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    Topographic divergence of atypical cortical asymmetry and atrophy patterns in temporal lobe epilepsy
    Park, B-Y ; Lariviere, S ; Rodriguez-Cruces, R ; Royer, J ; Tavakol, S ; Wang, Y ; Caciagli, L ; Caligiuri, ME ; Gambardella, A ; Concha, L ; Keller, SS ; Cendes, F ; Alvim, MKM ; Yasuda, C ; Bonilha, L ; Gleichgerrcht, E ; Focke, NK ; Kreilkamp, BAK ; Domin, M ; von Podewils, F ; Langner, S ; Rummel, C ; Rebsamen, M ; Wiest, R ; Martin, P ; Kotikalapudi, R ; Bender, B ; O'Brien, TJ ; Law, M ; Sinclair, B ; Vivash, L ; Kwan, P ; Desmond, PM ; Malpas, CB ; Lui, E ; Alhusaini, S ; Doherty, CP ; Cavalleri, GL ; Delanty, N ; Kalviainen, R ; Jackson, GD ; Kowalczyk, M ; Mascalchi, M ; Semmelroch, M ; Thomas, RH ; Soltanian-Zadeh, H ; Davoodi-Bojd, E ; Zhang, J ; Lenge, M ; Guerrini, R ; Bartolini, E ; Hamandi, K ; Foley, S ; Weber, B ; Depondt, C ; Absil, J ; Carr, SJA ; Abela, E ; Richardson, MP ; Devinsky, O ; Severino, M ; Striano, P ; Parodi, C ; Tortora, D ; Hatton, SN ; Vos, SB ; Duncan, JS ; Galovic, M ; Whelan, CD ; Bargallo, N ; Pariente, J ; Conde-Blanco, E ; Vaudano, AE ; Tondelli, M ; Meletti, S ; Kong, X-Z ; Francks, C ; Fisher, SE ; Caldairou, B ; Ryten, M ; Labate, A ; Sisodiya, SM ; Thompson, PM ; McDonald, CR ; Bernasconi, A ; Bernasconi, N ; Bernhardt, BC (OXFORD UNIV PRESS, 2022-03-25)
    Temporal lobe epilepsy, a common drug-resistant epilepsy in adults, is primarily a limbic network disorder associated with predominant unilateral hippocampal pathology. Structural MRI has provided an in vivo window into whole-brain grey matter structural alterations in temporal lobe epilepsy relative to controls, by either mapping (i) atypical inter-hemispheric asymmetry; or (ii) regional atrophy. However, similarities and differences of both atypical asymmetry and regional atrophy measures have not been systematically investigated. Here, we addressed this gap using the multisite ENIGMA-Epilepsy dataset comprising MRI brain morphological measures in 732 temporal lobe epilepsy patients and 1418 healthy controls. We compared spatial distributions of grey matter asymmetry and atrophy in temporal lobe epilepsy, contextualized their topographies relative to spatial gradients in cortical microstructure and functional connectivity calculated using 207 healthy controls obtained from Human Connectome Project and an independent dataset containing 23 temporal lobe epilepsy patients and 53 healthy controls and examined clinical associations using machine learning. We identified a marked divergence in the spatial distribution of atypical inter-hemispheric asymmetry and regional atrophy mapping. The former revealed a temporo-limbic disease signature while the latter showed diffuse and bilateral patterns. Our findings were robust across individual sites and patients. Cortical atrophy was significantly correlated with disease duration and age at seizure onset, while degrees of asymmetry did not show a significant relationship to these clinical variables. Our findings highlight that the mapping of atypical inter-hemispheric asymmetry and regional atrophy tap into two complementary aspects of temporal lobe epilepsy-related pathology, with the former revealing primary substrates in ipsilateral limbic circuits and the latter capturing bilateral disease effects. These findings refine our notion of the neuropathology of temporal lobe epilepsy and may inform future discovery and validation of complementary MRI biomarkers in temporal lobe epilepsy.
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    Healthy Life-Year Costs of Treatment Speed From Arrival to Endovascular Thrombectomy in Patients With Ischemic Stroke A Meta-analysis of Individual Patient Data From 7 Randomized Clinical Trials
    Almekhlafi, MA ; Goyal, M ; Dippel, DWJ ; Majoie, CBLM ; Campbell, BCV ; Muir, KW ; Demchuk, AM ; Bracard, S ; Guillemin, F ; Jovin, TG ; Mitchell, P ; White, P ; Hill, MD ; Brown, S ; Saver, JL (AMER MEDICAL ASSOC, 2021-05-03)
    Importance: The benefits of endovascular thrombectomy (EVT) are time dependent. Prior studies may have underestimated the time-benefit association because time of onset is imprecisely known. Objective: To assess the lifetime outcomes associated with speed of endovascular thrombectomy in patients with acute ischemic stroke due to large-vessel occlusion (LVO). Data Sources: PubMed was searched for randomized clinical trials of stent retriever thrombectomy devices vs medical therapy in patients with anterior circulation LVO within 12 hours of last known well time, and for which a peer-reviewed, complete primary results article was published by August 1, 2020. Study Selection: All randomized clinical trials of stent retriever thrombectomy devices vs medical therapy in patients with anterior circulation LVO within 12 hours of last known well time were included. Data Extraction/Synthesis: Patient-level data regarding presenting clinical and imaging features and functional outcomes were pooled from the 7 retrieved randomized clinical trials of stent retriever thrombectomy devices (entirely or predominantly) vs medical therapy. All 7 identified trials published in a peer-reviewed journal (by August 1, 2020) contributed data. Detailed time metrics were collected including last known well-to-door (LKWTD) time; last known well/onset-to-puncture (LKWTP) time; last known well-to-reperfusion (LKWR) time; door-to-puncture (DTP) time; and door-to-reperfusion (DTR) time. Main Outcomes and Measures: Change in healthy life-years measured as disability-adjusted life-years (DALYs). DALYs were calculated as the sum of years of life lost (YLL) owing to premature mortality and years of healthy life lost because of disability (YLD). Disability weights were assigned using the utility-weighted modified Rankin Scale. Age-specific life expectancies without stroke were calculated from 2017 US National Vital Statistics. Results: Among the 781 EVT-treated patients, 406 (52.0%) were early-treated (LKWTP ≤4 hours) and 375 (48.0%) were late-treated (LKWTP >4-12 hours). In early-treated patients, LKWTD was 188 minutes (interquartile range, 151.3-214.8 minutes) and DTP 105 minutes (interquartile range, 76-135 minutes). Among the 298 of 380 (78.4%) patients with substantial reperfusion, median DTR time was 145.0 minutes (interquartile range, 111.5-185.5 minutes). Care process delays were associated with worse clinical outcomes in LKW-to-intervention intervals in early-treated patients and in door-to-intervention intervals in early-treated and late-treated patients, and not associated with LKWTD intervals, eg, in early-treated patients, for each 10-minute delay, healthy life-years lost were DTP 1.8 months vs LKWTD 0.0 months; P < .001. Considering granular time increments, the amount of healthy life-time lost associated with each 1 second of delay was DTP 2.2 hours and DTR 2.4 hours. Conclusions and Relevance: In this study, care delays were associated with loss of healthy life-years in patients with acute ischemic stroke treated with EVT, particularly in the postarrival time period. The finding that every 1 second of delay was associated with loss of 2.2 hours of healthy life may encourage continuous quality improvement in door-to-treatment times.
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    Machine learning approaches for imaging-based prognostication of the outcome of surgery for mesial temporal lobe epilepsy
    Sinclair, B ; Cahill, V ; Seah, J ; Kitchen, A ; Vivash, LE ; Chen, Z ; Malpas, CB ; O'Shea, MF ; Desmond, PM ; Hicks, RJ ; Morokoff, AP ; King, JA ; Fabinyi, GC ; Kaye, AH ; Kwan, P ; Berkovic, SF ; Law, M ; O'Brien, TJ (WILEY, 2022-03-25)
    OBJECTIVES: Around 30% of patients undergoing surgical resection for drug-resistant mesial temporal lobe epilepsy (MTLE) do not obtain seizure freedom. Success of anterior temporal lobe resection (ATLR) critically depends on the careful selection of surgical candidates, aiming at optimizing seizure freedom while minimizing postoperative morbidity. Structural MRI and FDG-PET neuroimaging are routinely used in presurgical assessment and guide the decision to proceed to surgery. In this study, we evaluate the potential of machine learning techniques applied to standard presurgical MRI and PET imaging features to provide enhanced prognostic value relative to current practice. METHODS: Eighty two patients with drug resistant MTLE were scanned with FDG-PET pre-surgery and T1-weighted MRI pre- and postsurgery. From these images the following features of interest were derived: volume of temporal lobe (TL) hypometabolism, % of extratemporal hypometabolism, presence of contralateral TL hypometabolism, presence of hippocampal sclerosis, laterality of seizure onset volume of tissue resected and % of temporal lobe hypometabolism resected. These measures were used as predictor variables in logistic regression, support vector machines, random forests and artificial neural networks. RESULTS: In the study cohort, 24 of 82 (28.3%) who underwent an ATLR for drug-resistant MTLE did not achieve Engel Class I (i.e., free of disabling seizures) outcome at a minimum of 2 years of postoperative follow-up. We found that machine learning approaches were able to predict up to 73% of the 24 ATLR surgical patients who did not achieve a Class I outcome, at the expense of incorrect prediction for up to 31% of patients who did achieve a Class I outcome. Overall accuracies ranged from 70% to 80%, with an area under the receiver operating characteristic curve (AUC) of .75-.81. We additionally found that information regarding overall extent of both total and significantly hypometabolic tissue resected was crucial to predictive performance, with AUC dropping to .59-.62 using presurgical information alone. Incorporating the laterality of seizure onset and the choice of machine learning algorithm did not significantly change predictive performance. SIGNIFICANCE: Collectively, these results indicate that "acceptable" to "good" patient-specific prognostication for drug-resistant MTLE surgery is feasible with machine learning approaches utilizing commonly collected imaging modalities, but that information on the surgical resection region is critical for optimal prognostication.
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    Stroke population-specific neuroanatomical CT-MRI brain atlas
    Kaffenberger, T ; Venkatraman, V ; Steward, C ; Thijs, VN ; Bernhardt, J ; Desmond, PM ; Campbell, BC ; Yassi, N (SPRINGER, 2022-01-30)
    PURPOSE: Development of a freely available stroke population-specific anatomical CT/MRI atlas with a reliable normalisation pipeline for clinical CT. METHODS: By reviewing CT scans in suspected stroke patients and filtering the AIBL MRI database, respectively, we collected 50 normal-for-age CT and MRI scans to build a standard-resolution CT template and a high-resolution MRI template. The latter was manually segmented into anatomical brain regions. We then developed and validated a MRI to CT registration pipeline to align the MRI atlas onto the CT template. Finally, we developed a CT-to-CT-normalisation pipeline and tested its reliability by calculating Dice coefficient (Dice) and Average Hausdorff Distance (AHD) for predefined areas in 100 CT scans from ischaemic stroke patients. RESULTS: The resulting CT/MRI templates were age and sex matched to a general stroke population (median age 71.9 years (62.1-80.2), 60% male). Specifically, this accounts for relevant structural changes related to aging, which may affect registration. Applying the validated MRI to CT alignment (Dice > 0.78, Average Hausdorff Distance < 0.59 mm) resulted in our final CT-MRI atlas. The atlas has 52 manually segmented regions and covers the whole brain. The alignment of four cortical and subcortical brain regions with our CT-normalisation pipeline was reliable for small/medium/large infarct lesions (Dice coefficient > 0.5). CONCLUSION: The newly created CT-MRI brain atlas has the potential to standardise stroke lesion segmentation. Together with the automated normalisation pipeline, it allows analysis of existing and new datasets to improve prediction tools for stroke patients (free download at https://forms.office.com/r/v4t3sWfbKs ).
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    Atlas of lesion locations and postsurgical seizure freedom in focal cortical dysplasia: A MELD study
    Wagstyl, K ; Whitaker, K ; Raznahan, A ; Seidlitz, J ; Vertes, PE ; Foldes, S ; Humphreys, Z ; Hu, W ; Mo, J ; Likeman, M ; Davies, S ; Lenge, M ; Cohen, NT ; Tang, Y ; Wang, S ; Ripart, M ; Chari, A ; Tisdall, M ; Bargallo, N ; Conde-Blanco, E ; Carlos Pariente, J ; Pascual-Diaz, S ; Delgado-Martinez, I ; Perez-Enriquez, C ; Lagorio, I ; Abela, E ; Mullatti, N ; O'Muircheartaigh, J ; Vecchiato, K ; Liu, Y ; Caligiuri, M ; Sinclair, B ; Vivash, L ; Willard, A ; Kandasamy, J ; McLellan, A ; Sokol, D ; Semmelroch, M ; Kloster, A ; Opheim, G ; Yasuda, C ; Zhang, K ; Hamandi, K ; Barba, C ; Guerrini, R ; Gaillard, WD ; You, X ; Wang, I ; Gonzalez-Ortiz, S ; Severino, M ; Striano, P ; Tortora, D ; Kalviainen, R ; Gambardella, A ; Labate, A ; Desmond, P ; Lui, E ; O'Brien, T ; Shetty, J ; Jackson, G ; Duncan, JS ; Winston, GP ; Pinborg, L ; Cendes, F ; Cross, JH ; Baldeweg, T ; Adler, S (WILEY, 2021-11-29)
    OBJECTIVE: Drug-resistant focal epilepsy is often caused by focal cortical dysplasias (FCDs). The distribution of these lesions across the cerebral cortex and the impact of lesion location on clinical presentation and surgical outcome are largely unknown. We created a neuroimaging cohort of patients with individually mapped FCDs to determine factors associated with lesion location and predictors of postsurgical outcome. METHODS: The MELD (Multi-centre Epilepsy Lesion Detection) project collated a retrospective cohort of 580 patients with epilepsy attributed to FCD from 20 epilepsy centers worldwide. Magnetic resonance imaging-based maps of individual FCDs with accompanying demographic, clinical, and surgical information were collected. We mapped the distribution of FCDs, examined for associations between clinical factors and lesion location, and developed a predictive model of postsurgical seizure freedom. RESULTS: FCDs were nonuniformly distributed, concentrating in the superior frontal sulcus, frontal pole, and temporal pole. Epilepsy onset was typically before the age of 10 years. Earlier epilepsy onset was associated with lesions in primary sensory areas, whereas later epilepsy onset was associated with lesions in association cortices. Lesions in temporal and occipital lobes tended to be larger than frontal lobe lesions. Seizure freedom rates varied with FCD location, from around 30% in visual, motor, and premotor areas to 75% in superior temporal and frontal gyri. The predictive model of postsurgical seizure freedom had a positive predictive value of 70% and negative predictive value of 61%. SIGNIFICANCE: FCD location is an important determinant of its size, the age at epilepsy onset, and the likelihood of seizure freedom postsurgery. Our atlas of lesion locations can be used to guide the radiological search for subtle lesions in individual patients. Our atlas of regional seizure freedom rates and associated predictive model can be used to estimate individual likelihoods of postsurgical seizure freedom. Data-driven atlases and predictive models are essential for evidence-based, precision medicine and risk counseling in epilepsy.
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    Assessment of the DTI-ALPS Parameter Along the Perivascular Space in Older Adults at Risk of Dementia
    Steward, CE ; Venkatraman, VK ; Lui, E ; Malpas, CB ; Ellis, KA ; Cyarto, EV ; Vivash, L ; O'Brien, TJ ; Velakoulis, D ; Ames, D ; Masters, CL ; Lautenschlager, NT ; Bammer, R ; Desmond, PM (WILEY, 2021-02-08)
    BACKGROUND AND PURPOSE: Recently, there has been growing interest in the glymphatic system (the functional waste clearance pathway for the central nervous system and its role in flushing solutes (such as amyloid ß and tau), metabolic, and other cellular waste products in the brain. Herein, we investigate a recent potential biomarker for glymphatic activity (the diffusion tensor imaging along the perivascular space [DTI-ALPS] parameter) using diffusion MRI imaging in an elderly cohort comprising 10 cognitively normal, 10 mild cognitive impairment (MCI), and 16 Alzheimer's disease (AD). METHODS: All 36 participants imaged on a Siemens 3.0T Tim Trio. Single-SE diffusion weighted Echo-planar imaging scans were acquired as well as T1 magnetization prepared rapid gradient echo, T2 axial, and susceptibility weighted imaging. Three millimeter regions of interest were drawn in the projection and association fibers adjacent to the medullary veins at the level of the lateral ventricle. The DTI-ALPS parameter was calculated in these regions and correlated with cognitive status, Mini-Mental State Examination (MMSE), and ADASCog11 measures. RESULTS: Significant correlations were found between DTI-ALPS and MMSE and ADASCog11 in the right hemisphere adjusting for age, sex, and APoE ε4 status. Significant differences were also found in the right DTI-ALPS indices between cognitively normal and AD groups (P < .026) and MCI groups (P < .025) in a univariate general linear model corrected for age, sex, and APoE ε4. Significant differences in apparent diffusion coefficient between cognitively normal and AD groups were found in the right projection fibers (P = .028). CONCLUSION: Further work is needed to determine the utility of DTI-ALPS index in larger elderly cohorts and whether it measures glymphatic activity.
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    Audit of CT reporting standards in cases of intracerebral haemorrhage at a comprehensive stroke centre in Australia
    Barras, CD ; Asadi, H ; Phal, PM ; Tress, BM ; Davis, SM ; Desmond, PM (WILEY-BLACKWELL, 2016-12-01)
    INTRODUCTION: Multiple CT-derived biomarkers that are predictive of intracerebral haemorrhage (ICH) growth and outcome have been described in the literature, but the extent to which these appear in imaging reports of ICH is unknown. The aim of this retrospective process audit was to determine which of the known predictors of ICH outcome was recorded in reports of the disease, with a view to providing reporting recommendations, as appropriate. METHOD: We examined the initial CT report of patients diagnosed with ICH presenting to a metropolitan comprehensive stroke centre and meeting inclusion criteria during the audit period between 1 March 2013 and 28 February 2014. Each report was assessed for the inclusion of the following ICH characteristics: the number of measurement dimensions; volume; location; hydrocephalus; shape; density; 'CTA spot sign' (where CTA was performed). RESULTS: A total of 100 patients met audit inclusion criteria. At least one ICH dimension was recorded in 90% of reports; however, 39% did not include the measurements in three dimensions and volume was reported in just 6%. No ICH dimension was recorded in 10% of reports. With the exception of density and shape, reporting of other CT features exceeded 95%. Where CTA was performed (58%), 14 (24%) of 58 reported the 'CTA spot sign' status. CONCLUSION: In this audit, volume was the most under-reported of the established ICH characteristics predictive of ICH outcome. Readily calculated from multiplanar reformats using the ABC/2 technique, the routine reporting of ICH volume is recommended. More reporting attention to ICH density heterogeneity and shape irregularity is encouraged, given their emerging importance. Where acute CTA is performed, the presence of any dynamic haemorrhage (CTA spot sign) should be reported.
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    Frequency drift in MR spectroscopy at 3T
    Hui, SCN ; Mikkelsen, M ; Zollner, HJ ; Ahluwalia, V ; Alcauter, S ; Baltusis, L ; Barany, DA ; Barlow, LR ; Becker, R ; Berman, J ; Berrington, A ; Bhattacharyya, PK ; Blicher, JU ; Bogner, W ; Brown, MS ; Calhoun, VD ; Castillo, R ; Cecil, KM ; Choi, YB ; Chu, WCW ; Clarke, WT ; Craven, AR ; Cuypers, K ; Dacko, M ; de la Fuente-Sandoval, C ; Desmond, P ; Domagalik, A ; Dumont, J ; Duncan, NW ; Dydak, U ; Dyke, K ; Edmondson, DA ; Ende, G ; Ersland, L ; Evans, CJ ; Fermin, ASR ; Ferretti, A ; Fillmer, A ; Gong, T ; Greenhouse, I ; Grist, JT ; Gu, M ; Harris, AD ; Hatz, K ; Heba, S ; Heckova, E ; Hegarty, JP ; Heise, K-F ; Honda, S ; Jacobson, A ; Jansen, JFA ; Jenkins, CW ; Johnston, SJ ; Juchem, C ; Kangarlu, A ; Kerr, AB ; Landheer, K ; Lange, T ; Lee, P ; Levendovszky, SR ; Limperopoulos, C ; Liu, F ; Lloyd, W ; Lythgoe, DJ ; Machizawa, MG ; MacMillan, EL ; Maddock, RJ ; Manzhurtsev, A ; Martinez-Gudino, ML ; Miller, JJ ; Mirzakhanian, H ; Moreno-Ortega, M ; Mullins, PG ; Nakajima, S ; Near, J ; Noeske, R ; Nordhoy, W ; Oeltzschner, G ; Osorio-Duran, R ; Otaduy, MCG ; Pasaye, EH ; Peeters, R ; Peltier, SJ ; Pilatus, U ; Polomac, N ; Porges, EC ; Pradhan, S ; Prisciandaro, JJ ; Puts, NA ; Rae, CD ; Reyes-Madrigal, F ; Roberts, TPL ; Robertson, CE ; Rosenberg, JT ; Rotaru, D-G ; Tuura, RLO ; Saleh, MG ; Sandberg, K ; Sangill, R ; Schembri, K ; Schrantee, A ; Semenova, NA ; Singel, D ; Sitnikov, R ; Smith, J ; Song, Y ; Stark, C ; Stoffers, D ; Swinnen, SP ; Tain, R ; Tanase, C ; Tapper, S ; Tegenthoff, M ; Thiel, T ; Thioux, M ; Truong, P ; van Dijk, P ; Vella, N ; Vidyasagar, R ; Vovk, A ; Wang, G ; Westlye, LT ; Wilbur, TK ; Willoughby, WR ; Wilson, M ; Wittsack, H-J ; Woods, AJ ; Wu, Y-C ; Xu, J ; Lopez, MY ; Yeung, DKW ; Zhao, Q ; Zhou, X ; Zupan, G ; Edden, RAE (ACADEMIC PRESS INC ELSEVIER SCIENCE, 2021-07-29)
    PURPOSE: Heating of gradient coils and passive shim components is a common cause of instability in the B0 field, especially when gradient intensive sequences are used. The aim of the study was to set a benchmark for typical drift encountered during MR spectroscopy (MRS) to assess the need for real-time field-frequency locking on MRI scanners by comparing field drift data from a large number of sites. METHOD: A standardized protocol was developed for 80 participating sites using 99 3T MR scanners from 3 major vendors. Phantom water signals were acquired before and after an EPI sequence. The protocol consisted of: minimal preparatory imaging; a short pre-fMRI PRESS; a ten-minute fMRI acquisition; and a long post-fMRI PRESS acquisition. Both pre- and post-fMRI PRESS were non-water suppressed. Real-time frequency stabilization/adjustment was switched off when appropriate. Sixty scanners repeated the protocol for a second dataset. In addition, a three-hour post-fMRI MRS acquisition was performed at one site to observe change of gradient temperature and drift rate. Spectral analysis was performed using MATLAB. Frequency drift in pre-fMRI PRESS data were compared with the first 5:20 minutes and the full 30:00 minutes of data after fMRI. Median (interquartile range) drifts were measured and showed in violin plot. Paired t-tests were performed to compare frequency drift pre- and post-fMRI. A simulated in vivo spectrum was generated using FID-A to visualize the effect of the observed frequency drifts. The simulated spectrum was convolved with the frequency trace for the most extreme cases. Impacts of frequency drifts on NAA and GABA were also simulated as a function of linear drift. Data from the repeated protocol were compared with the corresponding first dataset using Pearson's and intraclass correlation coefficients (ICC). RESULTS: Of the data collected from 99 scanners, 4 were excluded due to various reasons. Thus, data from 95 scanners were ultimately analyzed. For the first 5:20 min (64 transients), median (interquartile range) drift was 0.44 (1.29) Hz before fMRI and 0.83 (1.29) Hz after. This increased to 3.15 (4.02) Hz for the full 30 min (360 transients) run. Average drift rates were 0.29 Hz/min before fMRI and 0.43 Hz/min after. Paired t-tests indicated that drift increased after fMRI, as expected (p < 0.05). Simulated spectra convolved with the frequency drift showed that the intensity of the NAA singlet was reduced by up to 26%, 44 % and 18% for GE, Philips and Siemens scanners after fMRI, respectively. ICCs indicated good agreement between datasets acquired on separate days. The single site long acquisition showed drift rate was reduced to 0.03 Hz/min approximately three hours after fMRI. DISCUSSION: This study analyzed frequency drift data from 95 3T MRI scanners. Median levels of drift were relatively low (5-min average under 1 Hz), but the most extreme cases suffered from higher levels of drift. The extent of drift varied across scanners which both linear and nonlinear drifts were observed.
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    Brain atrophy and lesion burden are associated with disability progression in a multiple sclerosis real-world dataset using only T2-FLAIR: The NeuroSTREAM MSBase study
    Barnett, M ; Bergsland, N ; Weinstock-Guttman, B ; Butzkueven, H ; Kalincik, T ; Desmond, P ; Gaillard, F ; van Pesch, V ; Ozakbas, S ; Ignacio Rojas, J ; Boz, C ; Altintas, A ; Wang, C ; Dwyer, MG ; Yang, S ; Jakimovski, D ; Kyle, K ; Ramasamy, DP ; Zivadinov, R (ELSEVIER SCI LTD, 2021-08-29)
    BACKGROUND: Methodological challenges limit the use of brain atrophy and lesion burden measures in the follow-up of multiple sclerosis (MS) patients on clinical routine datasets. OBJECTIVE: To determine the feasibility of T2-FLAIR-only measures of lateral ventricular volume (LVV) and salient central lesion volume (SCLV), as markers of disability progression (DP) in MS. METHODS: A total of 3,228 MS patients from 9 MSBase centers in 5 countries were enrolled. Of those, 2,875 (218 with clinically isolated syndrome, 2,231 with relapsing-remitting and 426 with progressive disease subtype) fulfilled inclusion and exclusion criteria. Patients were scanned on either 1.5 T or 3 T MRI scanners, and 5,750 brain scans were collected at index and on average after 42.3 months at post-index. Demographic and clinical data were collected from the MSBase registry. LVV and SCLV were measured on clinical routine T2-FLAIR images. RESULTS: Longitudinal LVV and SCLV analyses were successful in 96% of the scans. 57% of patients had scanner-related changes over the follow-up. After correcting for age, sex, disease duration, disability, disease-modifying therapy and LVV at index, and follow-up time, MS patients with DP (n = 671) had significantly greater absolute LVV change compared to stable (n = 1,501) or disability improved (DI, n = 248) MS patients (2.0 mL vs. 1.4 mL vs. 1.1 mL, respectively, ANCOVA p < 0.001, post-hoc pair-wise DP vs. Stable p = 0.003; and DP vs. DI, p = 0.002). Similar ANCOVA model was also significant for SCLV (p = 0.03). CONCLUSIONS: LVV-based atrophy and SCLV-based lesion outcomes are feasible on clinically acquired T2-FLAIR scans in a multicenter fashion and are associated with DP over mid-term.
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    Artificial intelligence for classification of temporal lobe epilepsy with ROI-level MRI data: A worldwide ENIGMA-Epilepsy study
    Gleichgerrcht, E ; Munsell, BC ; Alhusaini, S ; Alvim, MKM ; Bargallo, N ; Bender, B ; Bernasconi, A ; Bernasconi, N ; Bernhardt, B ; Blackmon, K ; Caligiuri, ME ; Cendes, F ; Concha, L ; Desmond, PM ; Devinsky, O ; Doherty, CP ; Domin, M ; Duncan, JS ; Focke, NK ; Gambardella, A ; Gong, B ; Guerrini, R ; Hatton, SN ; Kalviainen, R ; Keller, SS ; Kochunov, P ; Kotikalapudi, R ; Kreilkamp, BAK ; Labate, A ; Langner, S ; Lariviere, S ; Lenge, M ; Lui, E ; Martin, P ; Mascalchi, M ; Meletti, S ; O'Brien, TJ ; Pardoe, HR ; Pariente, JC ; Rao, JX ; Richardson, MP ; Rodriguez-Cruces, R ; Ruber, T ; Sinclair, B ; Soltanian-Zadeh, H ; Stein, DJ ; Striano, P ; Taylor, PN ; Thomas, RH ; Vaudano, AE ; Vivash, L ; von Podewills, F ; Vos, SB ; Weber, B ; Yao, Y ; Yasuda, CL ; Zhang, J ; Thompson, PM ; Sisodiya, SM ; McDonald, CR ; Bonilha, L (ELSEVIER SCI LTD, 2021-07-30)
    Artificial intelligence has recently gained popularity across different medical fields to aid in the detection of diseases based on pathology samples or medical imaging findings. Brain magnetic resonance imaging (MRI) is a key assessment tool for patients with temporal lobe epilepsy (TLE). The role of machine learning and artificial intelligence to increase detection of brain abnormalities in TLE remains inconclusive. We used support vector machine (SV) and deep learning (DL) models based on region of interest (ROI-based) structural (n = 336) and diffusion (n = 863) brain MRI data from patients with TLE with ("lesional") and without ("non-lesional") radiographic features suggestive of underlying hippocampal sclerosis from the multinational (multi-center) ENIGMA-Epilepsy consortium. Our data showed that models to identify TLE performed better or similar (68-75%) compared to models to lateralize the side of TLE (56-73%, except structural-based) based on diffusion data with the opposite pattern seen for structural data (67-75% to diagnose vs. 83% to lateralize). In other aspects, structural and diffusion-based models showed similar classification accuracies. Our classification models for patients with hippocampal sclerosis were more accurate (68-76%) than models that stratified non-lesional patients (53-62%). Overall, SV and DL models performed similarly with several instances in which SV mildly outperformed DL. We discuss the relative performance of these models with ROI-level data and the implications for future applications of machine learning and artificial intelligence in epilepsy care.