Glymphatic System Activity and Brain Morphology in Patients With Psychogenic Non-epileptic Seizures.

BACKGROUND: To clarify the neural correlates underlying psychogenic non-epileptic seizures (PNES), we compared glymphatic system activity between patients with PNES and healthy participants using diffusion tensor imaging (DTI)-analysis along the perivascular space (ALPS) method. METHODS: The DTI scans were acquired from 16 patients with PNES and 25 healthy participants. We computed the DTI-ALPS index as an index of glymphatic system function and estimated the disease-related changes in the DTI-ALPS index and brain structures in PNES patients. RESULTS: There were no significant differences in the DTI-ALPS index between patients with PNES and healthy participants. On the other hand, patients with PNES had decreased fractional anisotropy values in the bilateral posterior cingula, a higher mean diffusivity value around the left insula, and a lower gray matter volume in the bilateral amygdalae compared with healthy participants. CONCLUSIONS: Patients with PNES exhibited an impairment of white matter integrity and a reduction of gray matter volume, but no glymphatic-system changes. These findings will play a significant role in our comprehension of this complex illness.

Disease Progression Patterns of Brain Morphology in Schizophrenia: More Progressed Stages in Treatment Resistance.

BACKGROUND AND HYPOTHESIS: Given the heterogeneity and possible disease progression in schizophrenia, identifying the neurobiological subtypes and progression patterns in each patient may lead to novel biomarkers. Here, we adopted data-driven machine-learning techniques to identify the progression patterns of brain morphological changes in schizophrenia and investigate the association with treatment resistance. STUDY DESIGN: In this cross-sectional multicenter study, we included 177 patients with schizophrenia, characterized by treatment response or resistance, with 3D T1-weighted magnetic resonance imaging. Cortical thickness and subcortical volumes calculated by FreeSurfer were converted into z scores using 73 healthy controls data. The Subtype and Stage Inference (SuStaIn) algorithm was used for unsupervised machine-learning analysis. STUDY RESULTS: SuStaIn identified 3 different subtypes: (1) subcortical volume reduction (SC) type (73 patients), in which volume reduction of subcortical structures occurs first and moderate cortical thinning follows, (2) globus pallidus hypertrophy and cortical thinning (GP-CX) type (42 patients), in which globus pallidus hypertrophy initially occurs followed by progressive cortical thinning, and (3) cortical thinning (pure CX) type (39 patients), in which thinning of the insular and lateral temporal lobe cortices primarily happens. The remaining 23 patients were assigned to baseline stage of progression (no change). SuStaIn also found 84 stages of progression, and treatment-resistant schizophrenia showed significantly more progressed stages than treatment-responsive cases (P = .001). The GP-CX type presented earlier stages than the pure CX type (P = .009). CONCLUSIONS: The brain morphological progressions in schizophrenia can be classified into 3 subtypes, and treatment resistance was associated with more progressed stages, which may suggest a novel biomarker.

Identification of different MRI atrophy progression trajectories in epilepsy by subtype and stage inference.

Artificial intelligence (AI)-based tools are widely employed, but their use for diagnosis and prognosis of neurological disorders is still evolving. Here we analyse a cross-sectional multicentre structural MRI dataset of 696 people with epilepsy and 118 control subjects. We use an innovative machine-learning algorithm, Subtype and Stage Inference, to develop a novel data-driven disease taxonomy, whereby epilepsy subtypes correspond to distinct patterns of spatiotemporal progression of brain atrophy.In a discovery cohort of 814 individuals, we identify two subtypes common to focal and idiopathic generalized epilepsies, characterized by progression of grey matter atrophy driven by the cortex or the basal ganglia. A third subtype, only detected in focal epilepsies, was characterized by hippocampal atrophy. We corroborate external validity via an independent cohort of 254 people and confirm that the basal ganglia subtype is associated with the most severe epilepsy.Our findings suggest fundamental processes underlying the progression of epilepsy-related brain atrophy. We deliver a novel MRI- and AI-guided epilepsy taxonomy, which could be used for individualized prognostics and targeted therapeutics.

White Matter Structural Connectivity and Its Impact on Psychogenic Non-Epileptic Seizures: An Evidence-Based Review.

Psychiatric non-epileptic seizure (PNES), also known as a form of functional neurological disorders (FND), is a common but still underrecognized disorder presenting seizure-like symptoms and no electrophysiological abnormality. Despite the significant burden of this disorder, the neurobiological mechanisms are not clearly understood, which hinders the development of better diagnosis and treatment. In the recent neuroimaging research on PNES, brain network analysis has become a relevant topic beyond conventional methodologies. The human brain is a highly intricate system of interconnected regions that collaborate to facilitate a wide range of cognitive and behavioral functions. White matter tracts, which are comprised of bundles of axonal fibers, are the primary means by which information is transmitted between different brain regions. As such, comprehending the organization and structure of the brain's white matter network is critical for gaining insight into its functional architecture. This review article aims to provide an overview of the brain mechanisms underlying PNES, with a special focus on analyzing brain networks.

[Epilepsy in the Elderly Population: Clinical Practice and Recent Updates].

Optimal medical care and welfare is an urgent issue among the elderly population in a rapidly aging society. Epilepsy is a common chronic neurological disorder that affects all ages with peaks observed in children and in elderly individuals. Considering rapid population aging, the prevalence of epilepsy in the elderly population is expected to increase in the future. Clinicians should be aware of the clinical features of epilepsy in elderly patients because these differ from those observed in young patients from several perspectives. In this article, we review the clinical practice and recent updates in epilepsy in the elderly population. Epilepsy in elderly patients requires careful attention to subtle seizure symptoms and electroencephalography findings, and this disorder is associated with a wide range of differential diagnosis. Treatment is primarily pharmacological, and seizures may often be well controlled. Etiologies include dementia and cerebrovascular disease; however, a few patients may present with epilepsy secondary to an unknown cause. Psychosocial issues such as driver's license and stigma are also important.

Age and Sex-Related Effects on Single-Subject Gray Matter Networks in Healthy Participants.

Recent developments in image analysis have enabled an individual's brain network to be evaluated and brain age to be predicted from gray matter images. Our study aimed to investigate the effects of age and sex on single-subject gray matter networks using a large sample of healthy participants. We recruited 812 healthy individuals (59.3 ± 14.0 years, 407 females, and 405 males) who underwent three-dimensional T1-weighted magnetic resonance imaging. Similarity-based gray matter networks were constructed, and the following network properties were calculated: normalized clustering, normalized path length, and small-world coefficients. The predicted brain age was computed using a support-vector regression model. We evaluated the network alterations related to age and sex. Additionally, we examined the correlations between the network properties and predicted brain age and compared them with the correlations between the network properties and chronological age. The brain network retained efficient small-world properties regardless of age; however, reduced small-world properties were observed with advancing age. Although women exhibited higher network properties than men and similar age-related network declines as men in the subjects aged < 70 years, faster age-related network declines were observed in women, leading to no differences in sex among the participants aged ≥ 70 years. Brain age correlated well with network properties compared to chronological age in participants aged ≥ 70 years. Although the brain network retained small-world properties, it moved towards randomized networks with aging. Faster age-related network disruptions in women were observed than in men among the elderly. Our findings provide new insights into network alterations underlying aging.

Neuropsychiatric aspects of long COVID: A comprehensive review.

Although some patients have persistent symptoms or develop new symptoms following coronavirus disease 2019 (COVID-19) infection, neuropsychiatric aspects of long COVID are not well known. This review summarizes and provides an update on the neuropsychiatric dimensions of long COVID. Its neuropsychiatric manifestations commonly include fatigue, cognitive impairment, sleep disorders, depression, anxiety, and post-traumatic stress disorder. There are no specific tests for long COVID, but some characteristic findings such as hypometabolism on positron emission tomography have been reported. The possible mechanisms of long COVID include inflammation, ischemic effects, direct viral invasion, and social and environmental changes. Some patient characteristics and the severity and complications of acute COVID-19 infection may be associated with an increased risk of neuropsychiatric symptoms. Long COVID may resolve spontaneously or persist, depending on the type of neuropsychiatric symptoms. Although established treatments are lacking, various psychological and pharmacological treatments have been attempted. Vaccination against COVID-19 infection plays a key role in the prevention of long coronavirus disease. With differences among the SARS-CoV-2 variants, including the omicron variant, the aspects of long COVID are likely to change in the future. Further studies clarifying the aspects of long COVID to develop effective treatments are warranted.

Contribution of the µ-opioid receptor system to affective disorders in temporal lobe epilepsy: A bidirectional relationship?

OBJECTIVE: Affective disorders are frequent comorbidities of temporal lobe epilepsy (TLE). The endogenous opioid system has been implicated in both epilepsy and affective disorders, and may play a significant role in their bidirectional relationship. In this cross-sectional study, we investigated the association between µ-opioid receptor binding and affective disorders in patients with TLE. METHODS: Nine patients with TLE and depression/anxiety underwent 11 C-carfentanil positron emission tomography (CFN PET) and neuropsychiatric assessment, including the Hospital Anxiety and Depression Scale and the Positive and Negative Affect Schedule. The normalized CFN PET scans were compared with those of 26 age-matched healthy controls. Correlation analyses with affective symptoms were performed by region of interest-based analysis focusing on the limbic circuit and orbitofrontal cortex. RESULTS: We observed widely reduced CFN binding potential (BP) in bilateral frontal lobes and striata in patients with TLE compared to healthy controls. In the TLE group, more severe anxiety and negative affect were associated with decreased CFN BP in the posterior cingulate gyrus. SIGNIFICANCE: In patients with TLE, interictally reduced binding in the opioid system was associated with higher levels of anxiety and negative affect. We speculate that seizure-related agonist-driven desensitization and downregulation of opioid receptors could be a potential underlying pathomechanism.

Neurobiological mechanisms of psychosis in epilepsy: Findings from neuroimaging studies.

Despite the high prevalence and clinical importance of comorbid psychosis in epilepsy, its neurobiological mechanisms remain understudied. This narrative mini-review aims to provide an overview of recent updates in in vivo neuroimaging studies on psychosis in epilepsy, including structural and diffusion magnetic resonance imaging (MRI) and functional and molecular imaging, and to discuss future directions in this field. While the conventional morphological analysis of structural MRI has provided relatively inconsistent results, advanced methods, including brain network analysis, hippocampal subregion volumetry, and machine learning models, have recently provided novel findings. Diffusion MRI, for example, has revealed a reduction in white matter integrity mainly in the frontal and temporal lobes, as well as a disruption of brain white matter networks. Functional neuroimaging, such as perfusion single-photon emission computed tomography (SPECT) or fluorodeoxyglucose positron emission tomography (FDG-PET), often identifies hyperactivity in various brain regions. The current limitations of these more recent studies may include small and sometimes heterogeneous samples, insufficient control groups, the effects of psychoactive drugs, and the lack of longitudinal analysis. Further investigations are required to establish novel treatments and identify clinical diagnostic or disease-monitoring biomarkers in psychosis in epilepsy.

Neuroimaging-Based Brain Age Estimation: A Promising Personalized Biomarker in Neuropsychiatry.

It is now possible to estimate an individual's brain age via brain scans and machine-learning models. This validated technique has opened up new avenues for addressing clinical questions in neurology, and, in this review, we summarize the many clinical applications of brain-age estimation in neuropsychiatry and general populations. We first provide an introduction to typical neuroimaging modalities, feature extraction methods, and machine-learning models that have been used to develop a brain-age estimation framework. We then focus on the significant findings of the brain-age estimation technique in the field of neuropsychiatry as well as the usefulness of the technique for addressing clinical questions in neuropsychiatry. These applications may contribute to more timely and targeted neuropsychiatric therapies. Last, we discuss the practical problems and challenges described in the literature and suggest some future research directions.

Free water derived by multi-shell diffusion MRI reflects tau/neuroinflammatory pathology in Alzheimer's disease.

Introduction: Free-water (FW) imaging, a new analysis method for diffusion magnetic resonance imaging (MRI), can indicate neuroinflammation and degeneration. We evaluated FW in Alzheimer's disease (AD) using tau/inflammatory and amyloid positron emission tomography (PET). Methods: Seventy-one participants underwent multi-shell diffusion MRI, 18F-THK5351 PET, 11C-Pittsburgh compound B PET, and neuropsychological assessments. They were categorized into two groups: healthy controls (HCs) (n = 40) and AD-spectrum group (AD-S) (n = 31) using the Centiloid scale with amyloid PET and cognitive function. We analyzed group comparisons in FW and PET, correlations between FW and PET, and correlation analysis with neuropsychological scores. Results: In AD-S group, there was a significant positive correlation between FW and 18F-THK5351 in the temporal lobes. In addition, there were negative correlations between FW and cognitive function in the temporal lobe and cingulate gyrus, and negative correlations between 18F-THK5351 and cognitive function in the same regions. Discussion: FW imaging could be a biomarker for tau in AD alongside clinical correlations.

Upper cerebellar glucose hypermetabolism in patients with temporal lobe epilepsy and interictal psychosis.

OBJECTIVE: Psychosis is an important comorbidity in epilepsy, but its pathophysiology is still unknown. The imaging modality 18 F-fluorodeoxyglucose-positron emission tomography (18 F-FDG PET) is widely used to measure brain glucose metabolism, and we speculated that 18 F-FDG PET may detect characteristic alteration patterns in individuals with temporal lobe epilepsy (TLE) and psychosis. METHODS: We enrolled 13 patients with TLE and interictal psychosis (TLE-P) and 21 patients with TLE without psychosis (TLE-N). All underwent interictal 18 F-FDG-PET scanning. Statistical Parametric Mapping (SPM)12 software was used for the normalization process, and we performed a voxel-wise comparison of the TLE-P and TLE-N groups. RESULTS: Cerebral hypometabolic areas were observed in the ipsilateral temporal pole to hippocampus in both patient groups. In the TLE-P group, the voxel-wise comparison revealed significantly increased 18 F-FDG signals in the upper cerebellum, superior cerebellar peduncle, and midbrain. There were no significant between-group metabolic differences around the focus or other cerebral areas. SIGNIFICANCE: Our results demonstrated significant hypermetabolism around the upper cerebellum in patients with TLE and interictal psychosis compared to patients with TLE without psychosis. These findings may reflect the involvement of the cerebellum in the underlying neurobiology of interictal psychosis and could contribute to a better understanding of this disorder.

Impact of COVID-19 pandemic on epilepsy care in Japan: A national-level multicenter retrospective cohort study.

OBJECTIVE: The impact of the coronavirus disease 2019 (COVID-19) pandemic on epilepsy care across Japan was investigated by conducting a multicenter retrospective cohort study. METHODS: This study included monthly data on the frequency of (1) visits by outpatients with epilepsy, (2) outpatient electroencephalography (EEG) studies, (3) telemedicine for epilepsy, (4) admissions for epilepsy, (5) EEG monitoring, and (6) epilepsy surgery in epilepsy centers and clinics across Japan between January 2019 and December 2020. We defined the primary outcome as epilepsy-center-specific monthly data divided by the 12-month average in 2019 for each facility. We determined whether the COVID-19 pandemic-related factors (such as year [2019 or 2020], COVID-19 cases in each prefecture in the previous month, and the state of emergency) were independently associated with these outcomes. RESULTS: In 2020, the frequency of outpatient EEG studies (-10.7%, p<0.001) and cases with telemedicine (+2,608%, p=0.031) were affected. The number of COVID-19 cases was an independent associated factor for epilepsy admission (-3.75*10-3 % per case, p<0.001) and EEG monitoring (-3.81*10-3 % per case, p = 0.004). Further, the state of emergency was an independent factor associated with outpatient with epilepsy (-11.9%, p<0.001), outpatient EEG (-32.3%, p<0.001), telemedicine for epilepsy (+12,915%, p<0.001), epilepsy admissions (-35.3%; p<0.001), EEG monitoring (-24.7%: p<0.001), and epilepsy surgery (-50.3%, p<0.001). SIGNIFICANCE: We demonstrated the significant impact that the COVID-19 pandemic had on epilepsy care. These results support those of previous studies and clarify the effect size of each pandemic-related factor on epilepsy care.

Neuroimaging-derived brain age is associated with life satisfaction in cognitively unimpaired elderly: A community-based study.

With the widespread increase in elderly populations, the quality of life and mental health in old age are issues of great interest. The human brain changes with age, and the brain aging process is biologically complex and varies widely among individuals. In this cross-sectional study, to clarify the effects of mental health, as well as common metabolic factors (e.g., diabetes) on healthy brain aging in late life, we analyzed structural brain MRI findings to examine the relationship between predicted brain age and life satisfaction, depressive symptoms, resilience, and lifestyle-related factors in elderly community-living individuals with unimpaired cognitive function. We extracted data from a community-based cohort study in Arakawa Ward, Tokyo. T1-weighted images of 773 elderly participants aged ≥65 years were analyzed, and the predicted brain age of each subject was calculated by machine learning from anatomically standardized gray-matter images. Specifically, we examined the relationships between the brain-predicted age difference (Brain-PAD: real age subtracted from predicted age) and life satisfaction, depressive symptoms, resilience, alcohol consumption, smoking, diabetes, hypertension, and dyslipidemia. Brain-PAD showed significant negative correlations with life satisfaction (Spearman's rs= -0.102, p = 0.005) and resilience (rs= -0.105, p = 0.004). In a multiple regression analysis, life satisfaction (p = 0.038), alcohol use (p = 0.040), and diabetes (p = 0.002) were independently correlated with Brain-PAD. Thus, in the cognitively unimpaired elderly, higher life satisfaction was associated with a 'younger' brain, whereas diabetes and alcohol use had negative impacts on life satisfaction. Subjective life satisfaction, as well as the prevention of diabetes and alcohol use, may protect the brain from accelerated aging.

Optimal Surgical Extent for Memory and Seizure Outcome in Temporal Lobe Epilepsy.

OBJECTIVE: Postoperative memory decline is an important consequence of anterior temporal lobe resection (ATLR) for temporal lobe epilepsy (TLE), and the extent of resection may be a modifiable factor. This study aimed to define optimal resection margins for cognitive outcome while maintaining a high rate of postoperative seizure freedom. METHODS: This cohort study evaluated the resection extent on postoperative structural MRI using automated voxel-based methods and manual measurements in 142 consecutive patients with unilateral drug refractory TLE (74 left, 68 right TLE) who underwent standard ATLR. RESULTS: Voxel-wise analyses revealed that postsurgical verbal memory decline correlated with resections of the posterior hippocampus and inferior temporal gyrus, whereas larger resections of the fusiform gyrus were associated with worsening of visual memory in left TLE. Limiting the posterior extent of left hippocampal resection to 55% reduced the odds of significant postoperative verbal memory decline by a factor of 8.1 (95% CI 1.5-44.4, p = 0.02). Seizure freedom was not related to posterior resection extent, but to the piriform cortex removal after left ATLR. In right TLE, variability of the posterior extent of resection was not associated with verbal and visual memory decline or seizures after surgery. INTERPRETATION: The extent of surgical resection is an independent and modifiable risk factor for cognitive decline and seizures after left ATLR. Adapting the posterior extent of left ATLR might optimize postoperative outcome, with reduced risk of memory impairment while maintaining comparable seizure-freedom rates. The current, more lenient, approach might be appropriate for right ATLR. ANN NEUROL 2022;91:131-144.

Barriers to telemedicine among physicians in epilepsy care during the COVID-19 pandemic: A national-level cross-sectional survey in Japan.

OBJECTIVE: This study aimed to investigate the factors affecting the unwillingness of physicians involved in epilepsy care to continue telemedicine during the coronavirus disease 2019 (COVID-19) pandemic in Japan. METHOD: This was a national-level cross-sectional survey initiated by Japan Young Epilepsy Section (YES-Japan) which is a national chapter of The Young Epilepsy Section of the International League Against Epilepsy (ILAE-YES). We asked physicians who conducted telemedicine in patients with epilepsy (PWE) during the COVID-19 pandemic at four clinics and 21 hospitals specializing in epilepsy care in Japan from March 1 to April 30, 2021. The following data were collected: (1) participant profile, (2) characteristics of PWE treated by telemedicine, and (3) contents and environmental factors of telemedicine. Statistically significant variables (p < 0.05) in the univariate analysis were analyzed in a multivariate binary logistic regression model to detect the independently associated factors with the unwillingness to continue telemedicine. RESULT: Among the 115 respondents (response rate: 64%), 89 were included in the final analysis. Of them, 60 (67.4%) were willing to continue telemedicine, and 29 (32.6%) were unwilling. In the univariate binary logistic regression analysis, age (Odds ratio [OR] = 1.84, 95% confidence interval [CI] 1.10-3.09, p = 0.02), psychiatrist (OR = 5.88, 95% CI 2.15-16.08, p = 0.001), hospital (OR = 0.10, 95% CI 0.01-0.94, p = 0.04), the number of COVID-19 risk factors in the participant (OR = 2.88, 95% CI 1.46-5.69, p = 0.002), the number of COVID-19 risk factors in the cohabitants (OR = 2.52, 95% CI 1.05-6.01, p = 0.04), COVID-19 epidemic area (OR = 4.37, 95% CI 1.18-16.20, p = 0.03), consultation time during telemedicine (OR = 2.51, 95% CI 1.32-4.76, p = 0.005), workload due to telemedicine (OR = 4.17, 95% CI 2.11-8.24, p < 0.001) were statistically significant. In the multivariate binary logistic regression analysis, workload due to telemedicine (OR = 4.93, 95% CI 1.96-12.35) was independently associated with the unwillingness to continue telemedicine. CONCLUSION: This national-level cross-sectional survey found that workload due to telemedicine among physicians involved in epilepsy care was independently associated with the unwillingness to continue telemedicine.

Risk factors for psychological distress in electroencephalography technicians during the COVID-19 pandemic: A national-level cross-sectional survey in Japan.

OBJECTIVE: To identify the risk factors for psychological distress in electroencephalography (EEG) technicians during the coronavirus disease 2019 (COVID-19) pandemic. METHOD: In this national-level cross-sectional survey initiated by Japan Young Epilepsy Section (YES-Japan), which is a national chapter of The Young Epilepsy Section of the International League Against Epilepsy (ILAE-YES), a questionnaire was administered to 173 technicians engaged in EEG at four clinics specializing in epilepsy care and 20 hospitals accredited as (quasi-) epilepsy centers or epilepsy training facilities in Japan from March 1 to April 30, 2021. We collected data on participants' profiles, information about work, and psychological distress outcome measurements, such as the K-6 and Tokyo Metropolitan Distress Scale for Pandemic (TMDP). Linear regression analysis was used to identify the risk factors for psychological distress. Factors that were significantly associated with psychological distress in the univariate analysis were subjected to multivariate analysis. RESULTS: Among the 142 respondents (response rate: 82%), 128 were included in the final analysis. As many as 35.2% of EEG technicians have been under psychological distress. In multivariate linear regression analysis for K-6, female sex, examination for patients (suspected) with COVID-19, and change in salary or bonus were independent associated factors for psychological distress. Contrastingly, in multivariate linear regression analysis for TMDP, female sex, presence of cohabitants who had to be separated from the respondent due to this pandemic, and change in salary or bonus were independent associated factors for psychological distress. CONCLUSION: We successfully identified the risk factors associated with psychological distress in EEG technicians during the COVID-19 pandemic. Our results may help in understanding the psychological stress in EEG technicians during the COVID-19 pandemic and improving the work environment, which is necessary to maintain the mental health of EEG technicians.

Histopathological validation and clinical correlates of hippocampal subfield volumetry based on T2-weighted MRI in temporal lobe epilepsy with hippocampal sclerosis.

The objectives of this study were 1) to histologically validate the hippocampal subfield volumetry based on T2-weighted MRI, and 2) to explore its clinical impact on postsurgical memory function and seizure outcome in temporal lobe epilepsy (TLE) with hippocampal sclerosis (HS). We analyzed the cases of 24 patients with medial TLE (12 left, 12 right) and HS who were preoperatively examined with T2-weighted high-resolution MRI. The volume of each hippocampal subfield was calculated with an automatic segmentation of hippocampal subfields (ASHS) program. Hippocampal sclerosis patterns were determined pathologically, and the cross-sectional area and neuronal cell density of the CA1 and CA4 subfields were calculated using tissue specimens. Pre- and postoperative memory evaluations based on the Wechsler Memory Scale-Revised (WMS-R) were performed. We compared the presurgical MRI-based volumes with the pathological measurements in each subfield and then compared them with the change in the patients' neurocognitive function. As a result, there was a significant relationship between the presurgical MRI-based volume of CA4/dentate gyrus (DG) and the cross-sectional area of CA4 calculated with tissue specimens (Spearman's rs = 0.482, p = 0.023), and a similar trend-level correlation was observed in CA1 (rs = 0.455, p = 0.058). Some of MRI-based or pathology-based parameters in the subfields preliminarily showed relationships with the postsurgical memory changes. In conclusion, automated subfield volumetry for patients with hippocampal sclerosis moderately reflects their subfield atrophy and might be useful to predict the postsurgical change of memory function in these patients.