Dr Oun Al-Iedani

Background and Purpose: Fingolimod has been shown to be more effective in reducing relapse rate and disability than injectable therapies in clinical trials. An increase in N-acetylaspartate (NAA) as measured by MR spectroscopy is correlated with maintaining axonal metabolic functions. This study compared the neurometabolic and volumetric changes in relapsing-remitting multiple sclerosis (RRMS) patients on fingolimod or injectable therapies with healthy controls (HCs). Methods: Ninety-eight RRMS (52 on fingolimod, 46 on injectable therapies (27 on glatiramer acetate and 19 on interferon) were age and sex-matched to 51 HCs. RRMS patients underwent cognitive, fatigue, and mental health assessments, as well as an Expanded disability status scale (EDSS). MRI/S was acquired from the hippocampus, posterior cingulate gyrus (PCG), and prefrontal cortex (PFC). Volumetric and neurometabolic measures were compared across cohorts using a univariate general linear model and correlated with clinical severity and neuropsychological scores. Results: Clinical parameters, MR-volumetric, and neurometabolic profiles showed no differences between treatment groups (p¿>.05). Compared to HCs, both RRMS cohorts showed volume changes in white matter (-13%), gray matter (-16%), and cerebral spinal fluid (CSF) (+17-23%), as well as reduced NAA (-17%, p =.001, hippocampus), (-7%, p =.001, PCG), and (-9%, p =.001, PFC). MRI/S metrics in three regions were moderately correlated with cognition and fatigue functions. Conclusion: While both treatment arms showed overall similar volumetric and neurometabolic profiles, longitudinal studies are warranted to clarify neurometabolic changes and associations with treatment efficacy.

Purpose: Fatigue is a common symptom in patients with multiple sclerosis (MS) with unknown pathophysiology. Dysfunction of the GABAergic/glutamatergic pathways involving inhibitory and excitatory neurotransmitters such as ¿-aminobutyric acid (GABA) and glutamine + glutamate pool (Glx) have been implicated in several neurological disorders. This study is aimed to evaluate the potential role of GABA and Glx in the origin of central fatigue in relapse remitting MS (RRMS) patients. Methods: 24 RRMS patients and 16 age- and sex-matched healthy controls (HC) were scanned using Mescher-Garwood point resolved spectroscopy (MEGA-PRESS) with a 3 T system to quantify GABA+ and Glx from prefrontal (PFC) and sensorimotor (SMC) cortices. Self-reported fatigue status was measured on all participants using the Modified Fatigue Impact Scale (MFIS). Results: RRMS patients had higher fatigue scores relative to HC (p = 0.05). Compared to HC, Glx levels in RRMS patients were significantly decreased in SMC (p = 0.04). Significant correlations were found between fatigue scores and GABA+ (r = -0.531, p = 0.008) and Glx (r = 0.511, p = 0.018) in PFC. Physical fatigue was negatively correlated with GABA+ in SMC and PFC (r = -0.428 and -0.472 respectively, p = 0.04) and positively with PFC Glx (r = 0.480, p = 0.028). Conclusion: The associations between fatigue and GABA + and Glx suggest that there might be dysregulation of GABAergic/glutamatergic neurotransmission in the pathophysiological mechanism of central fatigue in MS.

Background: Due to the histological heterogeneity of the central gland, accurate detection of central gland prostate cancer remains a challenge. Purpose: To evaluate the efficacy of in vivo 3D 1H MR spectroscopic imaging (3D 1H MRSI) with a semi-localized adiabatic selective refocusing (sLASER) sequence and gradient-modulated offset-independent adiabatic (GOIA) pulses for detection of central gland prostate cancer. Additionally four risk models were developed to differentiate 1) normal vs. cancer, 2) low- vs. high-risk cancer, 3) low- vs. intermediate-risk cancer, and 4) intermediate- vs. high-risk cancer voxels. Study Type: Prospective. Subjects: Thirty-six patients with biopsy-proven central gland prostate cancer. Field Strength/Sequence: 3T MRI / 3D 1H MRSI using GOIA-sLASER. Assessment: Cancer and normal regions of interest (ROIs) were selected by an experienced radiologist and 1H MRSI voxels were placed within the ROIs to calculate seven metabolite signal ratios. Voxels were split into two subsets, 80% for model training and 20% for testing. Statistical Tests: Four support vector machine (SVM) models were built using the training dataset. The accuracy, sensitivity, and specificity for each model were calculated for the testing dataset. Results: High-quality MR spectra were obtained for the whole central gland of the prostate. The normal vs. cancer diagnostic model achieved the highest predictive performance with an accuracy, sensitivity, and specificity of 96.2%, 95.8%, and 93.1%, respectively. The accuracy, sensitivity, and specificity of the low- vs. high-risk cancer and low- vs. intermediate-risk cancer models were 82.5%, 89.2%, 70.2%, and 73.0%, 84.7%, 60.8%, respectively. The intermediate- vs. high-risk cancer model yielded an accuracy, sensitivity, and specificity lower than 55%. Data Conclusion: The GOIA-sLASER sequence with an external phased-array coil allows for fast assessment of central gland prostate cancer. The classification offers a promising diagnostic tool for discriminating normal vs. cancer, low- vs. high-risk cancer, and low- vs. intermediate-risk cancer. Level of Evidence: 2. Technical Efficacy: Stage 2. J. Magn. Reson. Imaging 2019;50:1926¿1936.