Volumetric MRI Measures Research Articles

Fully Automated MRI Segmentation and Volumetric Measurement of Intracranial Meningioma Using Deep Learning.

Accurate and rapid measurement of the MRI volume of meningiomas is essential in clinical practice to determine the growth rate of the tumor. Imperfect automation and disappointing performance for small meningiomas of previous automated volumetric tools limit their use in routine clinical practice. To develop and validate a computational model for fully automated meningioma segmentation and volume measurement on contrast-enhanced MRI scans using deep learning. Retrospective. A total of 659 intracranial meningioma patients (median age, 59.0 years; interquartile range: 53.0-66.0 years) including 554 women and 105 men. The 1.0 T, 1.5T, and 3.0 T; three-dimensional, T1 -weighted gradient-echo imaging with contrast enhancement. The tumors were manually segmented by two neurosurgeons, H.K. and C.-K.P., with 10 and 26 years of clinical experience, respectively, for use as the ground truth. Deep learning models based on U-Net and nnU-Net were trained using 459 subjects and tested for 100 patients from a single institution (internal validation set [IVS]) and 100 patients from other 24 institutions (external validation set [EVS]), respectively. The performance of each model was evaluated with the Sørensen-Dice similarity coefficient (DSC) compared with the ground truth. According to the normality of the data distribution verified by the Shapiro-Wilk test, variables with three or more categories were compared by the Kruskal-Wallis test with Dunn's post hoc analysis. A two-dimensional (2D) nnU-Net showed the highest median DSCs of 0.922 and 0.893 for the IVS and EVS, respectively. The nnU-Nets achieved superior performance in meningioma segmentation than the U-Nets. The DSCs of the 2D nnU-Net for small meningiomas less than 1cm3 were 0.769 and 0.780 with the IVS and EVS, respectively. A fully automated and accurate volumetric measurement tool for meningioma with clinically applicable performance for small meningioma using nnU-Net was developed. 3 TECHNICAL EFFICACY: Stage 2.

Structural Correlates of Overt Sentence Reading in Mild Cognitive Impairment and Mild-to-Moderate Alzheimer's Disease.

Overt sentence reading in mild cognitive impairment (MCI) and mild-tomoderate Alzheimer's disease (AD) has been associated with slowness of speech, characterized by a higher number of pauses, shorter speech units and slower speech rate and attributed to reduced working memory/ attention and language capacity. This preliminary case-control study investigates whether the temporal organization of speech is associated with the volume of brain regions involved in overt sentence reading and explores the discriminative ability of temporal speech parameters and standard volumetric MRI measures for the classification of MCI and AD. Individuals with MCI, mild-to-moderate AD, and healthy controls (HC) had a structural MRI scan and read aloud sentences varying in cognitive-linguistic demand (length). The association between speech features and regional brain volumes was examined by linear mixed-effect modeling. Genetic programming was used to explore the discriminative ability of temporal and MRI features. Longer sentences, slower speech rate, and a higher number of pauses and shorter interpausal units were associated with reduced volumes of the reading network. Speech-based classifiers performed similarly to the MRI-based classifiers for MCI-HC (67% vs. 68%) and slightly better for AD-HC (80% vs. 64%) and AD-MCI (82% vs. 59%). Adding the speech features to the MRI features slightly improved the performance of MRI-based classification for AD-HC and MCI-HC but not HC-MCI. The temporal organization of speech in overt sentence reading reflects underlying volume reductions. It may represent a sensitive marker for early assessment of structural changes and cognitive- linguistic deficits associated with healthy aging, MCI, and AD.

Larger hippocampal and brainstem volumes in high volume lumbar puncture responders as a predictor of shunt response in normal pressure hydrocephalus

AbstractBackgroundNormal pressure hydrocephalus (NPH) is a treatable cause of dementia, urinary incontinence and gait abnormality which is important to diagnose early as the longer the delay in treatment, the worse the outcome after shunting. Gait improvement following high volume lumbar puncture (HVLP) and continuous lumbar drain (cLD) is widely used to predict shunt responsiveness in patients with suspected NPH. Here, we investigate differences in MRI volumetric and traditional measures between HVLP/cLD responders and non‐responders to identify imaging features that may help predict shunt response.Method82 patients with suspected NPH were studied retrospectively. Gait testing was performed before and immediately/24‐hours/72‐hours after CSF removal. A positive response to HVLP/cLD was defined as improvement in gait following the procedure. The 36 responders (26 men; mean‐age 79.3±6.3) and 46 non‐responders (25 men; mean‐age 77.2±6.1) underwent pre‐procedure 3T brain MRI including 3D‐MPRAGE sequence. 66 subcortical regional volumes were segmented using FreeSurfer. After normalizing for total intracranial volume, two‐tailed student's t‐test and chi‐square test were used to characterize statistical group‐differences. Assessment of Evan’s‐index (EI), callosal‐angle (CA) and disproportionately enlarged subarachnoid space hydrocephalus (DESH) were also made. Multivariable logistic regression models were tested using Akaike information criterion to determine which combination of metrics is most accurate in prediction of HVLP/cLD response.ResultResponders and non‐responders demonstrated no differences in total ventricular and white/gray matter volumes and in EI and DESH. CA (in men) and third and fourth ventricular volumes were decreased; and hippocampal and brainstem volumes were increased in responders (p<0.05). Temporal horns volume correlated with degree of improvement in gait velocity in responders (p=0.0006). The regression model was 76.8% accurate for HVLP/cLD response with AUC of 0.81.ConclusionImaging biomarkers, such as CA, third and fourth ventricular volumes and hippocampal and brainstem volumes, combined in a predictive model, may provide a useful noninvasive diagnostic supportive tool potentially helpful in (a) replacing the need for HVLP/cLD, and (b) predicting the success of shunt surgery. Larger hippocampi and brainstem in responders may reflect decreased rate of other comorbidities that contribute to hippocampal and brainstem atrophy, such as possible concomitant pathology of Alzheimer's disease, in responders as compared to non‐responders.

Rethinking modeling Alzheimer's disease progression from a multi-task learning perspective with deep recurrent neural network

Alzheimer's disease (AD) is a severe neurodegenerative disorder that usually starts slowly and progressively worsens. Predicting the progression of Alzheimer's disease with longitudinal analysis on the time series data has recently received increasing attention. However, training an accurate progression model for brain network faces two major challenges: missing features, and the small sample size during the follow-up study. According to our analysis on the AD progression task, we thoroughly analyze the correlation among the multiple predictive tasks of AD progression at multiple time points. Thus, we propose a multi-task learning framework that can adaptively impute missing values and predict future progression over time from a subject's historical measurements. Progression is measured in terms of MRI volumetric measurements, trajectories of a cognitive score and clinical status. To this end, we propose a new perspective of predicting the AD progression with a multi-task learning paradigm. In our multi-task learning paradigm, we hypothesize that the inherent correlations exist among: (i). the prediction tasks of clinical diagnosis, cognition and ventricular volume at each time point; (ii). the tasks of imputation and prediction; and (iii). the prediction tasks at multiple future time points. According to our findings of the task correlation, we develop an end-to-end deep multi-task learning method to jointly improve the performance of assigning missing value and prediction. We design a balanced multi-task dynamic weight optimization. With in-depth analysis and empirical evidence on Alzheimer's Disease Neuroimaging Initiative (ADNI), we show the benefits and flexibility of the proposed multi-task learning model, especially for the prediction at the M60 time point. The proposed approach achieves 5.6%, 5.7%, 4.0% and 11.8% improvement with respect to mAUC, BCA and MAE (ADAS-Cog13 and Ventricles), respectively.

Brain MRI volumetric changes in the follow-up of patients with anti-NMDAr encephalitis

IntroductionAutoimmune anti-NMDAr encephalitis is an antibody-mediated disorder characterized by psychiatric symptoms followed by decreased consciousness, dysautonomia and seizures. The pathophysiology of the disease is related to the internalization of NR1 subtype NMDA receptors and the dysfunction of structures where they are abundant (frontotemporal and insular regions). Some reports suggest the existence of cerebral atrophy in the follow-up of these patients, with conflicting evidence regarding its presence and usefulness as a marker of prognosis. MethodsIn a longitudinal, observational study, all patients with the diagnosis of definite anti-NMDAr autoimmune encephalitis with initial and control MRI studies were included. Conventional MR Brain acquisition was performed using a 3-Tesla Skyra MRI System. Automated brain segmental analysis was performed using the Volbrain volumetry system. The differences between baseline MRI volumetric characteristics and volumetric measures at follow-up was assessed. Results25 patients were included (mean age 26.6, SD 9.6). 44% were females. The mean time between the studies was 24 (SD 21.4, 3–24) months. Significant volume loss was identified in the total brain volume (− 0.02%, p = 0.029), cerebellar volume (− 0.27%, p = 0.048) and brainstem volume (− 0.16%, p = 0.021). ConclusionsThis study supports previous observations regarding volume loss in several brain regions of patients with antiNMDAr encephalitis. Further analyses are required to understand the role of treatment and severe clinical forms, as well as the relationship between volume loss and functional outcome.

Quantitative imaging features predict spinal tap response in normal pressure hydrocephalus.

Gait improvement following high-volume lumbar puncture (HVLP) and continuous lumbar drain (cLD) is widely used to predict shunt response in patients with suspected normal pressure hydrocephalus (NPH). Here, we investigate differences in MRI volumetric and traditional measures between HVLP/cLD responders and non-responders to identify imaging features that may help predict HVLP/cLD response. Eighty-two patients with suspected NPH were studied retrospectively. Gait testing was performed before and immediately/24h/72h after HVLP/cLD. A positive response was defined as improvement in gait post-procedure. Thirty-six responders (26 men; mean age 79.3 ± 6.3) and 46 non-responders (25 men; mean age 77.2 ± 6.1) underwent pre-procedure brain MRI including a 3D T1-weighted sequence. Subcortical regional volumes were segmented using FreeSurfer. After normalizing for total intracranial volume, two-way type III ANCOVA test and chi-square test were used to characterize statistical group differences. Evans' index, callosal angle (CA), and disproportionately enlarged subarachnoid space hydrocephalus were assessed. Multivariable logistic regression models were tested using Akaike information criterion to determine which combination of metrics most accurately predicts HVLP/cLD response. Responders and non-responders demonstrated no differences in total ventricular and white/gray matter volumes. CA (men only) and third and fourth ventricular volumes were smaller; and hippocampal volume was larger in responders (p < 0.05). Temporal horns volume correlated with degree of improvement in gait velocity in responders (p = 0.0006). The regression model was 76.8% accurate for HVLP/cLD response. CA and third and fourth ventricular volumes and hippocampal volume may serve as potentially useful imaging features that may help predict spinal tap response and hence potentially shunt response.

The clinical and paraclinical correlates of employment status in multiple sclerosis.

To identify the clinical and paraclinical markers of employment status in multiple sclerosis (MS). This was a cross-sectional sub-study investigating 1226 MS patients. To minimalized confounding effect, two groups of patients, matched by sex, age, and education, were selected: 307 patients with full time employment and 153 unemployed patients receiving disability pension. We explored associations between employment status and Expanded Disability Status Scale (EDSS), 25 Foot Walk Test (25FWT), Nine Hole Peg Test (9HPT), Brief International Cognitive Assessment for MS (BICAMS), Paced Auditory Serial Addition Test (PASAT), Beck Depression Inventory (BDI), SLOAN charts (SLOAN), and brain volumetric MRI measures. Both groups differed significantly on all variables of interest (p < 0.001). In the univariate analyses, EDSS, SDMT (Symbol Digit Modalities Test) adjusted for BDI, 25FWT, and 9HPT best explained variability in vocational status. In multivariate analyses, the combination of EDSS, 25FWT, SDMT, BDI, and corpus callosum fraction (CCF) explained the greatest variability. As a next step, after patients were matched by EDSS, differences in SDMT, 25FWT (both p < 0.001), 9HPT, CCF, and T2 lesion volume were still present (all p < 0.005) between both groups. The best multivariate model consisted of SDMT, BDI, and T2 lesion volume. EDSS, walking ability, cognitive performance, and MRI volumetric parameters are independently associated with employment status.

Evaluation of Early Cerebral Atrophy in Mild Cognitive Impairment with Positive Amyloid PET using MRIVolumetric Measurement

Objective: To identify early cerebral atrophy by using magnetic resonance imaging (MRI) volumetric measurement to distinguish mild cognitive impairment (MCI) from normal aging. Materials and Methods: A retrospective study in 29 Alzheimer’s disease (AD) patients (mean 75.6±7.3 years), 11 MCI patients (mean 68.8±3.9 years), and 27 healthy control (HC) subjects (mean 69.3±4.7 years) was performed with analysis of neurological and neuropsychiatric test, and underwent 3T MRI with high-resolution 3D-T1W. Quantitative volumetric analysis of brain including surface area, cortical gray matter volume, cortical thickness, and hippocampal subfields was performed by using FreeSurfer software. Results: The diminishment of cortical gray matter volume and cortical thickness were involved in most of brain regions, predominantly in temporal lobe with statistical significance in AD compared with MCI and HC. Comparison between amyloid positron emission tomography (PET) positive MCI subjects and HC has statistically significant difference in most regions of hippocampal subfield. The highest accuracy of 90.01% with sensitivity of 50% and specificity of 100% were found at subiculum. A comparison between amyloid PET positive MCI subjects and amyloid PET negative MCI subjects revealed significant differences at right molecular layer, right/average GC-ML-DG, right CA2/3, right CA4, and average CA4 with good accuracy, sensitivity, and specificity. Conclusion: The present study confirmed improved sensitivity of MRI volumetric measurement with hippocampal subfield analysis to identify early stage of AD in MCI patients, at least compared with positive amyloid PET MCI. Study with higher number of subjects using this method to discriminate MCI and normal aging control would provide benefits as the screening tool in older population. Keywords: Hippocampal subfield; Volumetric analysis; Alzheimer’s disease; Mild cognitive impairment

Partial volume correction analysis for 11C-UCB-J PET studies of Alzheimer's disease

Purpose11C-UCB-J PET imaging, targeting synaptic vesicle glycoprotein 2A (SV2A), has been shown to be a useful indicator of synaptic density in Alzheimer's disease (AD). For SV2A imaging, a decrease in apparent tracer uptake is often due to the combination of gray-matter (GM) atrophy and SV2A decrease in the remaining tissue. Our aim is to reveal the true SV2A change by performing partial volume correction (PVC). MethodsWe performed two PVC algorithms, Müller-Gärtner (MG) and ‘iterative Yang’ (IY), on 17 AD participants and 11 cognitive normal (CN) participants using the brain-dedicated HRRT scanner. Distribution volume VT, the rate constant K1, binding potential BPND (centrum semiovale as reference region), and tissue volume were compared. ResultsIn most regions, both PVC algorithms reduced the between-group differences. Alternatively, in hippocampus, IY increased the significance of between-group differences while MG reduced it (VT, BPND and K1 group differences: uncorrected: 20%, 27%, 17%; MG: 18%, 22%, 14%; IY: 22%, 28%, 17%). The group difference in hippocampal volume (10%) was substantially smaller than any PET measures. MG increased GM binding values to a greater extent than IY due to differences in algorithm assumptions. Conclusion11C-UCB-J binding is significantly reduced in AD hippocampus, but PVC is important to adjust for significant volume reduction. After correction, PET measures are substantially more sensitive to group differences than volumetric MRI measures. Assumptions of each PVC algorithm are important and should be carefully examined and validated. For 11C-UCB-J, the less stringent assumptions of IY support its use as a PVC algorithm over MG.

Trametinib for orbital plexiform neurofibromas in young children with neurofibromatosis type 1.

Plexiform neurofibromas (PNF) in neurofibromatosis type 1 (NF1) are usually diagnosed in childhood and can grow rapidly during this period. In 10% of patients, PNF involve the orbital-periorbital area and may cause visual problems including glaucoma, visual loss from amblyopia (deprivational, strabismic, or refractive), optic nerve compression, or keratopathy. Ptosis, proptosis, and facial disfigurement lead to social problems and decreased self-esteem. Complete surgical removal involves significant risks and mutilation, and regrowth after debulking is not uncommon. Inhibitors of the RAS/MAPK pathway have recently been investigated for their activity in PNF. We administered the oral MEK inhibitor trametinib to five young children with NF1 and PNF of the orbital area, with visual compromise and progressive tumor growth; and followed them clinically and by volumetric MRI. Treatment was initiated at a mean age of 26.8 months (SD ± 12.8) and continued for a median 28 months (range 16-51). Doses were 0.025 mg/kg/day for children aged > 6 years and 0.032 mg/kg/day for those aged < 6 years. Volumetric MRI measurements showed a reduction of 2.9-33% at 1 year after treatment initiation, with maximal reductions of 44% and 49% in two patients, at 44 and 36 months, respectively. No change in visual function was recorded during treatment. One child reported decreased orbital pain after 2 weeks; and another, with involvement of the masseters, had increased ability to chew food. Toxicities were mostly to skin and nails, grades 1-2. Trametinib can decrease tumor size in some young children with orbital PNF and may prevent progressive disfigurement.

Structural changes in brains of patients with disorders of consciousness treated with deep brain stimulation

Disorders of consciousness (DOC) are one of the major consequences after anoxic or traumatic brain injury. So far, several studies have described the regaining of consciousness in DOC patients using deep brain stimulation (DBS). However, these studies often lack detailed data on the structural and functional cerebral changes after such treatment. The aim of this study was to conduct a volumetric analysis of specific cortical and subcortical structures to determine the impact of DBS after functional recovery of DOC patients. Five DOC patients underwent unilateral DBS electrode implantation into the centromedian parafascicular complex of the thalamic intralaminar nuclei. Consciousness recovery was confirmed using the Rappaport Disability Rating and the Coma/Near Coma scale. Brain MRI volumetric measurements were done prior to the procedure, then approximately a year after, and finally 7 years after the implementation of the electrode. The volumetric analysis included changes in regional cortical volumes and thickness, as well as in subcortical structures. Limbic cortices (parahippocampal and cingulate gyrus) and paralimbic cortices (insula) regions showed a significant volume increase and presented a trend of regional cortical thickness increase 1 and 7 years after DBS. The volumes of related subcortical structures, namely the caudate, the hippocampus as well as the amygdala, were significantly increased 1 and 7 years after DBS, while the putamen and nucleus accumbens presented with volume increase. Volume increase after DBS could be a result of direct DBS effects, or a result of functional recovery. Our findings are in accordance with the results of very few human studies connecting DBS and brain volume increase. Which mechanisms are behind the observed brain changes and whether structural changes are caused by consciousness recovery or DBS in patients with DOC is still a matter of debate.

NFB-03. TRAMETINIB FOR ORBITAL PLEXIFORM NEUROFIBROMAS IN YOUNG CHILDREN WITH NF1

Plexiform neurofibromas (PN) in NF1 are diagnosed in early childhood and may grow rapidly during this period. In 10% of patients they involve the orbital-periorbital area and may cause visual problems including glaucoma and visual loss from amblyopia (deprivational, strabismic, or refractive), optic nerve compression or keratopathy. Ptosis, proptosis and facial disfigurement lead to social problems and decreased self-esteem. Complete surgical removal is usually impossible and there is a tendency for regrowth after debulking. Recently inhibitors of the RAS/MAPK pathway have been investigated for their activity in PN. We describe 5 young children with NF1 and PN of the orbital area treated with the MEK inhibitor trametinib followed clinically and by volumetric MRI. Treatment was initiated at mean age 26.8 months (SD ±12.8) and continued for median 25 months (range 17– 48). Reasons for initiating treatment were visual compromise and progressive tumor growth. Doses were as recommended. One child reported decreased orbital pain after one week and another, with involvement of the masseters, had increased ability to chew food. Toxicities were mostly to skin and nails grades 1–2 as expected. Additionally, 60% had debulking surgery of preseptal eyelid tumor in first year of medical treatment. Volumetric MRI measurements showed reduction of 8–26% at 1 year from baseline with a maximal reduction of 45% in two patients at 22 & 45 months. No change in visual function was recorded following treatment initiation. In conclusion, trametinib may decrease tumor size in young children with orbital PN and may prevent progressive disfigurement.

Decision support models for Alzheimer’s disease and mild cognitive impairment classification based on features extracted from MRI and FDG PET

AbstractBackgroundAccurate diagnosis in early Alzheimers’ disease (AD) and Mild Cognitive Impairment (MCI) is critical for patients to start treatments. In AD and MCI patients, there is a high correlation between brain atrophy and metabolism and physical and cognitive impairments. Here we evaluate the performance of machine learning models for identifying AD and MCI subjects based on features extracted from MR and FDG PET imaging.MethodThe ADNI database was used in this study. 201 AD subjects: 80f(58‐88yo), 121m(56‐90yo); 427 MCI subjects: 163f(56‐90yo), 263m(59‐93yo); and 326 NL subjects: 169f(60‐95yo), 157m(66‐91yo) were included. T1‐weighted MRI and matched F18‐FDG PET scans were processed by PETQuant (CorTechs Labs, San Diego, USA). Segmented brain volumes normalized by intracranial volume and corresponding PET ROI SUV ratios (SUVRs) were calculated. Random Forests algorithm was used in creating machine learning models. One‐third of randomly selected data were used for testing while the rest were used for training. The performance of the models and importance of their features were evaluated.ResultThree models’ performances were evaluated. The first one used both PET SUVRs and MRI‐based volumes, and average model accuracy, recall for identifying AD, MCI and normal were 0.77 and 0.77. The top three features were right isthmus cingulate SUVR, left amygdala volume, and left hippocampus volume. The second model only used MRI, and average accuracy and recall for identifying AD,MCI and normal were 0.77 and 0.76. The top three features were left amygdala volume, left hippocampus volume, and right inferior temporal volume. The third model used PET SUVRs only, and average accuracy and recall were 0.72 and 0.72. The top three features were SUVRs from right isthmus cingulate, left medial‐parietal, and right entorhinal cortex.ConclusionThese models showed a good classification performance among AD, MCI and normal subjects. The model using MRI and PET showed the best performance and was slightly better than using MRI data alone. The MRI model performed better than using the PET data alone. This suggests MRI volumetric measures provide a better classification power than SUVRs extracted from FDG‐PET images.

Assessing the relationship between cognitive dysfunction and brain atrophy in Alzheimer's disease

AbstractBackgroundBrain atrophy can be reliably measured through volumetric analysis of structural MRI and is a valid biomarker of neurodegeneration in Alzheimer’s disease (AD) pathology1. The Alzheimer’s Disease Assessment Scale‐Cognitive subscale (ADAS‐Cog12) is a neuropsychological test designed to assess the level of cognitive dysfunction in AD within cognitive domains such as memory2. This study aimed to assess the relationship between brain atrophy and ADAS‐Cog12 score in patients with AD.Methods172 patients with mild‐to‐moderate Alzheimer’s disease (Age range 51‐58, Mean=71, SD=+/‐8) underwent a T1‐weighted MPRAGE MRI scan and neuropsychological testing using the ADAS‐Cog12. Volumetric MRI measures extracted using Freesurfer for the hippocampus, medial temporal lobe, temporal lobe and total grey matter were individually correlated with each patient’s ADAS‐Cog12 score using Pearson’s correlation coefficient following removal of outliers in each data set (+/‐ 2SD from the mean).ResultsIncreasing ADAS‐Cog12 score was significantly correlated with decreasing volume of the hippocampus (r=‐0.280, p&lt;0.001), medial temporal lobe (r=‐0.246, p=0.001), temporal lobe (r=‐0.300, p&lt;0.001) and total grey matter (r=‐0.236, p=0.002).ConclusionsThis study demonstrated that the ADAS‐Cog12 correlated with AD‐related regional brain atrophy, suggesting that ADAS‐Cog could be used as an outcome measure in AD trials and reflects underlying neuronal damage along with cognitive function.

Injury volume extracted from MRI predicts neurologic outcome in acute spinal cord injury: A prospective TRACK-SCI pilot study

Conventional MRI measures of traumatic spinal cord injury severity largely rely on 2-dimensional injury characteristics such as intramedullary lesion length and cord compression. Recent advances in spinal cord (SC) analysis have led to the development of a robust anatomic atlas incorporated into an open-source platform called the Spinal Cord Toolbox (SCT) that allows for quantitative volumetric injury analysis. In the current study, we evaluate the prognostic value of volumetric measures of spinal cord injury on MRI following registration of T2-weighted (T2w) images and segmented lesions from acute SCI patients with a standardized atlas. This IRB-approved prospective cohort study involved the image analysis of 60 blunt cervical SCI patients enrolled in the TRACK-SCI clinical research protocol. Axial T2w MRI data obtained within 24 h of injury were processed using the SCT. Briefly, SC MRIs were automatically segmented using the sct_deepseg_sc tool in the SCT and segmentations were manually corrected by a neuro-radiologist. Lesion volume data were used as predictor variables for correlation with lower extremity motor scores at discharge. Volumetric MRI measures of T2w signal abnormality comprising the SCI lesion accurately predict lower extremity motor scores at time of patient discharge. Similarly, MRI measures of injury volume significantly correlated with motor scores to a greater degree than conventional 2-D metrics of lesion size. The volume of total injury and of injured spinal cord motor regions on T2w MRI is significantly and independently associated with neurologic outcome at discharge after injury.

Assessment of Racial/Ethnic Disparities in Volumetric MRI Correlates of Clinical Disability in Multiple Sclerosis: A Preliminary Study.

Although global and regional brain volume has been established as a relevant measure to define and predict multiple sclerosis (MS) severity, characterization of specific trends by race/ethnicity is currently lacking. We aim to (1) characterize racial disparities in disability-specific patterns of brain MRI volumetric measures between Hispanic and Caucasian individuals with MS and (2) explore the relevance of these measures as predictors of clinical disability progression. Brain MRI scans from 94 Hispanic and 94 age- and gender-matched Caucasian MS patients were analyzed using automatic and manual segmentation techniques. Select global and regional volume measures were correlated to Expanded Disability Status Scale (EDSS) scores at baseline and subsequent follow-up visits. Hispanic patients had a higher baseline median EDSS score (interquartile range [IQR], 2.0; [1.0-3.5]) compared to Caucasians (median [IQR], 1.0 [.0-2.0]) and an increased risk of requiring ambulatory assistance (hazard ratio [HR], 9.7; 95% confidence interval [CI], 2.8-32.5). Normalized thalamic volume was moderately associated with EDSS scores (rs = -.42, P < .001 in Hispanics;rs = -.32,P = .002 in Caucasians) and was the best predictor of sustained disability worsening in both racial groups in a time-to-event analysis. The confounding impact of race on quantitative brain volume measures may affect the interpretation of outcome measures in MS clinical trials.

Can active life mitigate the impact of diabetes on dementia and brain aging?

We investigated whether lifelong exposure to stimulating activities (active life, AL) mitigates diabetes-associated dementia risk and brain aging. In the Swedish National Study on Aging and Care-Kungsholmen, 2286 dementia-free older adults (407 with MRI volumetric measures) were followed over 12 years to detect incident dementia. AL index (low, moderate, high) combined education, work complexity, leisure activities, and social network. Participants with diabetes and low AL had higher dementia risk (hazard ratio [HR]=2.36, 95% confidence interval [CI] 1.45-3.87) than patients who were diabetes-free with moderate-to-high AL (reference). Dementia risk in participants with diabetes and moderate-to-high AL did not differ from the reference. People with diabetes and low AL had the smallest brain volume, but those with diabetes and moderate-to-high AL exhibited total brain and gray-matter volumes that were similar to those of diabetes-free participants. AL did not modify the diabetes microvascular lesions association. AL could mitigate the deleterious impact of diabetes on dementia, potentially by limiting the loss of brain tissue volume.

Brainstem and striatal volume changes are detectable in under 1 year and predict motor decline in spinocerebellar ataxia type 1.

Spinocerebellar ataxia type 1 is a progressive neurodegenerative, movement disorder. With potential therapies on the horizon, it is critical to identify biomarkers that (i) differentiate between unaffected and spinocerebellar ataxia Type 1-affected individuals; (ii) track disease progression; and (iii) are directly related to clinical changes of the patient. Magnetic resonance imaging of volumetric changes in the brain may be a suitable source of biomarkers for spinocerebellar ataxia Type 1. In a previous report on a longitudinal study of patients with spinocerebellar ataxia Type 1, we evaluated the volume and magnetic resonance spectroscopy measures of the cerebellum and pons, showing pontine volume and pontine N-acetylaspartate-to-myo-inositol ratio were sensitive to change over time. As a follow-up, the current study conducts a whole brain exploration of volumetric MRI measures with the aim to identify biomarkers for spinocerebellar ataxia Type 1 progression. We adapted a joint label fusion approach using multiple, automatically generated, morphologically matched atlases to label brain regions including cerebellar sub-regions. We adjusted regional volumes by total intracranial volume allowing for linear and power-law relationships. We then utilized Bonferroni corrected linear mixed effects models to (i) determine group differences in regional brain volume and (ii) identify change within affected patients only. We then evaluated the rate of change within each brain region to identify areas that changed most rapidly. Lastly, we used a penalized, linear mixed effects model to determine the strongest brain predictors of motor outcomes. Decrease in pontine volume and accelerating decrease in putamen volume: (i) reliably differentiated spinocerebellar ataxia Type 1-affected and -unaffected individuals; (ii) were observable in affected individuals without referencing an unaffected comparison group; (iii) were detectable within ∼6–9 months; and (iv) were associated with increased disease burden. In conclusion, volumetric change in the pons and putamen may provide powerful biomarkers to track disease progression in spinocerebellar ataxia Type 1. The methods employed here are readily translatable to current clinical settings, providing a framework for study and usage of volumetric neuroimaging biomarkers for clinical trials.

Usefulness of Mitral Regurgitant Volume Quantified Using Magnetic Resonance Imaging to Predict Left Ventricular Remodeling After Mitral Valve “Correction”

MRI studies have shown a tight correlation between mitral regurgitant volume and left ventricular end-diastolic volume (LV EDV) in patients with primary chronic mitral regurgitation (MR). They have also shown a tight correlation between regurgitant volume and the decrease in LVEDV following mitral valve surgery. The purpose of this study is to validate an empiric calculation that can be used preoperatively to predict the amount of left ventricular remodeling following mitral valve correction. This is a prospective multicenter study of 63 (61 ± 13 years, male 65%) patients who underwent an MRI before and after mitral valve correction. Pre and postmitral valve correction ventricular volumes and ejection fractions were quantified. The predicted change in LV EDV was empirically calculated as mitral regurgitant volume/left ventricular ejection fraction. The observed change in LV EDV was compared to the predicted change in LV EDV. The LVEDV decreased in 61 (97%) patients following mitral valve correction (237 ± 66 ml vs 164 ± 46 ml, p <0.0001). Correlation between the observed and predicted change in LVEDV was good for the entire cohort (r = 0.77, p <0.0001) and excellent in patients with <10 ml of residual MR (r = 0.87, p <0.0001). This tight correlation was seen in both patients with primary (0.86, p <0.0001) and secondary MR (0.97, p <0.0001) and <10 ml of residual MR. Multivariate predictors of LV remodeling were MR volume, primary MR, and LVESV. In conclusion cardiac MRI volumetric measurements accurately predict LV remodeling following mitral valve correction. This finding supports the notion that MRI accurately quantifies the severity of chronic mitral regurgitation and a cardiac MRI should be strongly considered before mitral valve correction.

The Association Between Distinct Frontal Brain Volumes and Behavioral Symptoms in Mild Cognitive Impairment, Alzheimer's Disease, and Frontotemporal Dementia.

Our aim was to investigate the association between behavioral symptoms of agitation, disinhibition, irritability, elation, and aberrant motor behavior to frontal brain volumes in a cohort with various neurodegenerative diseases. A total of 121 patients with mild cognitive impairment (MCI, n = 58), Alzheimer's disease (AD, n = 45) and behavioral variant frontotemporal dementia (bvFTD, n = 18) were evaluated with a Neuropsychiatric Inventory (NPI). A T1-weighted MRI scan was acquired for each participant and quantified with a multi-atlas segmentation method. The volumetric MRI measures of the frontal lobes were associated with neuropsychiatric symptom scores with a linear model. In the regression model, we included CDR score and TMT B time as covariates to account for cognitive and executive functions. The brain volumes were corrected for age, gender and head size. The total behavioral symptom score of the five symptoms of interest was negatively associated with the volume of the subcallosal area (β = −0.32, p = 0.002). High disinhibition scores were associated with reduced volume in the gyrus rectus (β = −0.30, p = 0.002), medial frontal cortex (β = −0.30, p = 0.002), superior frontal gyrus (β = −0.28, p = 0.003), inferior frontal gyrus (β = −0.28, p = 0.005) and subcallosal area (β = −0.28, p = 0.005). Elation scores were associated with reduced volumes of the medial orbital gyrus (β = −0.30, p = 0.002) and inferior frontal gyrus (β = −0.28, p = 0.004). Aberrant motor behavior was associated with atrophy of frontal pole (β = −0.29, p = 0.005) and the subcallosal area (β = −0.39, p < 0.001). No significant associations with frontal brain volumes were found for agitation and irritability. We conclude that the subcallosal area may be common neuroanatomical area for behavioral symptoms in neurodegenerative diseases, and it appears to be independent of disease etiology.