Abstract：
OBJECTIVE: Temporal lobe epilepsy (TLE) is assumed to follow a steady course that is similar across patients. To date, phenotypic and temporal diversities of TLE evolution remain unknown. In this study, we aimed at simultaneously characterizing these sources of variability based on cross-sectional data.
METHODS: We studied consecutive patients with TLE referred for evaluation by neurologists to the Montreal Neurological Institute epilepsy clinic, who underwent in-patient video EEG monitoring and multimodal imaging at 3 Tesla, comprising 3D T1 and fluid-attenuated inversion recovery and 2D diffusion-weighted MRI. The cohort included patients with drug-resistant epilepsy and patients with drug-responsive epilepsy. The neuropsychological evaluation included Wechsler Adult Intelligence Scale-III and Leonard tapping task. The control group consisted of participants without TLE recruited through advertisement and who underwent the same MRI acquisition as patients. Based on surface-based analysis of key MRI markers of pathology (gray matter morphology and white matter microstructure), the Subtype and Stage Inference algorithm estimated subtypes and stages of brain pathology to which individual patients were assigned. The number of subtypes was determined by running the algorithm 100 times and estimating mean and SD of disease trajectories and the consistency of patients\' assignments based on 1,000 bootstrap samples. Effect of normal aging was subtracted from patients. We examined associations with clinical and cognitive parameters and utility for individualized predictions.
RESULTS: We studied 82 patients with TLE (52 female, mean age 35 ± 10 years; 11 drug-responsive) and 41 control participants (23 male, mean age 32 ± 8 years). Among 57 operated, 43/37/20 had Engel-I outcome/hippocampal sclerosis/hippocampal isolated gliosis, respectively. We identified 3 trajectory subtypes: S1 (n = 35), led by ipsilateral hippocampal atrophy and gliosis, followed by white-matter damage; S2 (n = 27), characterized by bilateral neocortical atrophy, followed by ipsilateral hippocampal atrophy and gliosis; and S3 (n = 20), typified by bilateral limbic white-matter damage, followed by bilateral hippocampal gliosis. Patients showed high assignability to their subtypes and stages (>90% bootstrap agreement). S1 had the highest proportions of patients with early disease onset (effect size d = 0.27 vs S2, d = 0.73 vs S3), febrile convulsions (χ2 = 3.70), drug resistance (χ2 = 2.94), a positive MRI (χ2 = 8.42), hippocampal sclerosis (χ2 = 7.57), and Engel-I outcome (χ2 = 1.51), pFDR < 0.05 across all comparisons. S2 and S3 exhibited the intermediate and lowest proportions, respectively. Verbal IQ and digit span were lower in S1 (d = 0.65 and d = 0.50, pFDR < 0.05) and S2 (d = 0.76 and d = 1.09, pFDR < 0.05), compared with S3. We observed progressive decline in sequential motor tapping in S1 and S3 (T = -3.38 and T = -4.94, pFDR = 0.027), compared with S2 (T = 2.14, pFDR = 0.035). S3 showed progressive decline in digit span (T = -5.83, p = 0.021). Supervised classifiers trained on subtype and stage outperformed subtype-only and stage-only models predicting drug response in 73% ± 1.0% (vs 70% ± 1.4% and 63% ± 1.3%) and 76% ± 1.6% for Engel-I outcome (vs 71% ± 0.8% and 72% ± 1.1%), pFDR < 0.05 across all comparisons.
CONCLUSIONS: Cross-sectional MRI-derived models provide reliable prognostic markers of TLE disease evolution, which follows distinct trajectories, each associated with divergent patterns of hippocampal and whole-brain structural alterations, as well as cognitive and clinical profiles.
METHODS: We studied consecutive patients with TLE referred for evaluation by neurologists to the Montreal Neurological Institute epilepsy clinic, who underwent in-patient video EEG monitoring and multimodal imaging at 3 Tesla, comprising 3D T1 and fluid-attenuated inversion recovery and 2D diffusion-weighted MRI. The cohort included patients with drug-resistant epilepsy and patients with drug-responsive epilepsy. The neuropsychological evaluation included Wechsler Adult Intelligence Scale-III and Leonard tapping task. The control group consisted of participants without TLE recruited through advertisement and who underwent the same MRI acquisition as patients. Based on surface-based analysis of key MRI markers of pathology (gray matter morphology and white matter microstructure), the Subtype and Stage Inference algorithm estimated subtypes and stages of brain pathology to which individual patients were assigned. The number of subtypes was determined by running the algorithm 100 times and estimating mean and SD of disease trajectories and the consistency of patients\' assignments based on 1,000 bootstrap samples. Effect of normal aging was subtracted from patients. We examined associations with clinical and cognitive parameters and utility for individualized predictions.
RESULTS: We studied 82 patients with TLE (52 female, mean age 35 ± 10 years; 11 drug-responsive) and 41 control participants (23 male, mean age 32 ± 8 years). Among 57 operated, 43/37/20 had Engel-I outcome/hippocampal sclerosis/hippocampal isolated gliosis, respectively. We identified 3 trajectory subtypes: S1 (n = 35), led by ipsilateral hippocampal atrophy and gliosis, followed by white-matter damage; S2 (n = 27), characterized by bilateral neocortical atrophy, followed by ipsilateral hippocampal atrophy and gliosis; and S3 (n = 20), typified by bilateral limbic white-matter damage, followed by bilateral hippocampal gliosis. Patients showed high assignability to their subtypes and stages (>90% bootstrap agreement). S1 had the highest proportions of patients with early disease onset (effect size d = 0.27 vs S2, d = 0.73 vs S3), febrile convulsions (χ2 = 3.70), drug resistance (χ2 = 2.94), a positive MRI (χ2 = 8.42), hippocampal sclerosis (χ2 = 7.57), and Engel-I outcome (χ2 = 1.51), pFDR < 0.05 across all comparisons. S2 and S3 exhibited the intermediate and lowest proportions, respectively. Verbal IQ and digit span were lower in S1 (d = 0.65 and d = 0.50, pFDR < 0.05) and S2 (d = 0.76 and d = 1.09, pFDR < 0.05), compared with S3. We observed progressive decline in sequential motor tapping in S1 and S3 (T = -3.38 and T = -4.94, pFDR = 0.027), compared with S2 (T = 2.14, pFDR = 0.035). S3 showed progressive decline in digit span (T = -5.83, p = 0.021). Supervised classifiers trained on subtype and stage outperformed subtype-only and stage-only models predicting drug response in 73% ± 1.0% (vs 70% ± 1.4% and 63% ± 1.3%) and 76% ± 1.6% for Engel-I outcome (vs 71% ± 0.8% and 72% ± 1.1%), pFDR < 0.05 across all comparisons.
CONCLUSIONS: Cross-sectional MRI-derived models provide reliable prognostic markers of TLE disease evolution, which follows distinct trajectories, each associated with divergent patterns of hippocampal and whole-brain structural alterations, as well as cognitive and clinical profiles.
摘要：
目的：假定颞叶癫痫（TLE）遵循一个稳定的病程，在不同患者中相似。迄今为止,TLE进化的表型和时间多样性仍然未知。在这项研究中,我们旨在基于横截面数据同时表征这些变异性来源.
方法：我们研究了由神经科医师转诊到蒙特利尔神经病学研究所癫痫诊所进行评估的连续TLE患者，在3特斯拉时接受了患者视频脑电图监测和多模态成像，包括3DT1和流体衰减反演恢复和2D扩散加权MRI。该队列包括耐药癫痫患者和药物反应性癫痫患者。神经心理学评估包括韦克斯勒成人智力量表III和伦纳德攻丝任务。对照组由没有通过广告招募的TLE的参与者组成，他们接受了与患者相同的MRI采集。基于病理学的关键MRI标志物（灰质形态和白质微观结构）的表面分析，亚型和分期推断算法估计了分配给各个患者的脑病理亚型和分期.通过运行算法100次并基于1,000个自举样本估计疾病轨迹的平均值和SD以及患者分配的一致性来确定亚型的数量。从患者中减去正常老化的影响。我们检查了与临床和认知参数以及个性化预测的实用性的关联。
结果：我们研究了82例TLE患者（52例女性，平均年龄35±10岁；11名药物反应型）和41名对照参与者（23名男性，平均年龄32±8岁)。在57个运营中，43/37/20有Engel-I结果/海马硬化/海马孤立性胶质增生，分别。我们确定了3种轨迹亚型：S1(n=35)，同侧海马萎缩和胶质增生，其次是白质损伤；S2(n=27)，以双侧新皮质萎缩为特征，其次是同侧海马萎缩和神经胶质增生；和S3（n=20），以双侧边缘白质损伤为代表，其次是双侧海马胶质增生。患者对其亚型和阶段表现出很高的可分配性（>90％的自举一致性）。S1的早期疾病发作患者比例最高（效应大小d=0.27vsS2，d=0.73vsS3），高热惊厥（χ2=3.70），耐药性（χ2=2.94），MRI阳性（χ2=8.42），海马硬化（χ2=7.57），和Engel-I结局（χ2=1.51），所有比较的pFDR<0.05。S2和S3表现出中间和最低的比例，分别。S1（d=0.65和d=0.50，pFDR<0.05）和S2（d=0.76和d=1.09，pFDR<0.05）的言语智商和数字跨度较低，与S3相比。我们观察到S1和S3的顺序运动敲击逐渐下降（T=-3.38和T=-4.94，pFDR=0.027），与S2相比(T=2.14,pFDR=0.035)。S3显示手指跨度逐渐下降（T=-5.83，p=0.021）。在亚型和阶段训练的监督分类器优于仅亚型和仅阶段模型，预测Engel-I结局的药物反应为73％±1.0％（vs70％±1.4％和63％±1.3％）和76％±1.6％（vs71％±0.8％和72％±1.1％），所有比较的pFDR<0.05。
结论：横断面MRI衍生模型提供了TLE疾病演变的可靠预后标志物，遵循不同的轨迹，每个都与海马和全脑结构改变的不同模式相关，以及认知和临床资料。
方法：我们研究了由神经科医师转诊到蒙特利尔神经病学研究所癫痫诊所进行评估的连续TLE患者，在3特斯拉时接受了患者视频脑电图监测和多模态成像，包括3DT1和流体衰减反演恢复和2D扩散加权MRI。该队列包括耐药癫痫患者和药物反应性癫痫患者。神经心理学评估包括韦克斯勒成人智力量表III和伦纳德攻丝任务。对照组由没有通过广告招募的TLE的参与者组成，他们接受了与患者相同的MRI采集。基于病理学的关键MRI标志物（灰质形态和白质微观结构）的表面分析，亚型和分期推断算法估计了分配给各个患者的脑病理亚型和分期.通过运行算法100次并基于1,000个自举样本估计疾病轨迹的平均值和SD以及患者分配的一致性来确定亚型的数量。从患者中减去正常老化的影响。我们检查了与临床和认知参数以及个性化预测的实用性的关联。
结果：我们研究了82例TLE患者（52例女性，平均年龄35±10岁；11名药物反应型）和41名对照参与者（23名男性，平均年龄32±8岁)。在57个运营中，43/37/20有Engel-I结果/海马硬化/海马孤立性胶质增生，分别。我们确定了3种轨迹亚型：S1(n=35)，同侧海马萎缩和胶质增生，其次是白质损伤；S2(n=27)，以双侧新皮质萎缩为特征，其次是同侧海马萎缩和神经胶质增生；和S3（n=20），以双侧边缘白质损伤为代表，其次是双侧海马胶质增生。患者对其亚型和阶段表现出很高的可分配性（>90％的自举一致性）。S1的早期疾病发作患者比例最高（效应大小d=0.27vsS2，d=0.73vsS3），高热惊厥（χ2=3.70），耐药性（χ2=2.94），MRI阳性（χ2=8.42），海马硬化（χ2=7.57），和Engel-I结局（χ2=1.51），所有比较的pFDR<0.05。S2和S3表现出中间和最低的比例，分别。S1（d=0.65和d=0.50，pFDR<0.05）和S2（d=0.76和d=1.09，pFDR<0.05）的言语智商和数字跨度较低，与S3相比。我们观察到S1和S3的顺序运动敲击逐渐下降（T=-3.38和T=-4.94，pFDR=0.027），与S2相比(T=2.14,pFDR=0.035)。S3显示手指跨度逐渐下降（T=-5.83，p=0.021）。在亚型和阶段训练的监督分类器优于仅亚型和仅阶段模型，预测Engel-I结局的药物反应为73％±1.0％（vs70％±1.4％和63％±1.3％）和76％±1.6％（vs71％±0.8％和72％±1.1％），所有比较的pFDR<0.05。
结论：横断面MRI衍生模型提供了TLE疾病演变的可靠预后标志物，遵循不同的轨迹，每个都与海马和全脑结构改变的不同模式相关，以及认知和临床资料。
