally large, compressing the CST at their crossing, and potentially impairing CST development. Methods: Tract macro- and micro-structural properties, including axon and tract caliber, axon density and geometry, and myelin content were estimated from diffusion-relaxometry and magnetization transfer imaging. These features were analyzed along segments of the CST, MCP, and TPF of 9 patients and 9 age-matched controls. Results: While the CST showed significant decreases in tract size, axon caliber, and myelination throughout its length compared to controls (p<0.01), the MCP and TPF were relatively unaffected. In our group, neither the MCP nor the pons were enlarged. The proximal MCP showed an increase in axon caliber. Conclusions: The increase in fractional anisotropy and axon density towards the center of the TPF could be driven by geometric confounds related to differences in the relative sizes of the CST and TPF compared to controls. This highlights the importance of investigating tract-specific microstructural profiles, particularly in regions of geometric complexity. The findings confirm the involvement of the CST, with a relatively limited involvement of the MCP and TPF. Highlights: Tract-based decrease in axon density in ARSACS patients estimated using diffusion-relaxometry Corticospinal tracts are significantly more affected than transverse pontine fibers Higher intra-axonal T2 (increase in axon caliber) in patient MCP not seen in TPF Tractometry helps resolve geometrical confounds that can cloud global tract measures Graphical

Incoherent k-space undersampling and deep learning-based reconstruction methods have shown great success in accelerating MRI. However, the performance of most previous methods will degrade dramatically under high acceleration factors, e.g., 8× or higher. Recently, denoising diffusion models (DM) have demonstrated promising results in solving this issue; however, one major drawback of the DM methods is the long inference time due to a dramatic number of iterative reverse posterior sampling steps. In this work, a Single Step Diffusion Model-based reconstruction framework, namely SSDM-MRI, is proposed for restoring MRI images from highly undersampled k-space. The proposed method achieves one-step reconstruction by first training a conditional DM and then iteratively distilling this model four times using an iterative selective distillation algorithm, which works synergistically with a shortcut reverse sampling strategy for model inference. Comprehensive experiments were carried out on both publicly available fastMRI brain and knee images, as well as an in-house multi-echo GRE (QSM) subject. Overall, the results showed that SSDM-MRI outperformed other methods in terms of numerical metrics (e.g., PSNR and SSIM), error maps, image fine details, and latent susceptibility information hidden in MRI phase images. In addition, the reconstruction time for a 320×320 brain slice of SSDM-MRI is only 0.45 second, which is only comparable to that of a simple U-net, making it a highly effective solution for MRI reconstruction tasks.

Background/Objective : Walking reflects a complex interaction between physiological systems that deteriorate with age and do so more rapidly in those living with diseases. We conducted a sex-specific cross-sectional analysis to determine the contribution of modifiable (risk) factors to single- and dual-task gait performance in older adults at risk of Alzheimer disease and related dementias. Methods : We included participants ( n = 103; 60.4% female; mean age 63.7 ± 6.2) who had completed a preintervention assessment for a randomized controlled trial. We used the following factors in hierarchical regressions while controlling for age, muscle strength, cardiorespiratory fitness, and global cognition. Participants walked at their usual pace on an electronic walkway and completed the two-back version of the n -back test either separately or together. Results : Males and females demonstrated reduced gait speed when comparing single to dual tasking ( p < .001), but only females demonstrated increasing (i.e., worse) gait variability ( p = .007). In females, the dual-task velocity model was significant, F (3, 56) = 5.173, with muscle strength ( p = .006) and cardiorespiratory fitness ( p = .049) contributing significantly. For males, the models were significant across all conditions, but muscle strength was the only significant (modifiable) factor (single task, p = .023; dual task, p = .046). Conclusion : Dual-task gait velocity is associated with a combination of modifiable factors. However, the importance of each factor differs between males and females. Significance/Implications : Such findings have implications for understanding gait decline and potential sex-specific intervention strategies in those at risk of Alzheimer disease and related dementias.