Biology subjects

Yendiki, A.

Publications and source records attributed to Yendiki, A..

At least 19 recordsLinked to original sources

bioRxiv2026

Bridging the microstructural gap in human connectomics using hierarchical phase-contrast tomography as a reference for diffusion MRI in the human brain

Diffusion MRI (dMRI) allows us to image the human connectome non-invasively, yet it provides indirect estimates of axonal orientations based on the diffusion of water molecules in millimeter-scale voxels, hence struggling to resolve complex micrometer-scale fiber geometries. Invasive methods for imaging axonal orientations ex vivo, e.g. histology, are destructive and limited to small volumes, creating a critical need for a non-destructive modality for imaging microscopic fiber orientations in 3D. Here, we use Hierarchical Phase-Contrast Tomography (HiP-CT) to characterize white matter architecture at the microscale. Applying structure-tensor analysis to HiP-CT data, we compute fiber Orientation Distribution Functions and perform tractography analogous to dMRI. Across multiple brain regions, HiP-CT derived fiber architecture shows strong correspondence with that derived from dMRI while revealing substantially greater microstructural complexity. Despite its label-free nature, we demonstrate that vascular structures minimally confound HiP-CT orientation estimates. These results establish HiP-CT as a reference microscopic modality that can complement dMRI in multi-scale studies of white-matter organization.

neuroscience↗

bioRxiv2026

Hierarchical X-ray microscopy and mesoscopic diffusion MRI in the same brain reveal the human connectome across scales

We present a multimodal pipeline for 3D imaging of cerebral white-matter archi-tecture across scales, from whole-brain axonal projections down to individual myelinated axons. After diffusion MRI, an adult ex vivo human hemisphere undergoes label-free imaging with Hierarchical Phase-Contrast Tomography (HiP-CT) from 20 {micro}m/voxel in the whole hemisphere to 2 {micro}m/voxel in areas of interest, with intrinsic cross-scale alignment. A 4 cm tissue block extracted from the hemisphere is reimaged with HiP-CT at 0.857 {micro}m/voxel, enabling direct visualisation of single myelinated axons. After osmium staining, micro-CT at 0.364 {micro}m/voxel and electron microscopy at 4 nm/voxel are acquired in biop-sies from the tissue block to validate the presence of myelinated axons in the label-free HiP-CT contrast. Spanning three orders of magnitude in resolution, these co-registered multimodal datasets bridge microscopic wiring and macro-scopic brain organisation, providing a foundation for anatomically grounded whole-brain connectomics.

neuroscience↗

bioRxiv2026

A human subcortical connectome at 400 μm resolution

The fiber pathways of the human subcortex are of particular importance in clinical neuroscience, as they are targeted by neuromodulation therapies, such as deep brain stimulation (DBS) and lesion approaches, in various motor and psychiatric disorders. However, the complexity, size, and position of these pathways make them challenging to image non-invasively with diffusion MRI (dMRI). As a result, recent efforts in atlasing these pathways to guide neuromodulation have resorted to using synthetic data. Here we present the first extensive reconstruction of fiber pathways of the human subcortex with ultra-high-resolution dMRI. We leverage a unique, ex vivo dMRI dataset acquired by the BRAIN CONNECTS center for Large-scale Imaging of Neural Circuits (LINC) on the first-of-its-kind Connectome 2.0 scanner. Our contribution is two-fold. First, by showing the feasibility of reconstructing these pathways with non-invasive neuroimaging at the single subject level, we set the stage for future research into reconstructing them in vivo in individual patients. Second, we provide a high-definition atlas of basal-ganglia-thalamocortical circuits that is readily extensible via our publicly released data and annotations. As a first demonstration of its clinical validity, we align our atlas to "hotspots" from previous DBS studies and identify pathways associated with therapeutic or side effects.

neuroscience↗

bioRxiv2026

Multi-dimensional diffusion MRI at ultra-high gradient strength for mapping axonal architecture and microstructure in the primate brain

We present the most comprehensive sampling of the macaque and human brain with diffusion MRI to date. As part of the BRAIN CONNECTS center for Large-scale Imaging of Neural Circuits, we leverage ultra-high-gradient MRI systems, including the first-of-its-kind Connectome 2.0, for post-mortem acquisitions. Each sample is imaged for [~]250 hours at multiple spatial resolutions down to 0.25 mm isotropic for whole macaque brains and 0.4 mm isotropic for human hemispheres. Our optimized protocols allow us to sample both species across [~]50 diffusion shells varying in b-value, diffusion time, and echo time, reaching ultra-high b-values up to 64000 s/mm2 with high signal-to-noise ratio. We demonstrate that these multi-dimensional data resolve not only white matter connectional architecture but also cortical and subcortical cytoarchitectonic boundaries, at a level of detail previously inaccessible in whole-brain noninvasive imaging. As such, these data are an important resource for both technical development and basic and clinical neuroscience.

neuroscience↗

bioRxiv2026

The modular nature of long-association pathways in the human brain

Human brain functions are organized within large-scale networks, yet the long-range structural pathways that support them remain only partly understood. Diffusion MRI tractography has enabled non-invasive mapping of these connections but its low resolution has also contributed to a view of white matter association bundles as monolithic entities linking distant cortical regions. This contrasts with anatomical studies in non-human primates and humans, which show these bundles act as conduits for complex systems of short- and long-range projections. Here we investigate the dorsal branch of the superior longitudinal fasciculus (SLF-I), the proposed structural backbone of the dorsal attention network, whose anatomy in humans is debated. Using anatomic tracing, polarization-sensitive optical coherence tomography, and in vivo and ex vivo diffusion MRI, we reconstruct the SLF-I across species, modalities, and scales. High resolution tractography (<750 m), consistent with tracer and PS-OCT data, reveals multiple fiber systems of varying lengths rather than uniform parieto-frontal connections observed at more conventional resolution (1.5 mm). Comparative analyses show a consistent organization: the SLF-I carries short-range cortico-cortical projections rostral to area 6 and longer, layered connections extending to parietal regions, while direct parieto-frontal connectivity is mostly supported by the adjacent cingulum bundle. These findings reconcile our understanding of the SLF-I across the scales of non-invasive neuroimaging and invasive anatomic studies and provide an updated framework for future investigations of large-scale networks in health and disease.

neuroscience↗

bioRxiv2025

The cellular associates of late life changes in white matter microstructure

The microstructural architecture of white matter supporting information flow across local circuits and large-scale networks changes throughout the lifespan. However, the genetic and cellular factors underlying age-related variations in white matter microstructure have yet to be established. Here, we examined the genetic associates of individual differences in diffusion-based measures of white matter in a population-based cohort (N=29,862) from the UK Biobank. Estimates of heritability from Genome-Wide Association Study (GWAS) data revealed that genetic factors are linked to population variability in 96.1% of 432 tract microstructural measures. The presence of shared genetic influences was observed to be greater within, relative to between, broad tract classes (commissural, association, projection, and complex cerebellar). Age associations with microstructural changes were estimated across diffusivity measures, with association class tracts showing the greatest vulnerability to age-related decline in older adults. Analyses of imputed cellular associates of age-related changes in white matter revealed a preferential relationship with cell gene markers of oligodendrocytes and other glial cell types, with sparse relationships observed for inhibitory and excitatory cells. These data indicate that white matter tract microstructure is shaped by genetic factors and suggest a role for glial cell-related transcripts in late-life changes in the structural wiring properties of the human brain. Significance StatementThe structural wiring of the human brain supports interregional communication and is essential for cognition, yet the genetic and cellular drivers underlying age-related changes are not well understood. Here, we characterize the extent to which genetic factors follow the major organizational patterns of white matter connectivity and associated age-related changes in late life. Age-related changes were most pronounced in late-developing association tracts, key pathways for higher cognitive functions. By integrating genetic and transcriptomic data, we show that genetic variation linked to glial cell markers accounts for a substantial proportion of age-related white matter changes. These findings provide a mechanistic framework for understanding genetic and cellular determinants of structural brain aging.

neuroscience↗

bioRxiv2024

A network correspondence toolbox for quantitative evaluation of novel neuroimaging results

Decades of neuroscience research has shown that macroscale brain dynamics can be reliably decomposed into a subset of large-scale functional networks, but the specific spatial topographies of these networks and the names used to describe them can vary across studies. Such discordance has hampered interpretation and convergence of research findings across the field. To address this problem, we have developed the Network Correspondence Toolbox (NCT) to permit researchers to examine and report spatial correspondence between their novel neuroimaging results and sixteen widely used functional brain atlases, consistent with recommended reporting standards developed by the Organization for Human Brain Mapping. The atlases included in the toolbox show some topographical convergence for specific networks, such as those labeled as "default" or "visual". Network naming varies across atlases, particularly for networks spanning frontoparietal association cortices. For this reason, quantitative comparison with multiple atlases is recommended to benchmark novel neuroimaging findings. We provide several exemplar demonstrations using the Human Connectome Project task fMRI results and UK Biobank independent component analysis maps to illustrate how researchers can use the NCT to report their own findings through quantitative evaluation against multiple published atlases. The NCT provides a convenient means for computing Dice coefficients with spin test permutations to determine the magnitude and statistical significance of correspondence among user-defined maps and existing atlas labels. The NCT also includes functionality to incorporate additional atlases in the future. The adoption of the NCT will make it easier for network neuroscience researchers to report their findings in a standardized manner, thus aiding reproducibility and facilitating comparisons between studies to produce interdisciplinary insights.

neuroscience↗

bioRxiv2024

Translation of monosynaptic circuits underlying amygdala fMRI neurofeedback training

BackgroundfMRI neurofeedback targeting the amygdala is a promising therapeutical tool in psychiatry. It induces resting-state functional connectivity (rsFC) changes between the amygdala and regions of the salience and default mode networks (SN and DMN, respectively). We hypothesize these rsFC changes happen on the amygdalas underlying anatomical circuits. MethodsWe used the coordinates from regions of interest (ROIs) from studies showing pre-to-post-neurofeedback changes in rsFC with the left amygdala. Using a cross-species brain parcellation, we identified the homologous locations in non-human primates. We injected bidirectional tracers in the amygdala of adult macaques and used bright- and dark-field microscopy to identify cells and axon terminals in each ROI. We also performed additional injections in specific ROIs to validate the results following amygdala injections and delineate potential disynaptic pathways. Finally, we used high-resolution diffusion MRI data from four post-mortem macaque brains and one in vivo human brain to translate our findings to the neuroimaging domain. ResultsThe amygdala had significant monosynaptic connections with all the SN and DMN ipsilateral ROIs. Amygdala connections with the DMN contralateral ROIs are disynaptic through the hippocampus and parahippocampal gyrus. Diffusion MRI in both species benefitted from using the ground-truth tracer data to validate its findings, as we identified false-negative ipsilateral and false-positive contralateral connectivity results. ConclusionsAmygdala neurofeedback modulates the SN and DMN through monosynaptic connections and disynaptic pathways - including hippocampal structures involved in the neurofeedback task. Neurofeedback may be a tool for rapid modulation and reinforcement of these anatomical connections, leading to clinical improvement.

neuroscience↗

bioRxiv2023

Self-supervised segmentation and characterization of fiber bundles in anatomic tracing data

Anatomic tracing is the gold standard tool for delineating brain connections and for validating more recently developed imaging approaches such as diffusion MRI tractography. A key step in the analysis of data from tracer experiments is the careful, manual charting of fiber trajectories on histological sections. This is a very time-consuming process, which limits the amount of annotated tracer data that are available for validation studies. Thus, there is a need to accelerate this process by developing a method for computer-assisted segmentation. Such a method must be robust to the common artifacts in tracer data, including variations in the intensity of stained axons and background, as well as spatial distortions introduced by sectioning and mounting the tissue. The method should also achieve satisfactory performance using limited manually charted data for training. Here we propose the first deep-learning method, with a self-supervised loss function, for segmentation of fiber bundles on histological sections from macaque brains that have received tracer injections. We address the limited availability of manual labels with a semi-supervised training technique that takes advantage of unlabeled data to improve performance. We also introduce anatomic and across-section continuity constraints to improve accuracy. We show that our method can be trained on manually charted sections from a single case and segment unseen sections from different cases, with a true positive rate of ~0.80. We further demonstrate the utility of our method by quantifying the density of fiber bundles as they travel through different white-matter pathways. We show that fiber bundles originating in the same injection site have different levels of density when they travel through different pathways, a finding that can have implications for microstructure-informed tractography methods. The code for our method is available at https://github.com/v-sundaresan/fiberbundle_seg_tracing.

neuroscience↗

bioRxiv2023

Eddy current-induced artifacts correction in high gradient strength diffusion MRI with dynamic field monitoring: demonstration in ex vivo human brain imaging

PurposeTo demonstrate the advantages of spatiotemporal magnetic field monitoring to correct eddy current-induced artifacts (ghosting and geometric distortions) in high gradient strength diffusion MRI (dMRI). MethodsA dynamic field camera with 16 NMR field probes was used to characterize eddy current fields induced from diffusion gradients for different gradients strengths (up to 300 mT/m), diffusion directions, and shots in a 3D multi-shot EPI sequence on a 3T Connectom scanner. The efficacy of dynamic field monitoring-based image reconstruction was demonstrated on high-resolution whole brain ex vivo dMRI. A 3D multi-shot image reconstruction framework was informed with the actual nonlinear phase evolution measured with the dynamic field camera, thereby accounting for high-order eddy currents fields on top of the image encoding gradients in the image formation model. ResultsEddy current fields from diffusion gradients at high gradient strength in a 3T Connectom scanner are highly nonlinear in space and time, inducing high-order spatial phase modulations between odd/even echoes and shots that are not static during the readout. Superior reduction of ghosting and geometric distortion was achieved with dynamic field monitoring compared to ghosting approaches such as navigator- and structured low-rank-based methods or MUSE, followed by image-based distortion correction with eddy. Improved dMRI analysis is demonstrated with diffusion tensor imaging and high-angular resolution diffusion imaging. ConclusionStrong eddy current artifacts characteristic of high gradient strength dMRI can be well corrected with dynamic field monitoring-based image reconstruction, unlike the two-step approach consisting of ghosting correction followed by geometric distortion reduction with eddy.

bioengineering↗

bioRxiv2022

Accurate Bayesian segmentation of thalamic nuclei using diffusion MRI and an improved histological atlas

The human thalamus is a highly connected brain structure, which is key for the control of numerous functions and is involved in several neurological disorders. Recently, neuroimaging studies have increasingly focused on the volume and connectivity of the specific nuclei comprising this structure, rather than looking at the thalamus as a whole. However, accurate identification of cytoarchitectonically designed histological nuclei on standard in vivo structural MRI is hampered by the lack of image contrast that can be used to distinguish nuclei from each other and from surrounding white matter tracts. While diffusion MRI may offer such contrast, it has lower resolution and lacks some boundaries visible in structural imaging. In this work, we present a Bayesian segmentation algorithm for the thalamus. This algorithm combines prior information from a probabilistic atlas with likelihood models for both structural and diffusion MRI, allowing label boundaries to be informed by both modalities. We present an improved probabilistic atlas, incorporating 26 thalamic nuclei identified from histology and 45 white matter tracts identified in ultra-high gradient strength diffusion imaging. We present a family of likelihood models for diffusion tensor imaging, ensuring compatibility with the vast majority of neuroimaging datasets that include diffusion MRI data. The use of these diffusion likelihood models greatly improves identification of nuclei versus segmentation based solely on structural MRI. Dice comparison of 5 manually identifiable groups of nuclei to ground truth segmentations show improvements of up to 10 percentage points. Additionally, our chosen model shows a high degree of reliability, with median test-retest Dice scores above 0.85 for four out of five nuclei groups, whilst also offering improved detection of differential thalamic involvement in Alzheimers disease (AUROC 83.36%). The probabilistic atlas and segmentation tool will be made publicly available as part of the neuroimaging package FreeSurfer.

neuroscience↗

bioRxiv2022

Targeting default mode network connectivity with mindfulness-based fMRI neurofeedback: A pilot study among adolescents with affective disorder history

Adolescents experience alarmingly high rates of major depressive disorder (MDD), however, gold-standard treatments are only effective for ~50% of youth. Accordingly, there is a critical need to develop novel interventions, particularly ones that target neural mechanisms believed to potentiate depressive symptoms. Directly addressing this gap, we developed a mindfulness-based fMRI neurofeedback (mbNF) for adolescents that targets default mode network (DMN) hyperconnectivity, which has been implicated in the onset and maintenance of MDD. In this proof-of-concept study, adolescents (n = 9) with a lifetime history of depression and/or anxiety were administered clinical interviews and self-report questionnaires, and then, each participants DMN and central executive network (CEN) were personalized using a resting state fMRI localizer. After the localizer scan, adolescents completed a brief mindfulness training followed by a mbNF session in the scanner wherein they were instructed to volitionally reduce DMN relative to CEN activation by practicing mindfulness meditation. Several promising findings emerged. First, mbNF successfully engaged the target brain state during neurofeedback; participants spent more time in the target state with DMN activation lower than CEN activation. Second, in each of the nine adolescents, mbNF led to significantly reduced within-DMN connectivity, which correlated with post-mbNF increases in state mindfulness. Last, a reduction of within-DMN connectivity mediated the association between better mbNF performance and increased state mindfulness. These findings demonstrate that personalized mbNF can effectively and non-invasively modulate the intrinsic networks known to be associated with the emergence and persistence of depressive symptoms during adolescence.

neuroscience↗

bioRxiv2022

The rostral zona incerta: a subcortical integrative hub and potential DBS target for OCD

BackgroundThe zona incerta (ZI) is involved in mediating survival behaviors and is connected to a wide range of cortical and subcortical structures, including key basal ganglia nuclei. Based on these connections and their links to behavioral modulation, we propose the ZI is a connectional hub for in mediating between top-down and bottom-up control and a possible target for deep brain stimulation for obsessive compulsive disorder. MethodsWe analyzed the trajectory of cortical fibers to the ZI in nonhuman and human primates, based on tracer injections in monkeys and high-resolution diffusion MRI in humans. The organization of cortical and subcortical connections with the ZI were identified in the nonhuman primate studies. ResultsMonkey anatomic data and human dMRI data showed a similar trajectory of fibers/streamlines to the ZI. PFC/ACC terminals all converge within the rostral ZI (ZIr), with dorsal and lateral areas most prominent. Motor areas terminate caudally. Dense subcortical reciprocal connections included the thalamus, medial hypothalamus, substantia nigra/ventral tegmental area, reticular formation, and pedunculopontine nucleus and a dense nonreciprocal projection to the lateral habenula (LHb). Additional connections included amygdala, dorsal raphe nucleus, and periaqueductal grey. ConclusionsDense connections with dorsal and lateral PFC/ACC cognitive control areas and LHb and SN/VTA coupled with inputs from the amygdala, hypothalamus, and brainstem, suggests that the ZIr is a subcortical hub positioned to modulate between top-down and bottom-up control. A DBS electrode placed in the ZIr would involve both connections common to other DBS sites, but also would capture several critically distinctive connections.

neuroscience↗

bioRxiv2021

Refractive-index matching enhanced polarization-sensitive optical coherence tomography quantification in human brain tissue

The importance of polarization-sensitive optical coherence tomography (PS-OCT) has been increasingly recognized in human brain imaging. Despite the recent progress of PS-OCT in revealing white matter architecture and orientation, quantification of fine-scale fiber tracts in the human brain cortex has been a challenging problem, due to a low birefringence in the gray matter. In this study, we investigated the effect of refractive index matching by 2,2-thiodiethanol (TDE) immersion on the improvement of PS-OCT measurements in ex vivo human brain tissue. We obtain the cortical fiber orientation maps in the gray matter, which reveals the radial fibers in the gyrus, the U-fibers along the sulcus, as well as distinct layers of fiber axes exhibiting laminar organization. Further analysis shows that index matching reduces the noise in axis orientation measurements by 56% and 39%, in white and gray matter, respectively. Index matching also enables precise measurements of apparent birefringence, which was underestimated in the white matter by 82% but overestimated in the gray matter by 16% prior to TDE immersion. Mathematical simulations show that the improvements are primarily attributed to the reduction in the tissue scattering coefficient, leading to an enhanced signal-to-noise ratio in deeper tissue regions, which could not be achieved by conventional noise reduction methods.

bioengineering↗

bioRxiv2021

High-fidelity, high-spatial-resolution diffusion MRI of the ex-vivo whole human brain on the 3T Connectom scanner using structured low-rank EPI ghost correction

Diffusion MRI (dMRI) of whole, intact, fixed postmortem human brain at high spatial resolution serves as key bridging technology for 3D mapping of structural connectivity and tissue microstructure at the mesoscopic scale. Ex vivo dMRI offers superior spatial resolution compared to in vivo dMRI but comes with its own technical challenges due to the significantly reduced T2 relaxation times and decreased diffusivity incurred by tissue fixation. The altered physical properties of fixed tissue necessitate the use of alternative acquisition strategies to preserve SNR and achieve sufficient diffusion weighting. Multi-shot or segmented 3D echo planar imaging (EPI) sequences have been used to shorten echo times (TEs) with reduced distortions from field inhomogeneity and eddy currents on small-bore MR scanners and have been adopted for high b-value dMRI of ex vivo whole human brain specimens. The advent of stronger gradients on human MRI scanners has led to improved image quality and a wider range of diffusion-encoding parameters for dMRI but at the cost of more severe eddy currents that result in spatial and temporal variations in the background magnetic field, which cannot be corrected for using standard vendor-provided ghost correction solutions. In this work, we show that conventional ghost correction techniques based on navigators and linear phase correction may be insufficient for EPI sequences using strong diffusion-sensitizing gradients in ex vivo dMRI experiments, resulting in orientationally biased dMRI estimates. This previously unreported problem is a critical roadblock in any effort to leverage scanners with ultra-high gradients for high-precision mapping of human neuroanatomy at the mesoscopic scale. We propose an advanced reconstruction method based on structured low-rank matrix modeling that reduces the ghosting substantially. We show that this method leads to more accurate and reliable dMRI metrics, as exemplified by diffusion tensor imaging and high angular diffusion imaging analyses in distributed neuroanatomical areas of fixed whole human brain specimens. Our findings advocate for the use of advanced reconstruction techniques for recovering unbiased metrics from ex vivo dMRI acquisitions and represent a crucial step toward making full use of strong diffusion-encoding gradients for neuroscientific studies seeking to study brain structure at multiple spatial scales.

neuroscience↗

bioRxiv2021

Using diffusion MRI data acquired with ultra-high gradients to improve tractography in routine-quality data

The development of scanners with ultra-high gradients, spearheaded by the Human Connectome Project, has led to dramatic improvements in the spatial, angular, and diffusion resolution that is feasible for in vivo diffusion MRI acquisitions. The improved quality of the data can be exploited to achieve higher accuracy in the inference of both microstructural and macrostructural anatomy. However, such high-quality data can only be acquired on a handful of Connectom MRI scanners worldwide, while remaining prohibitive in clinical settings because of the constraints imposed by hardware and scanning time. In this study, we first update the classical protocols for tractography-based, manual annotation of major white-matter pathways, to adapt them to the much greater volume and variability of the streamlines that can be produced from todays state-of-the-art diffusion MRI data. We then use these protocols to annotate 42 major pathways manually in data from a Connectom scanner. Finally, we show that, when we use these manually annotated pathways as training data for global probabilistic tractography with anatomical neighborhood priors, we can perform highly accurate, automated reconstruction of the same pathways in much lower-quality, more widely available diffusion MRI data. The outcomes of this work include both a new, comprehensive atlas of WM pathways from Connectom data, and an updated version of our tractography toolbox, TRActs Constrained by UnderLying Anatomy (TRACULA), which is trained on data from this atlas. Both the atlas and TRACULA are distributed publicly as part of FreeSurfer. We present the first comprehensive comparison of TRACULA to the more conventional, multi-region-of-interest approach to automated tractography, and the first demonstration of training TRACULA on high-quality, Connectom data to benefit studies that use more modest acquisition protocols.

neuroscience↗

bioRxiv2021

Post mortem mapping of connectional anatomy for the validation of diffusion MRI

Despite the impressive advances in diffusion MRI (dMRI) acquisition and analysis that have taken place during the Human Connectome era, dMRI tractography is still an imperfect source of information on the circuitry of the brain. In this review, we discuss methods for post mortem validation of dMRI tractography, fiber orientations, and other microstructural properties of axon bundles that are typically extracted from dMRI data. These methods include anatomic tracer studies, Klinglers dissection, myelin stains, label-free optical imaging techniques, and others. We provide an overview of the basic principles of each technique, its limitations, and what it has taught us so far about the accuracy of different dMRI acquisition and analysis approaches.

neuroscience↗

bioRxiv2021

A 48-Channel Receive Array Coil for Mesoscopic Diffusion-Weighted MRI of Human ex vivo Brain Imaging on the 3T Connectome Scanner

In vivo diffusion-weighted magnetic resonance imaging is limited in signal-to-noise-ratio (SNR) and acquisition time, which constrains spatial resolution to the macroscale regime. Ex vivo imaging, which allows for arbitrarily long scan times, is critical for exploring human brain structure in the mesoscale regime without loss of SNR. Standard head array coils designed for patients are sub-optimal for imaging ex vivo whole brain specimens. The goal of this work was to design and construct a 48-channel ex vivo whole brain array coil for high-resolution and high b-value diffusion-weighted imaging on a 3T Connectome scanner. The coil was validated with bench measurements and characterized by imaging metrics on an agar brain phantom and an ex vivo human brain sample. The two-segment coil former was constructed for a close fit to a whole human brain, with small receive elements distributed over the entire brain. Imaging tests including SNR and G-factor maps were compared to a 64-channel head coil designed for in vivo use. There was a 2.9-fold increase in SNR in the peripheral cortex and a 1.3-fold gain in the center when compared to the 64-ch head coil. The 48-channel ex vivo whole brain coil also decreases noise amplification in highly parallel imaging, allowing acceleration factors of approximately one unit higher for a given noise amplification level. The acquired diffusion-weighted images in a whole ex vivo brain specimen demonstrate the applicability of the developed coil for high-resolution and high b-value diffusion-weighted ex vivo brain MRI studies.

bioengineering↗