Classification and pathological characteristics of the cortical dysplasias

Reorganization of Parvalbumin Immunopositive Perisomatic Innervation of Principal Cells in Focal Cortical Dysplasia Type IIB in Human Epileptic Patients

Focal cortical dysplasia (FCD) is one of the most common causes of drug-resistant epilepsy. As several studies have revealed, the abnormal functioning of the perisomatic inhibitory system may play a role in the onset of seizures. Therefore, we wanted to investigate whether changes of perisomatic inhibitory inputs are present in FCD. Thus, the input properties of abnormal giant- and control-like principal cells were examined in FCD type IIB patients. Surgical samples were compared to controls from the same cortical regions with short postmortem intervals. For the study, six subjects were selected/each group. The perisomatic inhibitory terminals were quantified in parvalbumin and neuronal nuclei double immunostained sections using a confocal fluorescent microscope. The perisomatic input of giant neurons was extremely abundant, whereas control-like cells of the same samples had sparse inputs. A comparison of pooled data shows that the number of parvalbumin-immunopositive perisomatic terminals contacting principal cells was significantly larger in epileptic cases. The analysis showed some heterogeneity among epileptic samples. However, five out of six cases had significantly increased perisomatic input. Parameters of the control cells were homogenous. The reorganization of the perisomatic inhibitory system may increase the probability of seizure activity and might be a general mechanism of abnormal network activity.

Unsupervised machine learning reveals lesional variability in focal cortical dysplasia at mesoscopic scale

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Consensus clustering of MRI contrasts maps focal cortical dysplasia lesional variability.

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Lesions were parcellated into four classes with distinct structural profiles.

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FCD classes reflected typical functional and histopathological characteristics.

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Class membership was replicated in two independent datasets.

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Class-informed detection algorithm outperformed a class-naïve paradigm.

Objective

Focal cortical dysplasia (FCD) is the most common epileptogenic developmental malformation and a prevalent cause of surgically amenable epilepsy. While cellular and molecular biology data suggest that FCD lesional characteristics lie along a spectrum, this notion remains to be verified in vivo. We tested the hypothesis that machine learning applied to MRI captures FCD lesional variability at a mesoscopic scale.

Methods

We studied 46 patients with histologically verified FCD Type II and 35 age- and sex-matched healthy controls. We applied consensus clustering, an unsupervised learning technique that identifies stable clusters based on bootstrap-aggregation, to 3 T multicontrast MRI (T1-weighted MRI and FLAIR) features of FCD normalized with respect to distributions in controls.

Results

Lesions were parcellated into four classes with distinct structural profiles variably expressed within and across patients: Class-1 with isolated white matter (WM) damage; Class-2 combining grey matter (GM) and WM alterations; Class-3 with isolated GM damage; Class-4 with GM-WM interface anomalies. Class membership was replicated in two independent datasets. Classes with GM anomalies impacted local function (resting-state fMRI derived ALFF), while those with abnormal WM affected large-scale connectivity (assessed by degree centrality). Overall, MRI classes reflected typical histopathological FCD characteristics: Class-1 was associated with severe WM gliosis and interface blurring, Class-2 with severe GM dyslamination and moderate WM gliosis, Class-3 with moderate GM gliosis, Class-4 with mild interface blurring. A detection algorithm trained on class-informed data outperformed a class-naïve paradigm.

Significance

Machine learning applied to widely available MRI contrasts uncovers FCD Type II variability at a mesoscopic scale and identifies tissue classes with distinct structural dimensions, functional and histopathological profiles. Integrating in vivo staging of FCD traits with automated lesion detection is likely to inform the development of novel personalized treatments.

Clinical characteristics and epilepsy outcomes following surgery caused by focal cortical dysplasia (type IIa) in 110 adult epileptic patients

The aim of the present study was to investigate the effects of surgical intervention of focal cortical dysplasia (FCD) IIa on the outcome of epilepsy, and to evaluate the prognostic factors of seizure freedom. Patient data from epilepsy surgeries were retrospectively reviewed at the Second Affiliated Hospital of Dalian Medical University between 2007 and 2015. A total of 110 patients with a definite pathological diagnosis of FCD IIa were included. Moreover, the clinical characteristics, seizure outcome and quality of life in adults with FCD IIa were evaluated. The Engel seizure outcome achievements were class I in 72, class II in 20, class III in 11 and class IV in 7 patients. In addition, the Engel seizure outcome was relevant with the resection range of the lesions (P=0.028). The assessments of electrocorticography (ECoG) patterns and magnetic resonance imaging (MRI) are relevant to determining the extent of the resection, which may influence the surgery outcome (P=0.001 and P=0.023). Using multivariate regression analyses, the extent of resection, seizure frequency, preoperative ECoG and location of resection were the most important risk factors for seizure recurrence. The results of quality of life in epilepsy-10 scoring revealed that the quality of life improved significantly following surgery (P<0.01). Moreover, surgical intervention, EcoG, MRI positioning and complete resection helped to have improved seizure control, relief of anxiety and quality of life. All these observations strongly recommend an early consideration of epilepsy surgery in FCD IIa patients.