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    Examining the structural correlates of amyloid‐beta in people with DLB
    Gajamange, S ; Yassi, N ; Chin, KS ; Desmond, PM ; Villemagne, VL ; Rowe, CC ; Watson, R (Wiley, 2021-12)
    Background Dementia with Lewy bodies (DLB) is a neurodegenerative disorder characterized pathologically by the deposition of alpha synuclein. Many patients with DLB also have brain compatible with Alzheimer’s disease (namely Amyloid‐β and tau), which can lead to challenges with clinical diagnosis and management. In this study we aim to understand the influence of Aβ on brain atrophy in DLB patients. Method 19 participants with probable DLB underwent 3T MRI T1‐weighted (voxel size=0.8x0.8x0.8mm3, TR=2400ms, TE=2.31ms) and β‐amyloid (Aβ) PET (radiotracer 18F‐NAV4694) imaging. Participants were grouped into Aβ negative (n=10; age=71.6±5.8 years) and Aβ positive (n=9; age=75.1±4.3 years) with a threshold of 50 centiloid units to identify neuropathological change (Amadoru et al. 2020). Brain volume measures (regional subcortical grey matter and global white and grey matter) were segmented from T1‐weighted images with FreeSurfer (Fischl et al. 2002, Fischl 2012). Given previous literature suggesting prominence of thalamic structural changes in DLB, we also specifically analysed changes in the thalamus by segmenting the thalamus into 25 nuclei, which were then grouped into six regions (anterior, lateral, ventral, intralaminar, medial and posterior) (Watson et al. 2017, Iglesias et al. 2018). All brain volumes were expressed as fractions of intracranial volume to account for differences in head size. Group comparison analyses were not controlled for age and sex as both these covariates did not statistically differ between groups. Result Brain volume differed significantly between Aβ‐ and Aβ+ DLB patients in the left thalamus (Aβ‐:4.39±0.37x103, Aβ+:4.07±0.19x103, p=0.03) and right thalamus (Aβ‐:4.17±0.34x103, Aβ+:3.84±0.22 x103, p=0.03). Specifically, the ventral (LEFT; Aβ‐:1.78±0.15, Aβ+:1.63±0.14, p=0.03. RIGHT; Aβ‐:1.83±0.15, Aβ+:1.65±0.12, p=0.01) and posterior (LEFT; Aβ‐:1.30±0.12, Aβ+:1.17±0.10, p=0.04. RIGHT; Aβ‐:1.42±0.14, Aβ+:1.21±0.12, p=0.003) regions were significantly reduced in Aβ+ compared to Aβ‐ DLB patients. Conclusion We demonstrated significant thalamic atrophy in Aβ+ patients compared to Aβ‐ DLB patients. We did not observe significant differences in grey matter and hippocampal volume between patient groups. This study showed that AD‐related processes in DLB patients are associated with thalamic atrophy, specifically in the ventral and posterior regions. Future studies would benefit a larger DLB cohort to further understand the association between AD‐related pathology and the regional thalamic correlates of clinical function.
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    Interpretable surface-based detection of focal cortical dysplasias: a Multi-centre Epilepsy Lesion Detection study
    Spitzer, H ; Ripart, M ; Whitaker, K ; D'Arco, F ; Mankad, K ; Chen, AA ; Napolitano, A ; De Palma, L ; De Benedictis, A ; Foldes, S ; Humphreys, Z ; Zhang, K ; Hu, W ; Mo, J ; Likeman, M ; Davies, S ; Guttler, C ; Lenge, M ; Cohen, NT ; Tang, Y ; Wang, S ; Chari, A ; Tisdall, M ; Bargallo, N ; Conde-Blanco, E ; Pariente, JC ; Pascual-Diaz, S ; Delgado-Martinez, I ; Perez-Enriquez, C ; Lagorio, I ; Abela, E ; Mullatti, N ; O'Muircheartaigh, J ; Vecchiato, K ; Liu, Y ; Caligiuri, ME ; Sinclair, B ; Vivash, L ; Willard, A ; Kandasamy, J ; McLellan, A ; Sokol, D ; Semmelroch, M ; Kloster, AG ; Opheim, G ; Ribeiro, L ; Yasuda, C ; Rossi-Espagnet, C ; Hamandi, K ; Tietze, A ; 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, LH ; Cendes, F ; Theis, FJ ; Shinohara, RT ; Cross, JH ; Baldeweg, T ; Adler, S ; Wagstyl, K (OXFORD UNIV PRESS, 2022-11-21)
    One outstanding challenge for machine learning in diagnostic biomedical imaging is algorithm interpretability. A key application is the identification of subtle epileptogenic focal cortical dysplasias (FCDs) from structural MRI. FCDs are difficult to visualize on structural MRI but are often amenable to surgical resection. We aimed to develop an open-source, interpretable, surface-based machine-learning algorithm to automatically identify FCDs on heterogeneous structural MRI data from epilepsy surgery centres worldwide. The Multi-centre Epilepsy Lesion Detection (MELD) Project collated and harmonized a retrospective MRI cohort of 1015 participants, 618 patients with focal FCD-related epilepsy and 397 controls, from 22 epilepsy centres worldwide. We created a neural network for FCD detection based on 33 surface-based features. The network was trained and cross-validated on 50% of the total cohort and tested on the remaining 50% as well as on 2 independent test sites. Multidimensional feature analysis and integrated gradient saliencies were used to interrogate network performance. Our pipeline outputs individual patient reports, which identify the location of predicted lesions, alongside their imaging features and relative saliency to the classifier. On a restricted 'gold-standard' subcohort of seizure-free patients with FCD type IIB who had T1 and fluid-attenuated inversion recovery MRI data, the MELD FCD surface-based algorithm had a sensitivity of 85%. Across the entire withheld test cohort the sensitivity was 59% and specificity was 54%. After including a border zone around lesions, to account for uncertainty around the borders of manually delineated lesion masks, the sensitivity was 67%. This multicentre, multinational study with open access protocols and code has developed a robust and interpretable machine-learning algorithm for automated detection of focal cortical dysplasias, giving physicians greater confidence in the identification of subtle MRI lesions in individuals with epilepsy.
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    Event-based modeling in temporal lobe epilepsy demonstrates progressive atrophy from cross-sectional data
    Lopez, SM ; Aksman, LM ; Oxtoby, NP ; Vos, SB ; Rao, J ; Kaestner, E ; Alhusaini, S ; Alvim, M ; Bender, B ; Bernasconi, A ; Bernasconi, N ; Bernhardt, B ; Bonilha, L ; Caciagli, L ; Caldairou, B ; Caligiuri, ME ; Calvet, A ; Cendes, F ; Concha, L ; Conde-Blanco, E ; Davoodi-Bojd, E ; de Bezenac, C ; Delanty, N ; Desmond, PM ; Devinsky, O ; Domin, M ; Duncan, JS ; Focke, NK ; Foley, S ; Fortunato, F ; Galovic, M ; Gambardella, A ; Gleichgerrcht, E ; Guerrini, R ; Hamandi, K ; Ives-Deliperi, V ; Jackson, GD ; Jahanshad, N ; Keller, SS ; Kochunov, P ; Kotikalapudi, R ; Kreilkamp, BAK ; Labate, A ; Lariviere, S ; Lenge, M ; Lui, E ; Malpas, C ; Martin, P ; Mascalchi, M ; Medland, SE ; Meletti, S ; Morita-Sherman, ME ; Owen, TW ; Richardson, M ; Riva, A ; Ruber, T ; Sinclair, B ; Soltanian-Zadeh, H ; Stein, DJ ; Striano, P ; Taylor, PN ; Thomopoulos, SI ; Thompson, PM ; Tondelli, M ; Vaudano, AE ; Vivash, L ; Wang, Y ; Weber, B ; Whelan, CD ; Wiest, R ; Winston, GP ; Yasuda, CL ; McDonald, CR ; Alexander, DC ; Sisodiya, SM ; Altmann, A (WILEY, 2022-08)
    OBJECTIVE: Recent work has shown that people with common epilepsies have characteristic patterns of cortical thinning, and that these changes may be progressive over time. Leveraging a large multicenter cross-sectional cohort, we investigated whether regional morphometric changes occur in a sequential manner, and whether these changes in people with mesial temporal lobe epilepsy and hippocampal sclerosis (MTLE-HS) correlate with clinical features. METHODS: We extracted regional measures of cortical thickness, surface area, and subcortical brain volumes from T1-weighted (T1W) magnetic resonance imaging (MRI) scans collected by the ENIGMA-Epilepsy consortium, comprising 804 people with MTLE-HS and 1625 healthy controls from 25 centers. Features with a moderate case-control effect size (Cohen d ≥ .5) were used to train an event-based model (EBM), which estimates a sequence of disease-specific biomarker changes from cross-sectional data and assigns a biomarker-based fine-grained disease stage to individual patients. We tested for associations between EBM disease stage and duration of epilepsy, age at onset, and antiseizure medicine (ASM) resistance. RESULTS: In MTLE-HS, decrease in ipsilateral hippocampal volume along with increased asymmetry in hippocampal volume was followed by reduced thickness in neocortical regions, reduction in ipsilateral thalamus volume, and finally, increase in ipsilateral lateral ventricle volume. EBM stage was correlated with duration of illness (Spearman ρ = .293, p = 7.03 × 10-16 ), age at onset (ρ = -.18, p = 9.82 × 10-7 ), and ASM resistance (area under the curve = .59, p = .043, Mann-Whitney U test). However, associations were driven by cases assigned to EBM Stage 0, which represents MTLE-HS with mild or nondetectable abnormality on T1W MRI. SIGNIFICANCE: From cross-sectional MRI, we reconstructed a disease progression model that highlights a sequence of MRI changes that aligns with previous longitudinal studies. This model could be used to stage MTLE-HS subjects in other cohorts and help establish connections between imaging-based progression staging and clinical features.
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    Prevalence and Significance of Impaired Microvascular Tissue Reperfusion Despite Macrovascular Angiographic Reperfusion (No-Reflow)
    Ng, FC ; Churilov, L ; Yassi, N ; Kleinig, TJ ; Thijs, V ; Wu, T ; Shah, D ; Dewey, H ; Sharma, G ; Desmond, P ; Yan, B ; Parsons, M ; Donnan, G ; Davis, S ; Mitchell, P ; Campbell, B (LIPPINCOTT WILLIAMS & WILKINS, 2022-02-22)
    BACKGROUND AND OBJECTIVES: The relevance of impaired microvascular tissue-level reperfusion despite complete upstream macrovascular angiographic reperfusion (no-reflow) in human stroke remains controversial. We investigated the prevalence and clinical-radiologic features of this phenomenon and its associations with outcomes in 3 international randomized controlled thrombectomy trials with prespecified follow-up perfusion imaging. METHODS: In a pooled analysis of the Extending the Time for Thrombolysis in Emergency Neurological Deficits-Intra-Arterial (EXTEND-IA; ClinicalTrials.gov NCT01492725), Tenecteplase Versus Alteplase Before Endovascular Therapy for Ischemic Stroke (EXTEND-IA TNK; NCT02388061), and Determining the Optimal Dose of Tenecteplase Before Endovascular Therapy for Ischaemic Stroke (EXTEND-IA TNK Part 2; NCT03340493) trials, patients undergoing thrombectomy with final angiographic expanded Treatment in Cerebral Infarction score of 2c to 3 score for anterior circulation large vessel occlusion and 24-hour follow-up CT or MRI perfusion imaging were included. No-reflow was defined as regions of visually demonstrable persistent hypoperfusion on relative cerebral blood volume or flow maps within the infarct and verified quantitatively by >15% asymmetry compared to a mirror homolog in the absence of carotid stenosis or reocclusion. RESULTS: Regions of no-reflow were identified in 33 of 130 patients (25.3%), encompassed a median of 60.2% (interquartile range 47.8%-70.7%) of the infarct volume, and involved both subcortical (n = 26 of 33, 78.8%) and cortical (n = 10 of 33, 30.3%) regions. Patients with no-reflow had a median 25.2% (interquartile range 16.4%-32.2%, p < 0.00001) relative cerebral blood volume interside reduction and 19.1% (interquartile range 3.9%-28.3%, p = 0.00011) relative cerebral blood flow reduction but similar mean transit time (median -3.3%, interquartile range -11.9% to 24.4%, p = 0.24) within the infarcted region. Baseline characteristics were similar between patients with and those without no-reflow. The presence of no-reflow was associated with hemorrhagic transformation (adjusted odds ratio [aOR] 1.79, 95% confidence interval [CI] 2.32-15.57, p = 0.0002), greater infarct growth (β = 11.00, 95% CI 5.22-16.78, p = 0.00027), reduced NIH Stroke Scale score improvement at 24 hours (β = -4.06, 95% CI 6.78-1.34, p = 0.004) and being dependent or dead at 90 days as assessed by the modified Rankin Scale (aOR 3.72, 95% CI 1.35-10.20, p = 0.011) in multivariable analysis. DISCUSSION: Cerebral no-reflow in humans is common, can be detected by its characteristic perfusion imaging profile using readily available sequences in the clinical setting, and is associated with posttreatment complications and being dependent or dead. Further studies evaluating the role of no-reflow in secondary injury after angiographic reperfusion are warranted. CLASSIFICATION OF EVIDENCE: This study provides Class II evidence that cerebral no-reflow on CT/MRI perfusion imaging at 24 hours is associated with posttreatment complications and poor 3-month functional outcome.
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    In Vivo 7-Tesla MRI Investigation of Brain Iron and Its Metabolic Correlates in Chronic Schizophrenia
    Ravanfar, P ; Syeda, WT ; Jayaram, M ; Rushmore, RJ ; Moffat, B ; Lin, AP ; Lyall, AE ; Merritt, AH ; Yaghmaie, N ; Laskaris, L ; Luza, S ; Opazo, CM ; Liberg, B ; Chakravarty, MM ; Devenyi, GA ; Desmond, P ; Cropley, VL ; Makris, N ; Shenton, ME ; Bush, A ; Velakoulis, D ; Pantelis, C (NATURE PORTFOLIO, 2022-10-26)
    Brain iron is central to dopaminergic neurotransmission, a key component in schizophrenia pathology. Iron can also generate oxidative stress, which is one proposed mechanism for gray matter volume reduction in schizophrenia. The role of brain iron in schizophrenia and its potential link to oxidative stress has not been previously examined. In this study, we used 7-Tesla MRI quantitative susceptibility mapping (QSM), magnetic resonance spectroscopy (MRS), and structural T1 imaging in 12 individuals with chronic schizophrenia and 14 healthy age-matched controls. In schizophrenia, there were higher QSM values in bilateral putamen and higher concentrations of phosphocreatine and lactate in caudal anterior cingulate cortex (caCC). Network-based correlation analysis of QSM across corticostriatal pathways as well as the correlation between QSM, MRS, and volume, showed distinct patterns between groups. This study introduces increased iron in the putamen in schizophrenia in addition to network-wide disturbances of iron and metabolic status.
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    Structural network alterations in focal and generalized epilepsy assessed in a worldwide ENIGMA study follow axes of epilepsy risk gene expression
    Lariviere, S ; Royer, J ; Rodriguez-Cruces, R ; Paquola, C ; Caligiuri, ME ; Gambardella, A ; Concha, L ; Keller, SS ; Cendes, F ; Yasuda, CL ; Bonilha, L ; Gleichgerrcht, E ; Focke, NK ; Domin, M ; von Podewills, F ; Langner, S ; Rummel, C ; Wiest, R ; Martin, P ; Kotikalapudi, R ; O'Brien, TJ ; Sinclair, B ; Vivash, L ; Desmond, PM ; Lui, E ; Vaudano, AE ; Meletti, S ; Tondelli, M ; 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 ; Winston, GP ; Griffin, A ; Singh, A ; Tiwari, VK ; Kreilkamp, BAK ; Lenge, M ; Guerrini, R ; Hamandi, K ; Foley, S ; Ruber, T ; Weber, B ; Depondt, C ; Absil, J ; Carr, SJA ; Abela, E ; Richardson, MP ; Devinsky, O ; Severino, M ; Striano, P ; Tortora, D ; Kaestner, E ; Hatton, SN ; Vos, SB ; Caciagli, L ; Duncan, JS ; Whelan, CD ; Thompson, PM ; Sisodiya, SM ; Bernasconi, A ; Labate, A ; McDonald, CR ; Bernasconi, N ; Bernhardt, BC (NATURE PORTFOLIO, 2022-07-27)
    Epilepsy is associated with genetic risk factors and cortico-subcortical network alterations, but associations between neurobiological mechanisms and macroscale connectomics remain unclear. This multisite ENIGMA-Epilepsy study examined whole-brain structural covariance networks in patients with epilepsy and related findings to postmortem epilepsy risk gene expression patterns. Brain network analysis included 578 adults with temporal lobe epilepsy (TLE), 288 adults with idiopathic generalized epilepsy (IGE), and 1328 healthy controls from 18 centres worldwide. Graph theoretical analysis of structural covariance networks revealed increased clustering and path length in orbitofrontal and temporal regions in TLE, suggesting a shift towards network regularization. Conversely, people with IGE showed decreased clustering and path length in fronto-temporo-parietal cortices, indicating a random network configuration. Syndrome-specific topological alterations reflected expression patterns of risk genes for hippocampal sclerosis in TLE and for generalized epilepsy in IGE. These imaging-transcriptomic signatures could potentially guide diagnosis or tailor therapeutic approaches to specific epilepsy syndromes.
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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, 2021-06)
    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, 2022-05)
    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, 2022-08)
    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 ).