Event-based modeling in temporal lobe epilepsy demonstrates progressive atrophy from cross-sectional data

Connectome architecture for gray matter atrophy and surgical outcomes in temporal lobe epilepsy

OBJECTIVE

Temporal lobe epilepsy (TLE) has been recognized as a network disorder with widespread gray matter atrophy. However, the role of connectome architecture in shaping morphological alterations and identifying atrophy epicenters remains unclear. Furthermore, individualized modeling of atrophy epicenters and their potential clinical applications have not been well established. This study aims to explore how gray matter atrophy correlates with normal connectome architecture, identify potential atrophy epicenters, and employ individualized modeling approach to evaluate the impact of different epicenter patterns on surgical outcomes in patients with TLE.

METHODS

This study utilized anatomic MRI data from 126 refractory TLE patients who underwent anterior temporal lobectomy and 60 healthy controls (HCs), along with normative functional and structural connectome data, to investigate the relationship between gray matter volume (GMV) changes and functional or structural connectivity. Two models were employed to identify atrophy epicenters: a data-driven approach evaluating nodal and neighbor atrophy rankings, and a network diffusion model (NDM) simulating the spread of pathology from different seed regions. K-means clustering was applied in patient-tailored modeling to uncover distinct epicenter subtypes.

RESULTS

Our findings indicate that the pattern of gray matter atrophy in TLE is constrained primarily by structural connectivity rather than by functional connectivity. Using the structural connectome, we pinpointed the hippocampus and adjacent temporo-limbic regions as key atrophy epicenters. The patient-tailored modeling revealed significant variability in epicenter distribution, allowing us to categorize them into two distinct subtypes. Notably, patients in subtype 2, with epicenters localized to the ipsilateral temporal pole and medial temporal lobe, exhibited significantly higher seizure-free rates compared to patients in subtype 1, whose epicenters situated in frontocentral regions.

SIGNIFICANCE

These findings highlight the central role of structural connectivity in shaping TLE-related morphological changes. Individualized epicenter modeling may enhance surgical decisions and improve prognostic stratification in TLE management.

Hippocampal Glutamate Levels and Their Correlation With Subregion Volume in School-Aged Children With MRI-Negative Epilepsy: A Preliminary Study

BACKGROUND

Abnormal levels of glutamate constitute a key pathophysiologic mechanism in epilepsy. The use of glutamate chemical exchange saturation transfer (GluCEST) imaging to measure glutamate levels in pediatric epilepsy is rarely reported in research.

PURPOSE

To investigate hippocampal glutamate level variations in pediatric epilepsy and the correlation between glutamate and hippocampal subregional volumes.

STUDY TYPE

Cross-sectional, prospective.

SUBJECTS

A total of 38 school-aged pediatric epilepsy patients with structurally normal MRI as determined by at least two independent radiologists (60% males; 8.7 ± 2.5 years; including 20 cases of focal pediatric epilepsy [FE] and 18 cases of generalized pediatric epilepsy [GE]) and 17 healthy controls (HC) (41% males; 9.0 ± 2.5 years).

FIELD STRENGTH/SEQUENCE

3.0 T; 3D magnetization prepared rapid gradient echo (MPRAGE) and 2D turbo spin echo GluCEST sequences.

ASSESSMENT

The relative concentration of glutamate was calculated through pixel-wise magnetization transfer ratio asymmetry (MTRasym) analysis of the GluCEST data. Hippocampal subfield volumes were computed from MPRAGE data using FreeSurfer.

STATISTICAL TESTS

This study used t tests, one-way analysis of variance, Kruskal-Wallis tests, and Pearson correlation analysis. P < 0.05 was considered statistically significant.

RESULTS

The MTRasym values of both the left and right hippocampi were significantly elevated in GE (left: 2.51 ± 0.23 [GE] vs. 2.31 ± 0.12 [HCs], right: 2.50 ± 0.22 [GE] vs. 2.27 ± 0.22 [HCs]). The MTRasym values of the ipsilateral hippocampus were significantly elevated in FE (2.49 ± 0.28 [ipsilateral] vs. 2.29 ± 0.16 [HCs]). The MTRasym values of the ipsilateral hippocampus were significantly increased compared to the contralateral hippocampus in FE (2.49 ± 0.28 [ipsilateral] vs. 2.35 ± 0.34 [contralateral]). No significant differences in hippocampal volume were found between different groups (left hippocampus, P = 0.87; right hippocampus, P = 0.87).

DATA CONCLUSION

GluCEST imaging have potential for the noninvasive measurement of glutamate levels in the brains of children with epilepsy.

LEVEL OF EVIDENCE

2 TECHNICAL EFFICACY: Stage 1.

Progressive brain atrophy and cortical reorganization related to surgery in temporal lobe epilepsy

OBJECTIVE

Epilepsy is associated with progressive cortical atrophy exceeding normal aging. We aimed to explore longitudinal cortical alterations in patients with temporal lobe epilepsy (TLE) and distinct surgery outcomes.

METHODS

We obtained longitudinal T1-weighted MRI data in a well-designed cohort, including 53 operative TLE patients, 23 nonoperative TLE patients, and 23 healthy controls. According to seizure outcomes at 24 months after surgery, operative patients were divided into seizure-free (SF) and nonseizure-free (NSF) group. Operative patients were scanned before and after surgery, while nonoperative patients and healthy controls were rescanned with similar interval times. We measured gray matter volume (GMV) in all participants and compared longitudinal cortical alterations among groups.

RESULTS

In nonoperative group, statistically significant GMV decrease was observed in ipsilateral median cingulate and paracingulate gyri and cerebellum crus I when compared with healthy controls. In operative group, postoperative GMV increase was discovered in many regions involving bilateral hemispheres, especially in the frontal lobe, without differences between SF and NSF group. Postoperative GMV decrease was found in ipsilateral inferior frontal gyrus, putamen, thalamus, and insula. GMV decrease in ipsilateral inferior frontal gyrus, putamen, and insula was more significant in SF group.

INTERPRETATION

Progressive cortical atrophy existed in nonoperative TLE patients. Cortical remodeling indicated by postoperative GMV increase may arise mostly from the surgery itself, rather than postsurgical seizure outcomes. More significant GMV decrease in ipsilateral inferior frontal gyrus, putamen, and insula may imply their closer connections with resected regions in seizure-free patients.

Association of Normative and Non-Normative Brain Networks With Cognitive Function in Patients With Temporal Lobe Epilepsy

BACKGROUND AND OBJECTIVES

Despite their temporal lobe pathology, a significant subgroup of patients with temporal lobe epilepsy (TLE) is able to maintain normative cognitive functioning. In this study, we identify patients with TLE with intact vs impaired neurocognitive profiles and interrogate for the presence of both normative and highly individual intrinsic connectivity networks (ICNs)-all toward understanding the transition from impaired to intact neurocognitive status.

METHODS

This retrospective cross-sectional study included patients with TLE and matched healthy controls (HCs) from the Thomas Jefferson Comprehensive Epilepsy Center. Functional MRI data were decomposed using independent component analysis to obtain individualized ICNs. In this article, we calculated the degree of match between individualized ICNs and canonical ICNs (e.g., 17 resting-state networks by Yeo et al.) and divided each participant's ICNs into normative or non-normative status based on the degree of match.

RESULTS

100 patients with TLE (mean age 42.0 [SD: 13.7] years, 47 women) and 92 HCs were included in this study. We found that the individualized networks matched to the canonical networks less well in the cognitively impaired (n = 24) compared with the cognitively intact (n = 63) patients with TLE by 2-way mixed-measures analysis of variance (impaired vs intact mean difference [MD] -0.165 [-0.317, -0.013], p = 0.028). The cognitively impaired patients showed significant abnormalities in the profiles of both normative (impaired vs intact MD -0.537 [-0.998, -0.076], p = 0.017, intact vs HC MD -0.221 [-0.536, 0.924], p = 0.220, and impaired vs HC MD -0.759 [-1.200, -0.319], p < 0.001) and non-normative networks (impaired vs intact MD 0.484 [0.030, 0.937], p = 0.033, intact vs HC MD 0.369 [0.059, 0.678], p = 0.014, and impaired vs HC MD 0.853 [0.419, 1.286], p < 0.001) while the intact patients showed abnormalities only in non-normative networks. At the same time, we found that normative networks held a strong, positive association with the neuropsychological measures, with this association negative in non-normative networks.

DISCUSSION

Our data demonstrated that significant cognitive deficits are associated with the status of both canonical and highly individual ICNs, making clear that the transition from intact to impaired cognitive status is not simply the result of disruption to normative brain networks.

Neurofilament light (NfL) concentrations in patients with epilepsy with recurrent isolated seizures: Insights from a clinical cohort study

PURPOSE

To detect possible neuronal damage due to recurrent isolated seizures in patients with epilepsy in a clinical routine setting.

METHODS

We measured the serum concentrations of neurofilament light chain (sNfL) in 46 outpatients with an at least monthly occurrence (self-reported) of generalized tonic-clonic seizures in the six months prior to the study and in 49 patients who had been seizure free (self-reported) for at least one year. We assigned the patients with seizure activity into groups with moderate and high seizure frequency. We measured sNfL with a highly sensitive single molecule array (Simoa).

RESULTS

The majority (94 %) of all patients with epilepsy had sNfL values within the age adjusted reference ranges of our laboratory. Three patients with and three patients without seizure activity (each 3 %) showed elevated sNfL concentrations. Age adjusted sNfL concentrations did not differ significantly between patients with and without seizure activity in the total sample or in the female subgroup. In contrast, NfL concentrations were significantly higher in male patients with seizure activity and highest in the subgroup of those with high seizure activity, but were only above the reference range in two patients. sNfL concentrations did not differ between focal and generalized epilepsies and between genetic and structural etiologies.

CONCLUSIONS

The sNfL concentrations in patients with epilepsy and healthy patients did not differ significantly. The finding of higher sNfL concentrations in males with self-reported seizure activity should be viewed with utmost caution because the difference was small and only two male patients showed sNfL concentrations above the reference range.

MRI-Derived Modeling of Disease Progression Patterns in Patients With Temporal Lobe Epilepsy

BACKGROUND AND OBJECTIVES

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.

DISCUSSION

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.

Impaired interhemispheric synchrony and effective connectivity in right temporal lobe epilepsy

BACKGROUND

The brain functional network plays a crucial role in cognitive impairment in temporal lobe epilepsy (TLE). Based on voxel-mirrored homotopic connectivity (VMHC), this study explored how directed functional connectivity changes and is associated with impaired cognition in right TLE (rTLE).

METHODS

Twenty-seven patients with rTLE and twenty-seven healthy controls were included to perform VMHC and Granger causality analysis (GCA). Correlation analysis was performed based on GCA and cognitive function.

RESULTS

Bilateral middle frontal gyrus (MFG), middle temporal gyrus, dorsolateral superior frontal gyrus (SFGdor), and supramarginal gyrus (SMG) exhibited decreased VMHC values in the rTLE group. Brain regions with altered VMHC had abnormal directed functional connectivity with multiple brain regions, mainly belonging to the default mode network, sensorimotor network, and visual network. Besides, the Montreal Cognitive Assessment (MoCA) score was positively correlated with the connectivity from the left SFGdor to the right cerebellum crus2 and was negatively correlated with the connectivity from the left SMG to the right supplementary motor area (SMA) before correction. Before correction, both phasic and intrinsic alertness reaction time were positively correlated with the connectivity from the left MFG to the left precentral gyrus (PreCG), connectivity from the left SMG to the right PreCG, and the connectivity from the left SMG to the right SMA. The executive control effect reaction time was positively correlated with the connectivity from the left MFG to the left calcarine fissure surrounding cortex before correction.

CONCLUSION

The disordered functional network tended to be correlated with cognition impairment in rTLE.

The use of automated and AI-driven algorithms for the detection of hippocampal sclerosis and focal cortical dysplasia

In drug-resistant epilepsy, magnetic resonance imaging (MRI) plays a central role in detecting lesions as it offers unmatched spatial resolution and whole-brain coverage. In addition, the last decade has witnessed continued developments in MRI-based computer-aided machine-learning techniques for improved diagnosis and prognosis. In this review, we focus on automated algorithms for the detection of hippocampal sclerosis and focal cortical dysplasia, particularly in cases deemed as MRI negative, with an emphasis on studies with histologically validated data. In addition, we discuss imaging-derived prognostic markers, including response to anti-seizure medication, post-surgical seizure outcome, and cognitive reserves. We also highlight the advantages and limitations of these approaches and discuss future directions toward person-centered care.

Identification of four biotypes in temporal lobe epilepsy via machine learning on brain images

Artificial intelligence provides an opportunity to try to redefine disease subtypes based on similar pathobiology. Using a machine-learning algorithm (Subtype and Stage Inference) with cross-sectional MRI from 296 individuals with focal epilepsy originating from the temporal lobe (TLE) and 91 healthy controls, we show phenotypic heterogeneity in the pathophysiological progression of TLE. This study was registered in the Chinese Clinical Trials Registry (number: ChiCTR2200062562). We identify two hippocampus-predominant phenotypes, characterized by atrophy beginning in the left or right hippocampus; a third cortex-predominant phenotype, characterized by hippocampus atrophy after the neocortex; and a fourth phenotype without atrophy but amygdala enlargement. These four subtypes are replicated in the independent validation cohort (109 individuals). These subtypes show differences in neuroanatomical signature, disease progression and epilepsy characteristics. Five-year follow-up observations of these individuals reveal differential seizure outcomes among subtypes, indicating that specific subtypes may benefit from temporal surgery or pharmacological treatment. These findings suggest a diverse pathobiological basis underlying focal epilepsy that potentially yields to stratification and prognostication - a necessary step for precise medicine.

Data-driven modelling of neurodegenerative disease progression: thinking outside the black box

Data-driven disease progression models are an emerging set of computational tools that reconstruct disease timelines for long-term chronic diseases, providing unique insights into disease processes and their underlying mechanisms. Such methods combine a priori human knowledge and assumptions with large-scale data processing and parameter estimation to infer long-term disease trajectories from short-term data. In contrast to 'black box' machine learning tools, data-driven disease progression models typically require fewer data and are inherently interpretable, thereby aiding disease understanding in addition to enabling classification, prediction and stratification. In this Review, we place the current landscape of data-driven disease progression models in a general framework and discuss their enhanced utility for constructing a disease timeline compared with wider machine learning tools that construct static disease profiles. We review the insights they have enabled across multiple neurodegenerative diseases, notably Alzheimer disease, for applications such as determining temporal trajectories of disease biomarkers, testing hypotheses about disease mechanisms and uncovering disease subtypes. We outline key areas for technological development and translation to a broader range of neuroscience and non-neuroscience applications. Finally, we discuss potential pathways and barriers to integrating disease progression models into clinical practice and trial settings.

Morphometrics of the preserved post-surgical hemisphere in pediatric drug-resistant epilepsy

Importance

Structural integrity of cortex following cortical resection for epilepsy management has been previously characterized, but only in adult patients.

Objective

This study sought to determine whether morphometrics of the preserved hemisphere in pediatric cortical resection patients differ from non-neurological controls.

Design

This was a case-control study, from 2013-2022.

Setting

This was a single-site study.

Participants

32 patients with childhood epilepsy surgery and 51 age- and gender-matched controls participated.

Main Measures

We quantified morphometrics of the preserved hemisphere at the level of gross anatomy (lateral ventricle size, volume of gray and white matter). Additionally, cortical thickness, volume, and surface area were measured for 34 cortical regions segmented with the Desikan-Killiany atlas, and, last, volumes of nine subcortical regions were also quantified.

Results

13 patients with left hemisphere (LH) surgery and a preserved right hemisphere (RH) (median age/median absolute deviation of age: 15.7/1.7 yr; 6 females, 7 males) and 19 patients with RH surgery and a preserved LH (15.4/3.7 yr; 11 females, 8 males) were compared to 51 controls (14.8/4.9 yr; 24 females, 27 males). Patient groups had larger ventricles and reduced total white matter volume relative to controls, and only patients with a preserved RH, but not patients with a preserved LH, had reduced total gray matter volume relative to controls. Furthermore, patients with a preserved RH had lower cortical thickness and volume and greater surface area of several cortical regions, relative to controls. Patients with a preserved LH had no differences in thickness, volume, or area, of any of the 34 cortical regions, relative to controls. Moreover, both LH and RH patients showed reduced volumes in select subcortical structures, relative to controls.

Conclusions and Relevance

That left-sided, but not right-sided, resection is associated with more pronounced reduction in cortical thickness and volume and increased cortical surface area relative to typically developing, age-matched controls suggests that the preserved RH undergoes structural plasticity to an extent not observed in cases of right-sided pediatric resection. Future work probing the association of the current findings with neuropsychological outcomes will be necessary to understand the implications of these structural findings for clinical practice.

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.

Out of one, how many? Subtyping in epilepsy

This scientific commentary refers to ‘Identification of different MRI atrophy progression trajectories in epilepsy by subtype and stage inference’ by Xiao et al. (https://doi.org/10.1093/brain/awad284).

Neurofilament light, glial fibrillary acidic protein, and tau in a regional epilepsy cohort: High plasma levels are rare but related to seizures

OBJECTIVE

Higher levels of biochemical blood markers of brain injury have been described immediately after tonic-clonic seizures and in drug-resistant epilepsy, but the levels of such markers in epilepsy in general have not been well characterized. We analyzed neurofilament light (NfL), glial fibrillary acidic protein (GFAP), and tau in a regional hospital-based epilepsy cohort and investigated what proportion of patients have levels suggesting brain injury, and whether certain epilepsy features are associated with high levels.

METHODS

Biomarker levels were measured in 204 patients with an epilepsy diagnosis participating in a prospective regional biobank study, with age and sex distribution correlating closely to that of all patients seen for epilepsy in the health care region. Absolute biomarker levels were assessed between two patient groups: patients reporting seizures within the 2 months preceding inclusion and patients who did not have seizures for more than 1 year. We also assessed the proportion of patients with above-normal levels of NfL.

RESULTS

NfL and GFAP, but not tau, increased with age. Twenty-seven patients had abnormally high levels of NfL. Factors associated with such levels were recent seizures (p = .010) and epileptogenic lesion on radiology (p = .001). Levels of NfL (p = .006) and GFAP (p = .032) were significantly higher in young patients (<65 years) with seizures ≤2 months before inclusion compared to those who reported no seizures for >1 year. NfL and GFAP correlated weakly with the number of days since last seizure (NfL: rs  = -.228, p = .007; GFAP: rs  = -.167, p = .048) in young patients. NfL also correlated weakly with seizure frequency in the last 2 months (rs  = .162, p = .047).

SIGNIFICANCE

Most patients with epilepsy do not have biochemical evidence of brain injury. The association with seizures merits further study; future studies should aim for longitudinal sampling and examine whether individual variations in NfL or GFAP levels could reflect seizure activity.

sEBM: Scaling Event Based Models to Predict Disease Progression via Implicit Biomarker Selection and Clustering

The Event Based Model (EBM) is a probabilistic generative model to explore biomarker changes occurring as a disease progresses. Disease progression is hypothesized to occur through a sequence of biomarker dysregulation "events". The EBM estimates the biomarker dysregulation event sequence. It computes the data likelihood for a given dysregulation sequence, and subsequently evaluates the posterior distribution on the dysregulation sequence. Since the posterior distribution is intractable, Markov Chain Monte-Carlo is employed to generate samples under the posterior distribution. However, the set of possible sequences increases as N ! where N is the number of biomarkers (data dimension) and quickly becomes prohibitively large for effective sampling via MCMC. This work proposes the "scaled EBM" (sEBM) to enable event based modeling on large biomarker sets (e.g. high-dimensional data). First, sEBM implicitly selects a subset of biomarkers useful for modeling disease progression and infers the event sequence only for that subset. Second, sEBM clusters biomarkers with similar positions in the event sequence and only orders the "clusters", with each successive cluster corresponding to the next stage in disease progression. These two modifications used to construct the sEBM method provably reduces the possible space of event sequences by multiple orders of magnitude. The novel modifications are supported by theory and experiments on synthetic and real clinical data provides validation for sEBM to work in higher dimensional settings. Results on synthetic data with known ground truth shows that sEBM outperforms previous EBM variants as data dimensions increase. sEBM was successfully implemented with up to 300 biomarkers, which is a 6-fold increase over previous EBM applications. A real-world clinical application of sEBM is performed using 119 neuroimaging markers from publicly available Alzheimer's Disease Neuroimaging Initiative (ADNI) data to stratify subjects into 6 stages of disease progression. Subjects included cognitively normal (CN), mild cognitive impairment (MCI), and Alzheimer's Disease (AD). sEBM stage is differentiated for the 3 groups ( χ 2 p - v a l u e < 4.6 e - 32 ) . Increased sEBM stage is a strong predictor of conversion risk to AD ( p - v a l u e < 2.3 e - 14 ) for MCI subjects, as verified with a Cox proportional-hazards model adjusted for age, sex, education and APOE4 status. Like EBM, sEBM does not rely on apriori defined diagnostic labels and only uses cross-sectional data.

Chronic effects of inflammation on tauopathies

Tauopathies are a heterogeneous group of neurodegenerative disorders that are characterised by the aggregation of the microtubule-associated protein tau into filamentous inclusions within neurons and glia. Alzheimer's disease is the most prevalent tauopathy. Despite years of intense research efforts, developing disease-modifying interventions for these disorders has been very challenging. The detrimental role that chronic inflammation plays in the pathogenesis of Alzheimer's disease is increasingly recognised; however, it is largely ascribed to the accumulation of amyloid β, leaving the effect of chronic inflammation on tau pathology and neurofibrillary tangle-related pathways greatly overlooked. Tau pathology can independently arise secondary to a range of triggers that are each associated with inflammatory processes, including infection, repetitive mild traumatic brain injury, seizure activity, and autoimmune disease. A greater understanding of the chronic effects of inflammation on the development and progression of tauopathies could help forge a path for the establishment of effective immunomodulatory disease-modifying interventions for clinical use.

Relationship between visuoperceptual functions and parietal structural abnormalities in temporal lobe epilepsy

Progressive gray matter volume reductions beyond the epileptogenic area has been described in temporal lobe epilepsy. There is less evidence regarding correlations between gray and white matter volume changepres and multi-domain cognitive performance in this setting. We aimed to investigate correlations between volume changes in parietal structures and visuospatial performance in temporal lobe epilepsy patients. we performed a cross-sectional study comparing global and regional brain volume data from 34 temporal lobe epilepsy patients and 30 healthy controls. 3D T1-weighted sequences were obtained on a 3.0 T magnet, and data were analyzed using age and sex-adjusted linear regression models. Global and regional brain volumes and cortical thickness in patients were correlated with standardized visual memory, visuoperceptual, visuospatial, and visuoconstructive parameters obtained in a per-protocol neuropsychological assessment. temporal lobe epilepsy patients had smaller volume fractions of the deep gray matter structures, putamen and nucleus accumbens, and larger cerebrospinal fluid volume fraction than controls. Correlations were found between: 1) visual memory and precuneus and inferior parietal cortical thickness; 2) visuoperceptual performance and precuneus and supramarginal white matter volumes; 3) visuospatial skills and precuneus, postcentral, and inferior and superior parietal white matter volumes; 4) visuoconstructive performance and inferior parietal white matter volume. Brain volume loss is widespread in temporal lobe epilepsy. Volumetric reductions in parietal lobe structures were associated with visuoperceptual cognitive performance.