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Article: Assessing the Reliability of Template-Based Clustering for Tractography in Healthy Human Adults.

Kai, Jason / Khan, Ali R

2022 Volume 16, Page(s) 777853

Abstract: Tractography is a non-invasive technique to investigate the brain's structural pathways (also referred to as tracts) that connect different brain regions. A commonly used approach for identifying tracts is with template-based clustering, where ... ...

Abstract	Tractography is a non-invasive technique to investigate the brain's structural pathways (also referred to as tracts) that connect different brain regions. A commonly used approach for identifying tracts is with template-based clustering, where unsupervised clustering is first performed on a template in order to label corresponding tracts in unseen data. However, the reliability of this approach has not been extensively studied. Here, an investigation into template-based clustering reliability was performed, assessing the output from two datasets: Human Connectome Project (HCP) and MyConnectome project. The effect of intersubject variability on template-based clustering reliability was investigated, as well as the reliability of both deep and superficial white matter tracts. Identified tracts were evaluated by assessing Euclidean distances from a dataset-specific tract average centroid, the volumetric overlap across corresponding tracts, and along-tract agreement of quantitative values. Further, two template-based techniques were employed to evaluate the reliability of different clustering approaches. Reliability assessment can increase the confidence of a tract identifying technique in future applications to study pathways of interest. The two different template-based approaches exhibited similar reliability for identifying both deep white matter tracts and the superficial white matter.
Language	English
Publishing date	2022-02-17
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2452979-5
ISSN	1662-5196
ISSN	1662-5196
DOI	10.3389/fninf.2022.777853
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Article ; Online: Aberrant frontal lobe "U"-shaped association fibers in first-episode schizophrenia: A 7-Tesla Diffusion Imaging Study.

Kai, Jason / Mackinley, Michael / Khan, Ali R / Palaniyappan, Lena

NeuroImage. Clinical

2023 Volume 38, Page(s) 103367

Abstract: Schizophrenia is believed to be a developmental disorder with one hypothesis suggesting that symptoms arise due to abnormal interactions (or disconnectivity) between different brain regions. While some major deep white matter pathways have been ... ...

Abstract	Schizophrenia is believed to be a developmental disorder with one hypothesis suggesting that symptoms arise due to abnormal interactions (or disconnectivity) between different brain regions. While some major deep white matter pathways have been extensively studied (e.g. arcuate fasciculus), studies of short-ranged, "U"-shaped tracts have been limited in patients with schizophrenia, in part due to the sheer abundance of tracts present and due to the spatial variations across individuals that defy probabilistic characterization in the absence of reliable templates. In this study, we use diffusion magnetic resonance imaging (dMRI) to investigate frontal lobe superficial white matter that are present in the majority of study participants, comparing healthy controls and minimally treated patients with first-episode schizophrenia (<3 median days of lifetime treatment). Through group comparisons, 3 out of 63 frontal lobe "U"-shaped tracts were found to demonstrate localized aberrations affecting the microstructural tissue properties (via diffusion tensor metrics) in this early stage of disease. No associations were found in patients between aberrant segments of affected tracts and clinical or cognitive variables. Aberrations in the frontal lobe "U"-shaped tracts in early untreated stages of psychosis occur irrespective of symptom burden, and are distributed across critical functional networks associated with executive function and salience processing. While we limited the investigation to the frontal lobe, a framework has been developed to study such connections in other brain regions, enabling further extensive investigations jointly with the major deep white matter pathways.
MeSH term(s)	Humans ; Schizophrenia/diagnosis ; Frontal Lobe/diagnostic imaging ; Frontal Lobe/pathology ; Brain/pathology ; Diffusion Magnetic Resonance Imaging ; Psychotic Disorders/pathology
Language	English
Publishing date	2023-03-05
Publishing country	Netherlands
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2701571-3
ISSN	2213-1582 ; 2213-1582
ISSN (online)	2213-1582
ISSN	2213-1582
DOI	10.1016/j.nicl.2023.103367
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Article ; Online: A population-averaged structural connectomic brain atlas dataset from 422 HCP-aging subjects.

Xiao, Yiming / Gilmore, Greydon / Kai, Jason / Lau, Jonathan C / Peters, Terry / Khan, Ali R

Data in brief

2023 Volume 50, Page(s) 109513

Abstract: Population-averaged brain atlases, that are represented in a standard space with anatomical labels, are instrumental tools in neurosurgical planning and the study of neurodegenerative conditions. Traditional brain atlases are primarily derived from ... ...

Abstract	Population-averaged brain atlases, that are represented in a standard space with anatomical labels, are instrumental tools in neurosurgical planning and the study of neurodegenerative conditions. Traditional brain atlases are primarily derived from anatomical scans and contain limited information regarding the axonal organization of the white matter. With the advance of diffusion MRI that allows the modeling of fiber orientation distribution (FOD) in the brain tissue, there is an increasing interest for a population-averaged FOD template, especially based on a large healthy aging cohort, to offer structural connectivity information for connectomic surgery and analysis of neurodegeneration. The dataset described in this article contains a set of multi-contrast structural connectomic MRI atlases, including T1w, T2w, and FOD templates, along with the associated whole brain tractograms. The templates were made using multi-contrast group-wise registration based on 3T MRIs of 422 Human Connectome Project in Aging (HCP-A) subjects. To enhance the usability, probabilistic tissue maps and segmentation of 22 subcortical structures are provided. Finally, the subthalamic nucleus shown in the atlas is parcellated into sensorimotor, limbic, and associative sub-regions based on their structural connectivity to facilitate the analysis and planning of deep brain stimulation procedures. The dataset is available on the OSF Repository: https://osf.io/p7syt.
Language	English
Publishing date	2023-08-22
Publishing country	Netherlands
Document type	Journal Article
ZDB-ID	2786545-9
ISSN	2352-3409 ; 2352-3409
ISSN (online)	2352-3409
ISSN	2352-3409
DOI	10.1016/j.dib.2023.109513
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Article ; Online: Mapping the subcortical connectome using in vivo diffusion MRI: Feasibility and reliability.

Kai, Jason / Khan, Ali R / Haast, Roy Am / Lau, Jonathan C

NeuroImage

2022 Volume 262, Page(s) 119553

Abstract: Tractography combined with regions of interest (ROIs) has been used to non-invasively study the structural connectivity of the cortex as well as to assess the reliability of these connections. However, the subcortical connectome (subcortex to subcortex) ... ...

Abstract	Tractography combined with regions of interest (ROIs) has been used to non-invasively study the structural connectivity of the cortex as well as to assess the reliability of these connections. However, the subcortical connectome (subcortex to subcortex) has not been comprehensively examined, in part due to the difficulty of performing tractography in this complex and compact region. In this study, we performed an in vivo investigation using tractography to assess the feasibility and reliability of mapping known connections between structures of the subcortex using the test-retest dataset from the Human Connectome Project (HCP). We further validated our observations using a separate unrelated subjects dataset from the HCP. Quantitative assessment was performed by computing tract densities and spatial overlap of identified connections between subcortical ROIs. Further, known connections between structures of the basal ganglia and thalamus were identified and visually inspected, comparing tractography reconstructed trajectories with descriptions from tract-tracing studies. Our observations demonstrate both the feasibility and reliability of using a data-driven tractography-based approach to map the subcortical connectome in vivo.
MeSH term(s)	Cerebral Cortex ; Connectome ; Diffusion Magnetic Resonance Imaging ; Feasibility Studies ; Humans ; Reproducibility of Results
Language	English
Publishing date	2022-08-09
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	1147767-2
ISSN	1095-9572 ; 1053-8119
ISSN (online)	1095-9572
ISSN	1053-8119
DOI	10.1016/j.neuroimage.2022.119553
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Article ; Online: Funcmasker-flex: An Automated BIDS-App for Brain Segmentation of Human Fetal Functional MRI data.

Nichols, Emily S / Correa, Susana / Van Dyken, Peter / Kai, Jason / Kuehn, Tristan / de Ribaupierre, Sandrine / Duerden, Emma G / Khan, Ali R

Neuroinformatics

2023 Volume 21, Issue 3, Page(s) 565–573

Abstract: Fetal functional magnetic resonance imaging (fMRI) offers critical insight into the developing brain and could aid in predicting developmental outcomes. As the fetal brain is surrounded by heterogeneous tissue, it is not possible to use adult- or child- ... ...

Abstract	Fetal functional magnetic resonance imaging (fMRI) offers critical insight into the developing brain and could aid in predicting developmental outcomes. As the fetal brain is surrounded by heterogeneous tissue, it is not possible to use adult- or child-based segmentation toolboxes. Manually-segmented masks can be used to extract the fetal brain; however, this comes at significant time costs. Here, we present a new BIDS App for masking fetal fMRI, funcmasker-flex, that overcomes these issues with a robust 3D convolutional neural network (U-net) architecture implemented in an extensible and transparent Snakemake workflow. Open-access fetal fMRI data with manual brain masks from 159 fetuses (1103 total volumes) were used for training and testing the U-net model. We also tested generalizability of the model using 82 locally acquired functional scans from 19 fetuses, which included over 2300 manually segmented volumes. Dice metrics were used to compare performance of funcmasker-flex to the ground truth manually segmented volumes, and segmentations were consistently robust (all Dice metrics ≥ 0.74). The tool is freely available and can be applied to any BIDS dataset containing fetal bold sequences. Funcmasker-flex reduces the need for manual segmentation, even when applied to novel fetal functional datasets, resulting in significant time-cost savings for performing fetal fMRI analysis.
MeSH term(s)	Adult ; Humans ; Mobile Applications ; Magnetic Resonance Imaging/methods ; Neural Networks, Computer ; Brain/diagnostic imaging ; Fetus/diagnostic imaging ; Image Processing, Computer-Assisted/methods
Language	English
Publishing date	2023-03-31
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2111941-7
ISSN	1559-0089 ; 1539-2791
ISSN (online)	1559-0089
ISSN	1539-2791
DOI	10.1007/s12021-023-09629-3
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Article ; Online: Diffusion dispersion imaging: Mapping oscillating gradient spin-echo frequency dependence in the human brain.

Arbabi, Aidin / Kai, Jason / Khan, Ali R / Baron, Corey A

Magnetic resonance in medicine

2019 Volume 83, Issue 6, Page(s) 2197–2208

Abstract: Purpose: Oscillating gradient spin-echo (OGSE) diffusion MRI provides information about the microstructure of biological tissues by means of the frequency dependence of the apparent diffusion coefficient (ADC). ADC dependence on OGSE frequency has been ... ...

Abstract	Purpose: Oscillating gradient spin-echo (OGSE) diffusion MRI provides information about the microstructure of biological tissues by means of the frequency dependence of the apparent diffusion coefficient (ADC). ADC dependence on OGSE frequency has been explored in numerous rodent studies, but applications in the human brain have been limited and have suffered from low contrast between different frequencies, long scan times, and a limited exploration of the nature of the ADC dependence on frequency. Theory and methods: Multiple frequency OGSE acquisitions were acquired in healthy subjects at 7T to explore the power-law frequency dependence of ADC, the "diffusion dispersion." Furthermore, a method for optimizing the estimation of the ADC difference between different OGSE frequencies was developed, which enabled the design of a highly efficient protocol for mapping diffusion dispersion. Results: For the first time, evidence of a linear dependence of ADC on the square root of frequency in healthy human white matter was obtained. Using the optimized protocol, high-quality, full-brain maps of apparent diffusion dispersion rate were also demonstrated at an isotropic resolution of 2 mm in a scan time of 6 min. Conclusions: This work sheds light on the nature of diffusion dispersion in the healthy human brain and introduces full-brain diffusion dispersion mapping at clinically relevant scan times. These advances may lead to new biomarkers of pathology or improved microstructural modeling.
MeSH term(s)	Brain/diagnostic imaging ; Brain Mapping ; Diffusion ; Diffusion Magnetic Resonance Imaging ; Humans ; Magnetic Resonance Imaging
Language	English
Publishing date	2019-11-25
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	605774-3
ISSN	1522-2594 ; 0740-3194
ISSN (online)	1522-2594
ISSN	0740-3194
DOI	10.1002/mrm.28083
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Article ; Online: Magnetic resonance imaging datasets with anatomical fiducials for quality control and registration.

Taha, Alaa / Gilmore, Greydon / Abbass, Mohamad / Kai, Jason / Kuehn, Tristan / Demarco, John / Gupta, Geetika / Zajner, Chris / Cao, Daniel / Chevalier, Ryan / Ahmed, Abrar / Hadi, Ali / Karat, Bradley G / Stanley, Olivia W / Park, Patrick J / Ferko, Kayla M / Hemachandra, Dimuthu / Vassallo, Reid / Jach, Magdalena /

Thurairajah, Arun / Wong, Sandy / Tenorio, Mauricio C / Ogunsanya, Feyi / Khan, Ali R / Lau, Jonathan C

Scientific data

2023 Volume 10, Issue 1, Page(s) 449

Abstract: Tools available for reproducible, quantitative assessment of brain correspondence have been limited. We previously validated the anatomical fiducial (AFID) placement protocol for point-based assessment of image registration with millimetric (mm) accuracy. ...

Abstract	Tools available for reproducible, quantitative assessment of brain correspondence have been limited. We previously validated the anatomical fiducial (AFID) placement protocol for point-based assessment of image registration with millimetric (mm) accuracy. In this data descriptor, we release curated AFID placements for some of the most commonly used structural magnetic resonance imaging datasets and templates. The release of our accurate placements allows for rapid quality control of image registration, teaching neuroanatomy, and clinical applications such as disease diagnosis and surgical targeting. We release placements on individual subjects from four datasets (N = 132 subjects for a total of 15,232 fiducials) and 14 brain templates (4,288 fiducials), totalling more than 300 human rater hours of annotation. We also validate human rater accuracy of released placements to be within 1 - 2 mm (using more than 45,000 Euclidean distances), consistent with prior studies. Our data is compliant with the Brain Imaging Data Structure allowing for facile incorporation into neuroimaging analysis pipelines.
MeSH term(s)	Humans ; Brain/diagnostic imaging ; Magnetic Resonance Imaging ; Neuroimaging ; Quality Control
Language	English
Publishing date	2023-07-12
Publishing country	England
Document type	Dataset ; Journal Article
ZDB-ID	2775191-0
ISSN	2052-4463 ; 2052-4463
ISSN (online)	2052-4463
ISSN	2052-4463
DOI	10.1038/s41597-023-02330-9
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Article ; Online: A framework for evaluating correspondence between brain images using anatomical fiducials.

Lau, Jonathan C / Parrent, Andrew G / Demarco, John / Gupta, Geetika / Kai, Jason / Stanley, Olivia W / Kuehn, Tristan / Park, Patrick J / Ferko, Kayla / Khan, Ali R / Peters, Terry M

Human brain mapping

2019 Volume 40, Issue 14, Page(s) 4163–4179

Abstract: Accurate spatial correspondence between template and subject images is a crucial step in neuroimaging studies and clinical applications like stereotactic neurosurgery. In the absence of a robust quantitative approach, we sought to propose and validate a ... ...

Abstract	Accurate spatial correspondence between template and subject images is a crucial step in neuroimaging studies and clinical applications like stereotactic neurosurgery. In the absence of a robust quantitative approach, we sought to propose and validate a set of point landmarks, anatomical fiducials (AFIDs), that could be quickly, accurately, and reliably placed on magnetic resonance images of the human brain. Using several publicly available brain templates and individual participant datasets, novice users could be trained to place a set of 32 AFIDs with millimetric accuracy. Furthermore, the utility of the AFIDs protocol is demonstrated for evaluating subject-to-template and template-to-template registration. Specifically, we found that commonly used voxel overlap metrics were relatively insensitive to focal misregistrations compared to AFID point-based measures. Our entire protocol and study framework leverages open resources and tools, and has been developed with full transparency in mind so that others may freely use, adopt, and modify. This protocol holds value for a broad number of applications including alignment of brain images and teaching neuroanatomy.
MeSH term(s)	Brain/anatomy & histology ; Fiducial Markers ; Humans ; Image Processing, Computer-Assisted/methods ; Magnetic Resonance Imaging/methods ; Neuroimaging/methods
Language	English
Publishing date	2019-06-07
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	1197207-5
ISSN	1097-0193 ; 1065-9471
ISSN (online)	1097-0193
ISSN	1065-9471
DOI	10.1002/hbm.24693
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