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Mahsa Dadar

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20 papers

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YNIMG Journal 2022 Journal Article

DARQ: Deep learning of quality control for stereotaxic registration of human brain MRI to the T1w MNI-ICBM 152 template

Vladimir S. Fonov
Mahsa Dadar
The PREVENT-AD Research Group ADNI
D. Louis Collins

Linear registration to stereotaxic space is a common first step in many automated image-processing tools for analysis of human brain MRI scans. This step is crucial for the success of the subsequent image-processing steps. Several well-established algorithms are commonly used in the field of neuroimaging for this task, but none have a 100% success rate. Manual assessment of the registration is commonly used as part of quality control. To reduce the burden of this time-consuming step, we propose Deep Automated Registration Qc (DARQ), a fully automatic quality control method based on deep learning that can replace the human rater and accurately perform quality control assessment for stereotaxic registration of T1w brain scans. In a recently published study from our group comparing linear registration methods, we used a database of 9325 MRI scans and 64476 registrations from several publicly available datasets and applied seven linear registration tools to them. In this study, the resulting images that were assessed and labeled by a human rater are used to train a deep neural network to detect cases when registration failed. We further validated the results on an independent dataset of patients with multiple sclerosis, with manual QC labels available (n=1200). In terms of agreement with a manual rater, our automated QC method was able to achieve 89% accuracy and 85% true negative rate (equivalently 15% false positive rate) in detecting scans that should pass quality control in a balanced cross-validation experiments, and 96.1% accuracy and 95.5% true negative rate (or 4.5% FPR) when evaluated in a balanced independent sample, similar to manual QC rater (test-retest accuracy of 93%). The results show that DARQ is robust, fast, accurate, and generalizable in detecting failure in linear stereotaxic registrations and can substantially reduce QC time (by a factor of 20 or more) when processing large datasets.

YNIMG Journal 2022 Journal Article

Impact of weight loss on brain age: Improved brain health following bariatric surgery

Yashar Zeighami
Mahsa Dadar
Justine Daoust
Mélissa Pelletier
Laurent Biertho
Léonie Bouvet-Bouchard
Stephanie Fulton
André Tchernof

Individuals living with obesity tend to have increased brain age, reflecting poorer brain health likely due to grey and white matter atrophy related to obesity. However, it is unclear if older brain age associated with obesity can be reversed following weight loss and cardiometabolic health improvement. The aim of this study was to assess the impact of weight loss and cardiometabolic improvement following bariatric surgery on brain health, as measured by change in brain age estimated based on voxel-based morphometry (VBM) measurements. We used three distinct datasets to perform this study: 1) CamCAN dataset to train the brain age prediction model, 2) Human Connectome Project (HCP) dataset to investigate whether individuals with obesity have greater brain age than individuals with normal weight, and 3) pre-surgery, as well as 4, 12, and 24 month post-surgery data from participants (n = 87, age: 44. 0 ± 9. 2 years, BMI: 43. 9 ± 4. 2 kg/m2) who underwent a bariatric surgery to investigate whether weight loss and cardiometabolic improvement as a result of bariatric surgery lowers the brain age. As expected, our results from the HCP dataset showed a higher brain age for individuals with obesity compared to individuals with normal weight (T-value = 7. 08, p-value < 0. 0001). We also found significant improvement in brain health, indicated by a decrease of 2. 9 and 5. 6 years in adjusted delta age at 12 and 24 months following bariatric surgery compared to baseline (p-value < 0. 0005 for both). While the overall effect seemed to be driven by a global change across all brain regions and not from a specific region, our exploratory analysis showed lower delta age in certain brain regions (mainly in somatomotor, visual, and ventral attention networks) at 24 months. This reduced age was also associated with post-surgery improvements in BMI, systolic/diastolic blood pressure, and HOMA-IR (T-valueBMI=4. 29, T-valueSBP=4. 67, T-valueDBP=4. 12, T-valueHOMA-IR=3. 16, all p-values < 0. 05). In conclusion, these results suggest that obesity-related brain health abnormalities (as measured by delta age) might be reversed by bariatric surgery-induced weight loss and widespread improvements in cardiometabolic alterations.

YNICL Journal 2022 Journal Article

Regional brain atrophy and cognitive decline depend on definition of subjective cognitive decline

Cassandra Morrison
Mahsa Dadar
Neda Shafiee
Sylvia Villeneuve
D. Louis Collins

BACKGROUND: People with subjective cognitive decline (SCD) may be at increased risk for Alzheimer's disease (AD). However, not all studies have observed this increased risk. This project examined whether four common methods of defining SCD yields different patterns of atrophy and future cognitive decline between cognitively normal older adults with (SCD+ ) and without SCD (SCD-). METHODS: Data from 273 Alzheimer's Disease Neuroimaging Initiative cognitively normal older adults were examined. To operationalize SCD we used four common methods: Cognitive Change Index (CCI), Everyday Cognition Scale (ECog), ECog + Worry, and Worry. Voxel-based logistic regressions were applied to deformation-based morphology results to determine if regional atrophy between SCD- and SCD+ differed by SCD definition. Linear mixed-effects models were used to evaluate differences in future cognitive decline. RESULTS: Results varied between the four methods of defining SCD. Left hippocampal grading was more similar to AD in SCD+ than SCD- when using the CCI (p = .041) and Worry (p = .021) definitions. The right (p=.008) and left (p=.003) superior temporal regions had smaller volumes in SCD+ than SCD-, but only with the ECog. SCD+ was associated with greater future cognitive decline measured by Alzheimer's Disease Assessment Scale, but only with the CCI definition. In contrast, only the ECog definition of SCD was associated with future decline on the Montreal Cognitive Assessment. CONCLUSION: These findings suggest that the various methods used to differentiate between SCD- and SCD+ influence whether volume differences and findings of cognitive decline are observed between groups in this retrospective analysis.

YNICL Journal 2022 Journal Article

Ventricular features as reliable differentiators between bvFTD and other dementias

Ana L. Manera
Mahsa Dadar
D. Louis Collins
Simon Ducharme

INTRODUCTION: Lateral ventricles are reliable and sensitive indicators of brain atrophy and disease progression in behavioral variant frontotemporal dementia (bvFTD). We aimed to investigate whether an automated tool using ventricular features could improve diagnostic accuracy in bvFTD across neurodegenerative diseases. METHODS: Using 678 subjects -69 bvFTD, 38 semantic variant, 37 primary non-fluent aphasia, 218 amyloid + mild cognitive impairment, 74 amyloid + Alzheimer's Dementia and 242 normal controls- with a total of 2750 timepoints, lateral ventricles were segmented and differences in ventricular features were assessed between bvFTD, normal controls and other dementia cohorts. RESULTS: Ventricular antero-posterior ratio (APR) was the only feature that was significantly different and increased faster in bvFTD compared to all other cohorts. We achieved a 10-fold cross-validation accuracy of 80% (77% sensitivity, 82% specificity) in differentiating bvFTD from all other cohorts with other ventricular features (i.e., total ventricular volume and left-right lateral ventricle ratios), and 76% accuracy using only the single APR feature. DISCUSSION: Ventricular features, particularly the APR, might be reliable and easy-to-implement markers for bvFTD diagnosis. We have made our ventricle feature estimation and bvFTD diagnostic tool publicly available, allowing application of our model in other studies.

YNICL Journal 2022 Journal Article

White matter hyperintensity distribution differences in aging and neurodegenerative disease cohorts

Mahsa Dadar
Sawsan Mahmoud
Maryna Zhernovaia
Richard Camicioli
Josefina Maranzano
Simon Duchesne

INTRODUCTION: White matter hyperintensities (WMHs) are common magnetic resonance imaging (MRI) findings in the aging population in general, as well as in patients with neurodegenerative diseases. They are known to exacerbate the cognitive deficits and worsen the clinical outcomes in the patients. However, it is not well-understood whether there are disease-specific differences in prevalence and distribution of WMHs in different neurodegenerative disorders. METHODS: Data included 976 participants with cross-sectional T1-weighted and fluid attenuated inversion recovery (FLAIR) MRIs from the Comprehensive Assessment of Neurodegeneration and Dementia (COMPASS-ND) cohort of the Canadian Consortium on Neurodegeneration in Aging (CCNA) with eleven distinct diagnostic groups: cognitively intact elderly (CIE), subjective cognitive impairment (SCI), mild cognitive impairment (MCI), vascular MCI (V-MCI), Alzheimer's dementia (AD), vascular AD (V-AD), frontotemporal dementia (FTD), Lewy body dementia (LBD), cognitively intact elderly with Parkinson's disease (PD-CIE), cognitively impaired Parkinson's disease (PD-CI), and mixed dementias. WMHs were segmented using a previously validated automated technique. WMH volumes in each lobe and hemisphere were compared against matched CIE individuals, as well as each other, and between men and women. RESULTS: All cognitively impaired diagnostic groups had significantly greater overall WMH volumes than the CIE group. Vascular groups (i.e. V-MCI, V-AD, and mixed dementia) had significantly greater WMH volumes than all other groups, except for FTD, which also had significantly greater WMH volumes than all non-vascular groups. Women tended to have lower WMH burden than men in most groups and regions, controlling for age. The left frontal lobe tended to have a lower WMH burden than the right in all groups. In contrast, the right occipital lobe tended to have greater WMH volumes than the left. CONCLUSIONS: There were distinct differences in WMH prevalence and distribution across diagnostic groups, sexes, and in terms of asymmetry. WMH burden was significantly greater in all neurodegenerative dementia groups, likely encompassing areas exclusively impacted by neurodegeneration as well as areas related to cerebrovascular disease pathology.

YNICL Journal 2022 Journal Article

White matter lesions may be an early marker for age-related cognitive decline

Cassandra Morrison
Mahsa Dadar
Sylvia Villeneuve
D. Louis Collins

BACKGROUND: Research suggests that cerebral small vessel disease (CSVD), amyloid, and pTau contribute to age-related cognitive decline. It remains unknown how these factors relate to one another and how they jointly contribute to cognitive decline in normal aging. This project examines the association between these factors and their relationship to cognitive decline in cognitively unimpaired older adults without subjective cognitive decline. METHODS: A total of 230 subjects with cerebrospinal fluid (CSF) Aß42, CSF pTau181, white matter lesions (WMLs) used as a proxy of CSVD, and cognitive scores from the Alzheimer's Disease Neuroimaging Initiative were included. Associations between each factor and cognitive score were investigated using regression models. Furthermore, relationships between the three pathologies were also examined using regression models. RESULTS: At baseline, there was an inverse association between WML load and Aß42 (t = -4.20, p <.001). There was no association between WML load and pTau (t = 0.32, p = 0.75), nor with Aß42 and pTau (t = 0.51, p =.61). Correcting for age, sex and education, baseline WML load was associated with baseline ADAS-13 scores (t = 2.59, p =.01) and lower follow-up executive functioning (t = -2.84, p =.005). Baseline Aß42 was associated with executive function at baseline (t = 3.58, p<.004) but not at follow-up (t = 1.05, p = 0.30), nor with ADAS-13 at baseline (t = -0.24, p = 0.81) or follow-up (t = 0.09, p = 0.93). Finally, baseline pTau was not associated with any cognitive measure at baseline or follow-up. CONCLUSION: Both baseline Aß42 and WML load are associated with some baseline cognition scores, but only baseline WML load is associated with follow-up executive functioning. This finding suggests that WMLs may be one of the earliest clinical manifestations that contributes to future cognitive decline in cognitively healthy older adults. Given that healthy older adults with WMLs exhibit declines in cognitive functioning, they may be less resilient to future pathology increasing their risk for cognitive impairment due to dementia than those without WMLs.

YNIMG Journal 2021 Journal Article

Spontaneous neural activity changes after bariatric surgery: A resting-state fMRI study

Yashar Zeighami
Sylvain Iceta
Mahsa Dadar
Mélissa Pelletier
Mélanie Nadeau
Laurent Biertho
Annie Lafortune
André Tchernof

BACKGROUND: Metabolic disorders associated with obesity could lead to alterations in brain structure and function. Whether these changes can be reversed after weight loss is unclear. Bariatric surgery provides a unique opportunity to address these questions because it induces marked weight loss and metabolic improvements which in turn may impact the brain in a longitudinal fashion. Previous studies found widespread changes in grey matter (GM) and white matter (WM) after bariatric surgery. However, findings regarding changes in spontaneous neural activity following surgery, as assessed with the fractional amplitude of low frequency fluctuations (fALFF) and regional homogeneity of neural activity (ReHo), are scarce and heterogenous. In this study, we used a longitudinal design to examine the changes in spontaneous neural activity after bariatric surgery (comparing pre- to post-surgery), and to determine whether these changes are related to cardiometabolic variables. METHODS: ) who underwent sleeve gastrectomy (SG), biliopancreatic diversion with duodenal switch (BPD), or Roux-en-Y gastric bypass (RYGB), scanned prior to bariatric surgery and at follow-up visits of 4 months (N = 36), 12 months (N = 29), and 24 months (N = 14) after surgery. We examined fALFF and ReHo measures across 1022 cortical and subcortical regions (based on combined Schaeffer-Xiao parcellations) using a linear mixed effect model. Voxel-based morphometry (VBM) based on T1-weighted images was also used to measure GM density in the same regions. We also used an independent sample from the Human Connectome Project (HCP) to assess regional differences between individuals who had normal-weight (N = 46) or severe obesity (N = 46). RESULTS: We found a global increase in the fALFF signal with greater increase within dorsolateral prefrontal cortex, precuneus, inferior temporal gyrus, and visual cortex. This effect was more significant 4 months after surgery. The increase within dorsolateral prefrontal cortex, temporal gyrus, and visual cortex was more limited after 12 months and only present in the visual cortex after 24 months. These increases in neural activity measured by fALFF were also significantly associated with the increase in GM density following surgery. Furthermore, the increase in neural activity was significantly related to post-surgery weight loss and improvement in cardiometabolic variables, such as blood pressure. In the independent HCP sample, normal-weight participants had higher global and regional fALFF signals, mainly in dorsolateral/medial frontal cortex, precuneus and middle/inferior temporal gyrus compared to the obese participants. These BMI-related differences in fALFF were associated with the increase in fALFF 4 months post-surgery especially in regions involved in control, default mode and dorsal attention networks. CONCLUSIONS: Bariatric surgery-induced weight loss and improvement in metabolic factors are associated with widespread global and regional increases in neural activity, as measured by fALFF signal. These findings alongside the higher fALFF signal in normal-weight participants compared to participants with severe obesity in an independent dataset suggest an early recovery in the neural activity signal level after the surgery.

YNIMG Journal 2020 Journal Article

Automated separation of diffusely abnormal white matter from focal white matter lesions on MRI in multiple sclerosis

Josefina Maranzano
Mahsa Dadar
Maryna Zhernovaia
Douglas L. Arnold
D. Louis Collins
Sridar Narayanan

BACKGROUND: Previous histopathology and MRI studies have addressed the differences between focal white matter lesions (FWML) and diffusely abnormal white matter (DAWM) in multiple sclerosis (MS). These two categories of white matter T2-weighted (T2w) hyperintensity show different degrees of demyelination, axonal loss and immune cell density on histopathology, potentially offering distinct correlations with symptoms. OBJECTIVES: 1) To automate the separation of FWML and DAWM using T2w MRI intensity thresholds and to investigate their differences in magnetization transfer ratios (MTR), which are sensitive to myelin content; 2) to correlate MTR values in FWML and DAWM with normalized signal intensity values on fluid attenuated inversion recovery (FLAIR), T2w, and T1-weighted (T1w) contrasts, as well as with the ratio of T2w/T1w normalized values, in order to determine whether these normalized intensities can be used when MTR is not available. METHODS: We used three MRI datasets: datasets 1 and 2 had 20 MS participants each, scanned with similar 3T MRI protocols in 2 centers, including: 3D T1w (MP2RAGE), 3D FLAIR, 2D T2w, and 3D magnetization-transfer (MT) contrasts. Dataset 3 consisted of 67 scans of participants enrolled in a multisite study and had T1w and T2w contrasts. We used the first dataset to develop an automated technique to separate FWML from DAWM and the second and third to validate the automation of the technique. We applied the automatic thresholds to all datasets to assess the overlap of the manual and the automated masks using Dice kappa. We also assessed differences in mean MTR values between NAWM, DAWM and FWML, using manually and automatically derived masks in datasets 1 and 2. Finally, we used the mean intensity of manually-traced areas of NAWM on T2w images as the normalization factor for each MRI contrast, and compared these with the normalized-intensity values obtained using automated NAWM (A-NAWM) masks as the normalization factor. ANOVA assessed the MTR differences across tissue types. Paired t-test or Wilcoxon signed-ranked test assessed FWML and DAWM differences between manual and automatically derived volumes. Pearson correlations assessed the relationship between MTR and normalized intensity values in the manual and automatically derived masks. RESULTS: The mean Dice-kappa values for dataset 1 were: 0.79 for DAWM masks and 0.90 for FWML masks. In dataset 2, mean Dice-kappa values were: 0.78 for DAWM and 0.87 for FWML. In dataset 3, mean Dice-kappa values were 0.72 for DAWM, and 0.87 for FWML. Manual and automated DAWM and FWML volumes were not significantly different in all datasets. MTR values were significantly lower in manually and automatically derived FWML compared with DAWM in both datasets (dataset 1 manual: F = 111,08, p < 0.0001; automated: F = 153.90, p < 0.0001; dataset 2 manual: F = 31.25, p < 0.0001; automated: F = 74.04, p < 0.0001). In both datasets, manually derived FWML and DAWM MTR values showed significant correlations with normalized T1w (r = 0.77 to 0.94) intensities. CONCLUSIONS: The separation of FWML and DAWM on MRI scans of MS patients using automated intensity thresholds on T2w images is feasible. MTR values are significantly lower in FWML than DAWM, and DAWM values are significantly lower than NAWM, reflecting potentially greater demyelination within focal lesions. T1w normalized intensity values exhibit a significant correlation with MTR values in both tissues of interest and could be used as a proxy to assess demyelination when MTR or other myelin-sensitive images are not available.

YNICL Journal 2020 Journal Article

Cognitive and motor correlates of grey and white matter pathology in Parkinson’s disease

Mahsa Dadar
Myrlene Gee
Ashfaq Shuaib
Simon Duchesne
Richard Camicioli

INTRODUCTION: Previous studies have found associations between grey matter atrophy and white matter hyperintensities (WMH) of vascular origin with cognitive and motor deficits in Parkinson's disease (PD). Here we investigate these relationships in a sample of PD patients and age-matched healthy controls. METHODS: Data included 50 PD patients and 45 age-matched controls with T1-weighted and FLAIR scans at baseline, 18-months, and 36-months follow-up. Deformation-based morphometry was used to measure grey matter atrophy. SNIPE (Scoring by Nonlocal Image Patch Estimator) was used to measure Alzheimer's disease-like textural patterns in the hippocampi. WMHs were segmented using T1-weighted and FLAIR images. The relationship between MRI features and clinical scores was assessed using mixed-effects models. The motor subscore of the Unified Parkinson's Disease Rating Scale (UPDRSIII), number of steps in a walking trial, and Dementia Rating Scale (DRS) were used respectively as measures of motor function, gait, and cognition. RESULTS: Substantia nigra atrophy was significantly associated with motor deficits, with a greater impact in PDs (p < 0.05). Hippocampal SNIPE scores were associated with cognitve decline in both PD and controls (p < 0.01). WMH burden was significantly associated with cognitive decline and increased motor deficits in the PD group, and gait deficits in both PD and controls (p < 0.03). CONCLUSION: While substantia nigra atrophy and WMH burden were significantly associated with additional motor deficits, WMH burden and hippocampal atrophy were associated with cognitive deficits in PD patients. These results suggest an additive contribution of both grey and white matter damage to the motor and cognitive deficits in PD.

YNIMG Journal 2020 Journal Article

Neuroanatomical changes in white and grey matter after sleeve gastrectomy

Andréanne Michaud
Mahsa Dadar
Mélissa Pelletier
Yashar Zeighami
Isabel Garcia-Garcia
Sylvain Iceta
Yvonne Yau
Mélanie Nadeau

Background MRI studies show that obese adults have reduced grey matter (GM) and white matter (WM) tissue density as well as altered WM integrity. Bariatric surgery can lead to substantial weight loss and improvements in metabolic parameters, but it remains to be examined if it induces structural brain changes. The aim of this study was to characterize GM and WM density changes measured with MRI in a longitudinal setting following sleeve gastrectomy, and to determine whether any changes are related to inflammation and cardiometabolic blood markers. Methods 29 participants with obesity (age: 45. 9 ± 7. 8 years) scheduled to undergo sleeve gastrectomy were recruited. High-resolution T1-weighted anatomical images were acquired 1 month prior to as well as 4 and 12 months after surgery. GM and WM densities were quantified using voxel-based morphometry (VBM). Circulating lipid profile, glucose, insulin and inflammatory markers (interleukin-6, C-reactive protein and lipopolysaccharide-binding protein) were measured at each time point. A linear mixed effect model was used to compare brain changes before and after SG, controlling for age, sex, initial BMI and diabetic status. To assess the associations between changes in adiposity, metabolism and inflammation and changes in GM or WM density, the mean GM and WM densities were extracted across all the participants using atlas-derived regions of interest, and linear mixed-effect models were used. Results As expected, weight, BMI, waist circumference and neck circumference significantly decreased after SG compared with baseline (p < 0. 001 for all). A widespread increase in WM density was observed after surgery, particularly in the cerebellum, brain stem, cerebellar peduncle, cingulum, corpus callosum and corona radiata (p < 0. 05, after FDR correction). Significant increases in GM density were observed 4 months after SG compared to baseline in several brain regions such as the bilateral occipital cortex, temporal cortex, postcentral gyrus, cerebellum, hippocampus and insula as well as right fusiform gyrus, right parahippocampal gyrus, right lingual gyrus and right amygdala. These GM and WM increases were more pronounced and widespread after 12 months and were significantly associated with post-operative weight loss and the improvement of metabolic alterations. A linear mixed-effect model also showed associations between post-operative reductions in lipopolysaccharide-binding protein, a marker of inflammation, and increased WM density. To confirm our results, we tested whether the peak of each significant region showed BMI-related differences in an independent dataset (Human Connectome Project). We matched a group of individuals who were severely obese with a group of individuals who were lean for age, sex and ethnicity. Severe obesity was associated with reduced WM density in the brain stem and cerebellar peduncle as well as reduced GM density in cerebellum, regions that significantly changed after surgery (p < 0. 01 for all clusters). Conclusions Bariatric surgery-induced weight loss and improvement in metabolic alterations is associated with widespread increases in WM and GM densities. These post-operative changes overlapped with baseline brain differences between participants who were severely obese and those who were normal-weight in a separate dataset, which may suggest a recovery of WM and GM alterations after bariatric surgery.

YNIMG Journal 2020 Journal Article

Reliability assessment of tissue classification algorithms for multi-center and multi-scanner data

Mahsa Dadar
Simon Duchesne

Background Gray and white matter volume difference and change are important imaging markers of pathology and disease progression in neurology and psychiatry. Such measures are usually estimated from tissue segmentation maps produced by publicly available image processing pipelines. However, the reliability of the produced segmentations when using multi-center and multi-scanner data remains understudied. Here, we assess the robustness of six publicly available tissue classification pipelines across images acquired from different MR scanners and sites. Methods We used 90 T1-weighted images of a single individual, scanned in 73 sessions across 27 different sites to assess the robustness of the tissue classification tools. Variability in Dice similarity index values and tissue volumes was assessed for Atropos, BISON, Classify_Clean, FAST, FreeSurfer, and SPM12. Results BISON had the highest overall Dice coefficient for GM, followed by SPM12 and Atropos; while Atropos had the highest overall Dice coefficient for WM, followed by BISON and SPM12. BISON had the lowest overall variability in its volumetric estimates, followed by FreeSurfer, and SPM12. All methods also had significant differences between some of their estimates across different scanner manufacturers (e. g. BISON had significantly higher GM estimates and correspondingly lower WM estimates for GE scans compared to Philips and Siemens), and different signal-to-noise ratio (SNR) levels (e. g. FAST and FreeSurfer had significantly higher WM volume estimates for high versus medium and low SNR tertiles as well as correspondingly lower GM volume estimates). Conclusions Our comparisons provide a benchmark on the reliability of the publicly used tissue classification techniques and the amount of variability that can be expected when using large multi-center and multi-scanner databases.

YNICL Journal 2020 Journal Article

White matter hyperintensities and neuropsychiatric symptoms in mild cognitive impairment and Alzheimer’s disease

Karen Misquitta
Mahsa Dadar
D. Louis Collins
Maria Carmela Tartaglia

Neuropsychiatric symptoms (NPS), such as apathy, irritability and depression, are frequently encountered in patients with Alzheimer's disease (AD). Focal grey matter atrophy has been linked to NPS development. Cerebrovascular disease is common among AD patients and can be detected on MRI as white matter hyperintensities (WMH). In this longitudinal study, the relative contribution of WMH burden and GM atrophy to NPS was evaluated in a cohort of mild cognitive impairment (MCI), AD and normal controls. This study included 121 AD, 315 MCI and 225 normal control subjects from the Alzheimer's Disease Neuroimaging Initiative. NPS were assessed using the Neuropsychiatric Inventory and grouped into hyperactivity, psychosis, affective and apathy subsyndromes. WMH were measured using an automatic segmentation technique and mean deformation-based morphometry (DBM) was used to measure atrophy of grey matter regions. Linear mixed-effects models found focal grey matter atrophy and WMH volume both contributed significantly to NPS subsyndromes in MCI and AD subjects, however, WMH burden played a greater role. This study could provide a better understanding of the pathophysiology of NPS in AD and support the monitoring and control of vascular risk factors.

YNIMG Journal 2019 Journal Article

A clinical-anatomical signature of Parkinson's disease identified with partial least squares and magnetic resonance imaging

Yashar Zeighami
Seyed-Mohammad Fereshtehnejad
Mahsa Dadar
D. Louis Collins
Ronald B. Postuma
Bratislav Mišić
Alain Dagher

Parkinson's disease (PD) is a neurodegenerative disorder characterized by a wide array of motor and non-motor symptoms. It remains unclear whether neurodegeneration in discrete loci gives rise to discrete symptoms, or whether network-wide atrophy gives rise to the unique behavioural and clinical profile associated with PD. Here we apply a data-driven strategy to isolate large-scale, multivariate associations between distributed atrophy patterns and clinical phenotypes in PD. In a sample of N = 229 de novo PD patients, we estimate disease-related atrophy using deformation based morphometry (DBM) of T1 weighted MR images. Using partial least squares (PLS), we identify a network of subcortical and cortical regions whose collective atrophy is associated with a clinical phenotype encompassing motor and non-motor features. Despite the relatively early stage of the disease in the sample, the atrophy pattern encompassed lower brainstem, substantia nigra, basal ganglia and cortical areas, consistent with the Braak hypothesis. In addition, individual variation in this putative atrophy network predicted longitudinal clinical progression in both motor and non-motor symptoms. Altogether, these results demonstrate a pleiotropic mapping between neurodegeneration and the clinical manifestations of PD, and that this mapping can be detected even in de novo patients.

YNICL Journal 2019 Journal Article

Assessment of a prognostic MRI biomarker in early de novo Parkinson's disease

Yashar Zeighami
Seyed-Mohammad Fereshtehnejad
Mahsa Dadar
D. Louis Collins
Ronald B. Postuma
Alain Dagher

BACKGROUND: Commonly used neuroimaging biomarkers in Parkinson's disease (PD) are useful for diagnosis but poor at predicting outcomes. We explored whether an atrophy pattern from whole-brain structural MRI, measured in the drug-naïve early stage, could predict PD prognosis. METHODS: I ioflupane single photon emission computed tomography, the postural-instability-gait-disturbance score, and cerebrospinal fluid markers. FINDINGS: After 4.5 years follow-up, PD-specific atrophy network score at baseline significantly predicted change in UPDRS-part III (r = -0.197, p = .003), MoCA (r = 0.253, p = .0002) and global composite outcome (r = -0.249, p = .0002). Compared with the 3rd tertile (i.e. least atrophy), the tertile with the highest baseline atrophy (i.e. the 1st tertile) had a 3-point annual faster progression in UPDRS-part III (p = .012), faster worsening of posture-instability gait scores (+0.21 further annual increase, p < .0001), faster decline in MoCA (-0.74 further annual decline in MoCA, p = .0372) and a + 0.38 (p = .0029) faster annual increase in the global composite z-score. All findings were replicated in a validation analysis using 1.5T MRI. Receiver operating characteristic analysis confirmed the superiority of the MRI biomarker, although it had modest AUC values (0.63). By comparison, the other biomarkers were limited in their ability to predict prognosis either in the main or validation analysis. INTERPRETATION: A PD-specific network atrophy pattern predicts progression of motor, cognitive, and global outcome in PD, and is a better predictor of prognosis than any of the other tested biomarkers. Therefore, it has potential as a prognostic biomarker for clinical trials of early PD.

YNICL Journal 2019 Journal Article

Deformation based morphometry study of longitudinal MRI changes in behavioral variant frontotemporal dementia

Ana L. Manera
Mahsa Dadar
D. Louis Collins
Simon Ducharme

OBJECTIVE: To objectively quantify how cerebral volume loss could assist with clinical diagnosis and clinical trial design in the behavioural variant of frontotemporal dementia (bvFTD). METHODS: We applied deformation-based morphometric analyses with robust registration to precisely quantify the magnitude and pattern of atrophy in patients with bvFTD as compared to cognitively normal controls (CNCs), to assess the progression of atrophy over one year follow up and to generate clinical trial sample size estimates to detect differences for the structures most sensitive to change. This study included 203 subjects - 70 bvFTD and 133 CNCs - with a total of 482 timepoints from the Frontotemporal Lobar Degeneration Neuroimaging Initiative. RESULTS: Deformation based morphometry (DBM) revealed significant atrophy in the frontal lobes, insula, medial and anterior temporal regions bilaterally in bvFTD subjects compared to controls with outstanding subcortical involvement. We provide detailed information on regional changes per year. In both cross-sectional analysis and over a one-year follow-up period, ventricle expansion was the most prominent differentiator of bvFTD from controls and a sensitive marker of disease progression. CONCLUSIONS: Automated measurement of ventricular expansion is a sensitive and reliable marker of disease progression in bvFTD to be used in clinical trials for potential disease modifying drugs, as well as possibly to implement in clinical practice. Ventricular expansion measured with DBM provides the lowest published estimated sample size for clinical trial design to detect significant differences over one and two years.

YNIMG Journal 2018 Journal Article

A comparison of publicly available linear MRI stereotaxic registration techniques

Mahsa Dadar
Vladimir S. Fonov
D. Louis Collins

Introduction Linear registration to a standard space is one of the major steps in processing and analyzing magnetic resonance images (MRIs) of the brain. Here we present an overview of linear stereotaxic MRI registration and compare the performance of 5 publicly available and extensively used linear registration techniques in medical image analysis. Methods A set of 9693 T1-weighted MR images were obtained for testing from 4 datasets: ADNI, PREVENT-AD, PPMI, and HCP, two of which have multi-center and multi-scanner data and three of which have longitudinal data. Each individual native image was linearly registered to the MNI ICBM152 average template using five versions of MRITOTAL from MINC tools, FLIRT from FSL, two versions of Elastix, spm_affreg from SPM, and ANTs linear registration techniques. Quality control (QC) images were generated from the registered volumes and viewed by an expert rater to assess the quality of the registrations. The QC image contained 60 sub-images (20 of each of axial, sagittal, and coronal views at different levels throughout the brain) overlaid with contours of the ICBM152 template, enabling the expert rater to label the registration as acceptable or unacceptable. The performance of the registration techniques was then compared across different datasets. In addition, the effect of image noise, intensity non-uniformity, age, head size, and atrophy on the performance of the techniques was investigated by comparing differences between age, scaling factor, ventricle volume, brain volume, and white matter hyperintensity (WMH) volumes between passed and failed cases for each method. Results The average registration failure rate among all datasets was 27. 41%, 27. 14%, 12. 74%, 13. 03%, 0. 44% for the five versions of MRITOTAL techniques, 8. 87% for ANTs, 11. 11% for FSL, 12. 35% for Elastix Affine, 24. 40% for Elastix Similarity, and 30. 66% for SPM. There were significant effects of signal to noise ratio, image intensity non-uniformity estimates, as well as age, head size, and atrophy related changes between passed and failed registrations. Conclusion Our experiments show that the Revised BestLinReg had the best performance among the evaluated registration techniques while all techniques performed worse for images with higher levels of noise and non-uniformity as well as atrophy related changes.

YNICL Journal 2018 Journal Article

Structural neuroimaging as clinical predictor: A review of machine learning applications

José María Mateos-Pérez
Mahsa Dadar
María Lacalle-Aurioles
Yasser Iturria-Medina
Yashar Zeighami
Alan C. Evans

In this paper, we provide an extensive overview of machine learning techniques applied to structural magnetic resonance imaging (MRI) data to obtain clinical classifiers. We specifically address practical problems commonly encountered in the literature, with the aim of helping researchers improve the application of these techniques in future works. Additionally, we survey how these algorithms are applied to a wide range of diseases and disorders (e.g. Alzheimer's disease (AD), Parkinson's disease (PD), autism, multiple sclerosis, traumatic brain injury, etc.) in order to provide a comprehensive view of the state of the art in different fields.

YNICL Journal 2018 Journal Article

The relationship between brain atrophy and cognitive-behavioural symptoms in retired Canadian football players with multiple concussions

Karen Misquitta
Mahsa Dadar
Apameh Tarazi
Mohammed W. Hussain
Mohammed K. Alatwi
Ahmed Ebraheem
Namita Multani
Mozhgan Khodadadi

Multiple concussions, particularly in contact sports, have been associated with cognitive deficits, psychiatric impairment and neurodegenerative diseases like chronic traumatic encephalopathy. We used volumetric and deformation-based morphometric analyses to test the hypothesis that repeated concussions may be associated with smaller regional brain volumes, poorer cognitive performance and behavioural symptoms among former professional football players compared to healthy controls. This study included fifty-three retired Canadian Football League players, 25 age- and education-matched healthy controls, and controls from the Cambridge Centre for Aging and Neuroscience database for validation. Volumetric analyses revealed greater hippocampal atrophy than expected for age in former athletes with multiple concussions than controls and smaller left hippocampal volume was associated with poorer verbal memory performance in the former athletes. Deformation-based morphometry confirmed smaller bilateral hippocampal volume that was associated with poorer verbal memory performance in athletes. Repeated concussions may lead to greater regional atrophy than expected for age.

YNICL Journal 2018 Journal Article

White matter hyperintensities are linked to future cognitive decline in de novo Parkinson's disease patients

Mahsa Dadar
Yashar Zeighami
Yvonne Yau
Seyed-Mohammad Fereshtehnejad
Josefina Maranzano
Ronald B. Postuma
Alain Dagher
D. Louis Collins

White Matter Hyperintensities (WMHs) are associated with cognitive decline in aging and Alzheimer's disease. However, the pathogenesis of cognitive decline in Parkinson's disease (PD) is not as clearly related to vascular causes, and therefore the role of WMHs as a marker of small-vessel disease (SVD) in PD is less clear. Currently, SVD in PD is assessed and treated independently of the disease. However, if WMH as the major MRI sign of SVD has a higher impact on cognitive decline in PD patients than in healthy controls, vascular pathology needs to be assessed and treated with a higher priority in this population. Here we investigate whether the presence of WMHs leads to increased cognitive decline in de novo PD, and if these effects relate to cortical atrophy. WMHs and cortical thickness were measured in de novo PD patients and age-matched controls (NPD = 365, NControl = 174) from Parkinson's Progression Markers Initiative (PPMI) to study the relationship between baseline WMHs, future cognitive decline (follow-up: 4. 09 ± 1. 14 years) and cortical atrophy (follow-up: 1. 05 ± 0. 10 years). PD subjects with high baseline WMH loads had significantly greater cognitive decline than i) PD subjects with low WMH load, and ii) control subjects with high WMH load. Furthermore, in PD subjects, high WMH load resulted in more cortical thinning in the right frontal lobe. Theses results show that the presence of WMHs in de novo PD patients predicts greater future cognitive decline and cortical atrophy than in normal aging.

YNIMG Journal 2017 Journal Article

Performance comparison of 10 different classification techniques in segmenting white matter hyperintensities in aging

Mahsa Dadar
Josefina Maranzano
Karen Misquitta
Cassandra J. Anor
Vladimir S. Fonov
M. Carmela Tartaglia
Owen T. Carmichael
Charles DeCarli

Introduction White matter hyperintensities (WMHs) are areas of abnormal signal on magnetic resonance images (MRIs) that characterize various types of histopathological lesions. The load and location of WMHs are important clinical measures that may indicate the presence of small vessel disease in aging and Alzheimer's disease (AD) patients. Manually segmenting WMHs is time consuming and prone to inter-rater and intra-rater variabilities. Automated tools that can accurately and robustly detect these lesions can be used to measure the vascular burden in individuals with AD or the elderly population in general. Many WMH segmentation techniques use a classifier in combination with a set of intensity and location features to segment WMHs, however, the optimal choice of classifier is unknown. Methods We compare 10 different linear and nonlinear classification techniques to identify WMHs from MRI data. Each classifier is trained and optimized based on a set of features obtained from co-registered MR images containing spatial location and intensity information. We further assess the performance of the classifiers using different combinations of MRI contrast information. The performances of the different classifiers were compared on three heterogeneous multi-site datasets, including images acquired with different scanners and different scan-parameters. These included data from the ADC study from University of California Davis, the NACC database and the ADNI study. The classifiers (naïve Bayes, logistic regression, decision trees, random forests, support vector machines, k-nearest neighbors, bagging, and boosting) were evaluated using a variety of voxel-wise and volumetric similarity measures such as Dice Kappa similarity index (SI), Intra-Class Correlation (ICC), and sensitivity as well as computational burden and processing times. These investigations enable meaningful comparisons between the performances of different classifiers to determine the most suitable classifiers for segmentation of WMHs. In the spirit of open-source science, we also make available a fully automated tool for segmentation of WMHs with pre-trained classifiers for all these techniques. Results Random Forests yielded the best performance among all classifiers with mean Dice Kappa (SI) of 0. 66±0. 17 and ICC=0. 99 for the ADC dataset (using T1w, T2w, PD, and FLAIR scans), SI=0. 72±0. 10, ICC=0. 93 for the NACC dataset (using T1w and FLAIR scans), SI=0. 66±0. 23, ICC=0. 94 for ADNI1 dataset (using T1w, T2w, and PD scans) and SI=0. 72±0. 19, ICC=0. 96 for ADNI2/GO dataset (using T1w and FLAIR scans). Not using the T2w/PD information did not change the performance of the Random Forest classifier (SI=0. 66±0. 17, ICC=0. 99). However, not using FLAIR information in the ADC dataset significantly decreased the Dice Kappa, but the volumetric correlation did not drastically change (SI=0. 47±0. 21, ICC=0. 95). Conclusion Our investigations showed that with appropriate features, most off-the-shelf classifiers are able to accurately detect WMHs in presence of FLAIR scan information, while Random Forests had the best performance across all datasets. However, we observed that the performances of most linear classifiers and some nonlinear classifiers drastically decline in absence of FLAIR information, with Random Forest still retaining the best performance.