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Benoit H. Mulsant

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.

3 papers

1 author row

JBHI Journal 2024 Journal Article

A Novel Unsupervised Machine Learning Approach to Assess Postural Dynamics in Euthymic Bipolar Disorder

Ramzi Halabi
Christina Gonzalez-Torres
Stephane MacLean
M. Ishrat Husain
Abhishek Pratap
Martin Alda
Benoit H. Mulsant
Abigail Ortiz

Bipolar disorder (BD) is a mood disorder with different phases alternating between euthymia, manic or hypomanic episodes, and depressive episodes. While motor abnormalities are commonly seen during depressive or manic episodes, not much attention has been paid to postural abnormalities during periods of euthymia and their association with illness burden. We collected 24-hour posture data in 32 euthymic participants diagnosed with BD using a shirt-based wearable. We extracted a set of nine time-domain features, and performed unsupervised participant clustering. We investigated the association between posture variables and 12 clinical characteristics of illness burden. Based on their postural dynamics during the daytime, evening, or nighttime, participants clustered in three clusters. Higher illness burden was associated with lower postural variability, in particular during daytime. Participants who exhibited a mostly upright sitting/standing posture during the night with frequent nighttime postural transitions had the highest number of lifetime depressive episodes. Euthymic participants with BD exhibit postural abnormalities that are associated with illness burden, especially with the number of depressive episodes. Our results contribute to understanding the role of illness burden on posture changes and sleep consolidation in periods of euthymia.

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YNICL Journal 2019 Journal Article

Low-dose augmentation with buprenorphine increases emotional reactivity but not reward activity in treatment resistant mid- and late-life depression

Chemin Lin
Helmet T. Karim
Marta Pecina
Howard J. Aizenstein
Eric J. Lenze
Daniel M. Blumberger
Benoit H. Mulsant
Evan D. Kharasch

Buprenorphine is currently being studied for treatment-resistant depression because of its rapid effect, relative safety, and unique pharmacodynamics. To understand the neural impact of buprenorphine in depression, we examined acute limbic and reward circuit changes during an intervention with low-dose buprenorphine augmentation pharmacotherapy. Mid and late-life adults with major depression (N = 31) who did not completely respond to an adequate trial of venlafaxine were randomized to augmentation with low-dose buprenorphine or matching placebo. We investigated early neural changes using functional magnetic resonance imaging (fMRI) from pre-randomization to 3 weeks using both an emotional reactivity task and a gambling task. We tested if: 1) there were significant neural changes acutely per intervention group, and 2) if acute neural changes were associated with depressive symptom change over 8 weeks using both the total score and the dysphoria subscale of the Montgomery Asberg Depression Rating Scale. Participants in both the buprenorphine and placebo groups showed similar changes in depressive symptoms. Neither the emotional reactivity nor gambling task resulted in significant neural activation changes from pre-randomization to 3-weeks. In both groups, increases in rostral anterior cingulate (rACC) and ventromedial prefrontal cortex (vmPFC) activation during the emotional reactivity task were associated with overall symptom improvement. In the buprenorphine but not the placebo group, increased activation in left anterior insula (aINS) and bilateral middle frontal gyrus (MFG) was associated with improvement on the dysphoria subscale. Activation changes in the reward task were not associated with buprenorphine. This is the first study to show an association between acute neural changes during emotion reactivity and changes in depression severity with buprenorphine treatment.

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YNIMG Journal 2009 Journal Article

Quantitative examination of a novel clustering method using magnetic resonance diffusion tensor tractography

Aristotle N. Voineskos
Lauren J. O'Donnell
Nancy J. Lobaugh
Doug Markant
Stephanie H. Ameis
Marc Niethammer
Benoit H. Mulsant
Bruce G. Pollock

MR diffusion tensor imaging (DTI) can measure and visualize organization of white matter fibre tracts in vivo. DTI is a relatively new imaging technique, and new tools developed for quantifying fibre tracts require evaluation. The purpose of this study was to compare the reliability of a novel clustering approach with a multiple region of interest (MROI) approach in both healthy and disease (schizophrenia) populations. DTI images were acquired in 20 participants (n =10 patients with schizophrenia: 56±15 years; n =10 controls: 51±20 years) (1. 5 T GE system) with diffusion gradients applied in 23 non-collinear directions, repeated three times. Whole brain seeding and creation of fibre tracts were then performed. Interrater reliability of the clustering approach, and the MROI approach, were each evaluated and the methods compared. There was high spatial (voxel-based) agreement within and between the clustering and MROI methods. Fractional anisotropy, trace, and radial and axial diffusivity values showed high intraclass correlation (p <0. 001 for all tracts) for each approach. Differences in scalar indices of diffusion between the clustering and MROI approach were minimal. The excellent interrater reliability of the clustering method and high agreement with the MROI method, quantitatively and spatially, indicates that the clustering method can be used with confidence. The clustering method avoids biases of ROI drawing and placement, and, not limited by a priori predictions, may be a more robust and efficient way to identify and measure white matter tracts of interest.

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