Author name cluster

Joseph A. Helpern

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

8 papers

1 author row

YNICL Journal 2019 Journal Article

Brain iron levels in attention-deficit/hyperactivity disorder normalize as a function of psychostimulant treatment duration

Vitria Adisetiyo
Kevin M. Gray
Jens H. Jensen
Joseph A. Helpern

Brain iron homeostasis is a dopamine-related mechanism that may be modified with long-term psychostimulant treatment in attention-deficit/hyperactivity disorder (ADHD). We previously reported that while medication-naïve youth with ADHD have reduced brain iron compared to controls and psychostimulant-medicated patients, no differences were detected between the latter groups. In this follow-up study, we examined whether the duration of psychostimulant treatment correlates with the degree of iron normalization. Brain iron was indexed with MRI using an advanced method called magnetic field correlation (MFC) imaging and the conventional R2* proton transverse relaxation rate method. MFC was acquired in 30 psychostimulant-medicated youth with comorbid-free ADHD and 29 age-matched controls (all males). R2* was acquired in a subset of these individuals. Region-of-interest analyses for MFC and R2* group differences and within-group correlations with age and years of psychostimulant treatment were conducted in the globus pallidus (GP), putamen (PUT), caudate nucleus (CN), thalamus (THL) and red nucleus. No significant MFC and R2* group differences were detected. However, while all regional MFC and R2* significantly increased with age in the control group, MFC and R2* increased in the GP, PUT, CN and THL with psychostimulant treatment duration in the ADHD group to a greater degree than with age. Our findings suggest that while youth with ADHD may have less prominent age-related brain iron increases than that seen in typical development, long-term use of psychostimulant medications may compensate through a normalizing effect on basal ganglia iron. Longitudinal studies following ADHD patients before and after long-term psychostimulant treatment are needed to confirm these findings.

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YNIMG Journal 2019 Journal Article

Optimization of data acquisition and analysis for fiber ball imaging

Hunter G. Moss
Emilie T. McKinnon
G. Russell Glenn
Joseph A. Helpern
Jens H. Jensen

The inverse Funk transform of high angular resolution diffusion imaging (HARDI) data provides an estimate for the fiber orientation density function (fODF) in white matter (WM). Since the inverse Funk transform is a straightforward linear transformation, this technique, referred to as fiber ball imaging (FBI), offers a practical means of calculating the fODF that avoids the need for a response function or nonlinear numerical fitting. Nevertheless, the accuracy of FBI depends on both the choice of b-value and the number of diffusion-encoding directions used to acquire the HARDI data. To inform the design of optimal scan protocols for its implementation, FBI predictions are investigated here with in vivo data from healthy adult volunteers acquired at 3 T for b-values spanning 1000 to 10, 000 s/mm2, for diffusion-encoding directions varying in number from 30 to 256 and for TE ranging from 90 to 120 ms. Our results suggest b-values above 4000 s/mm2 with at least 64 diffusion-encoding directions are adequate to achieve reasonable accuracy with FBI for calculating axon-specific diffusion measures and for performing WM fiber tractography (WMFT).

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YNIMG Journal 2018 Journal Article

Modeling white matter microstructure with fiber ball imaging

Emilie T. McKinnon
Joseph A. Helpern
Jens H. Jensen

Fiber ball imaging (FBI) provides a means of calculating the fiber orientation density function (fODF) in white matter from diffusion MRI (dMRI) data obtained over a spherical shell with a b-value of about 4000 s/mm2 or higher. By supplementing this FBI-derived fODF with dMRI data acquired for two lower b-value shells, it is shown that several microstructural parameters may be estimated, including the axonal water fraction (AWF) and the intrinsic intra-axonal diffusivity. This fiber ball white matter (FBWM) modeling method is demonstrated for dMRI data acquired from healthy volunteers, and the results are compared with those of the white matter tract integrity (WMTI) method. Both the AWF and the intra-axonal diffusivity obtained with FBWM are found to be significantly larger than for WMTI, with the FBWM values for the intra-axonal diffusivity being more consistent with recent results obtained using isotropic diffusion weighting. An important practical advantage of FBWM is that the only nonlinear fitting required is the minimization of a cost function with just a single free parameter, which facilitates the implementation of efficient and robust numerical routines.

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YNIMG Journal 2016 Journal Article

Fiber ball imaging

Jens H. Jensen
G. Russell Glenn
Joseph A. Helpern

By modeling axons as thin cylinders, it is shown that the inverse Funk transform of the diffusion MRI (dMRI) signal intensity obtained on a spherical shell in q-space gives an estimate for a fiber orientation density function (fODF), where the accuracy improves with increasing b-value provided the signal-to-noise ratio is sufficient. The method is similar to q-ball imaging, except that the Funk transform of q-ball imaging is replaced by its inverse. We call this new approach fiber ball imaging. The fiber ball method is demonstrated for healthy human brain, and fODF estimates are compared to diffusion orientation distribution function (dODF) approximations obtained with q-ball imaging. The fODFs are seen to have sharper features than the dODFs, reflecting an enhancement of the higher degree angular frequencies. The inverse Funk transform of the dMRI signal intensity data provides a simple and direct method of estimating a fODF. In addition, fiber ball imaging leads to an estimate for the ratio of the fraction of MRI visible water confined to the intra-axonal space divided by the square root of the intra-axonal diffusivity. This technique may be useful for white matter fiber tractography, as well as other types of microstructural modeling of brain tissue.

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YNIMG Journal 2015 Journal Article

Kurtosis analysis of neural diffusion organization

Edward S. Hui
G. Russell Glenn
Joseph A. Helpern
Jens H. Jensen

A computational framework is presented for relating the kurtosis tensor for water diffusion in brain to tissue models of brain microstructure. The tissue models are assumed to be comprised of non-exchanging compartments that may be associated with various microstructural spaces separated by cell membranes. Within each compartment the water diffusion is regarded as Gaussian, although the diffusion for the full system would typically be non-Gaussian. The model parameters are determined so as to minimize the Frobenius norm of the difference between the measured kurtosis tensor and the model kurtosis tensor. This framework, referred to as kurtosis analysis of neural diffusion organization (KANDO), may be used to help provide a biophysical interpretation to the information provided by the kurtosis tensor. In addition, KANDO combined with diffusional kurtosis imaging can furnish a practical approach for developing candidate biomarkers for neuropathologies that involve alterations in tissue microstructure. KANDO is illustrated for simple tissue models of white and gray matter using data obtained from healthy human subjects.

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YNICL Journal 2014 Journal Article

White matter tract integrity metrics reflect the vulnerability of late-myelinating tracts in Alzheimer's disease

Andreana Benitez
Els Fieremans
Jens H. Jensen
Maria F. Falangola
Ali Tabesh
Steven H. Ferris
Joseph A. Helpern

Post-mortem and imaging studies have observed that white matter (WM) degenerates in a pattern inverse to myelin development, suggesting preferential regional vulnerabilities influencing cognitive decline in AD. This study applied novel WM tract integrity (WMTI) metrics derived from diffusional kurtosis imaging (DKI) to examine WM tissue properties in AD within this framework. Using data from amnestic mild cognitive impairment (aMCI, n = 12), AD (n = 14), and normal control (NC; n = 15) subjects, mixed models revealed interaction effects: specific WMTI metrics of axonal density and myelin integrity (i.e. axonal water fraction, radial extra-axonal diffusivity) in late-myelinating tracts (i.e. superior and inferior longitudinal fasciculi) changed in the course of disease, but were stable in the initial stages for early-myelinating tracts (i.e. posterior limb of the internal capsule, cerebral peduncles). WMTI metrics in late-myelinating tracts correlated with semantic verbal fluency, a cognitive function known to decline in AD. These findings corroborate the preferential vulnerability of late-myelinating tracts, and illustrate an application of WMTI metrics to characterizing the regional course of WM changes in AD.

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YNIMG Journal 2011 Journal Article

White matter characterization with diffusional kurtosis imaging

Els Fieremans
Jens H. Jensen
Joseph A. Helpern

Diffusional kurtosis imaging (DKI) is a clinically feasible extension of diffusion tensor imaging that probes restricted water diffusion in biological tissues using magnetic resonance imaging. Here we provide a physically meaningful interpretation of DKI metrics in white matter regions consisting of more or less parallel aligned fiber bundles by modeling the tissue as two non-exchanging compartments, the intra-axonal space and extra-axonal space. For the b-values typically used in DKI, the diffusion in each compartment is assumed to be anisotropic Gaussian and characterized by a diffusion tensor. The principal parameters of interest for the model include the intra- and extra-axonal diffusion tensors, the axonal water fraction and the tortuosity of the extra-axonal space. A key feature is that these can be determined directly from the diffusion metrics conventionally obtained with DKI. For three healthy young adults, the model parameters are estimated from the DKI metrics and shown to be consistent with literature values. In addition, as a partial validation of this DKI-based approach, we demonstrate good agreement between the DKI-derived axonal water fraction and the slow diffusion water fraction obtained from standard biexponential fitting to high b-value diffusion data. Combining the proposed WM model with DKI provides a convenient method for the clinical assessment of white matter in health and disease and could potentially provide important information on neurodegenerative disorders.

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YNIMG Journal 2009 Journal Article

Does diffusion kurtosis imaging lead to better neural tissue characterization? A rodent brain maturation study

Matthew M. Cheung
Edward S. Hui
Kevin C. Chan
Joseph A. Helpern
Liqun Qi
Ed X. Wu

Diffusion kurtosis imaging (DKI) can be used to estimate excess kurtosis, which is a dimensionless measure for the deviation of water diffusion profile from Gaussian distribution. Several recent studies have applied DKI to probe the restricted water diffusion in biological tissues. The directional analysis has also been developed to obtain the directionally specific kurtosis. However, these studies could not directly evaluate the sensitivity of DKI in detecting subtle neural tissue alterations. Brain maturation is known to involve various biological events that can affect water diffusion properties, thus providing a sensitive platform to evaluate the efficacy of DKI. In this study, in vivo DKI experiments were performed in normal Sprague–Dawley rats of 3 different ages: postnatal days 13, 31 and 120 (N =6 for each group). Regional analysis was then performed for 4 white matter (WM) and 3 gray matter (GM) structures. Diffusivity and kurtosis estimates derived from DKI were shown to be highly sensitive to the developmental changes in these chosen structures. Conventional diffusion tensor imaging (DTI) parameters were also computed using monoexponential model, yielding reduced sensitivity and directional specificity in monitoring the brain maturation changes. These results demonstrated that, by measuring directionally specific diffusivity and kurtosis, DKI offers a more comprehensive and sensitive detection of tissue microstructural changes. Such imaging advance can provide a better MR diffusion characterization of neural tissues, both WM and GM, in normal, developmental and pathological states.

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