Can we predict drug response by volumes of the corpus callosum in newly diagnosed focal epilepsy?

Clinical prediction models for treatment outcomes in newly diagnosed epilepsy: A systematic review

Up to 35% of individuals diagnosed with epilepsy continue to have seizures despite treatment, commonly referred to as drug-resistant epilepsy. Uncontrolled seizures can directly, or indirectly, negatively impact an individual's quality of life. To inform clinical management and life decisions, it is important to be able to predict the likelihood of seizure control. Those likely to achieve seizure control will be able to return sooner to their usual work and leisure activities and require less follow-up, whereas those with a poor prognosis will need more frequent clinical attendance and earlier consideration of epilepsy surgery. This is a systematic review aimed at identifying demographic, clinical, physiological (e.g., electroencephalographic), and imaging (e.g., magnetic resonance imaging) factors that may be predictive of treatment outcomes in patients with newly diagnosed epilepsy (NDE). MEDLINE and Embase were searched for prediction models of treatment outcomes in patients with NDE. Study characteristics were extracted and subjected to assessment of risk of bias (and applicability concerns) using the PROBAST (Prediction Model Risk of Bias Assessment Tool) tool. Baseline variables associated with treatment outcomes are reported as prognostic factors. After screening, 48 models were identified in 32 studies, which generally scored low for concerns of applicability, but universally scored high for susceptibility to bias. Outcomes reported fit broadly into four categories: drug resistance, short-term treatment response, seizure remission, and mortality. Prognostic factors were also heterogenous, but the predictors that were commonly significantly associated with outcomes were those related to seizure characteristics/types, epilepsy history, and age at onset. Antiseizure medication response was often included as a baseline variable, potentially obscuring other factor relationships at baseline. Currently, outcome prediction models for NDE demonstrate a high risk of bias. Model development could be improved with a stronger adherence to recommended TRIPOD (Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis) practices. Furthermore, we outline actionable changes to common practices that are intended to improve the overall quality of prediction model development in NDE.

Corpus callosum thickness: A predictive factor for the first drug efficiency of self-limited epilepsy with centrotemporal spikes (selects)?

OBJECTIVE

To investigate the existence of a possible linkage between the thickness of corpus callosum (CC) regions and the first antiepileptic drug response in patients with Selects.

MATERIALS AND METHODS

CC thickness of 68 patients with Selects and 42 healthy controls between 4 and 12 years of age were measured using brain magnetic resonance imaging (MRI). Clinical and EEG features of newly diagnosed Selects patients were recorded. Patients were divided into two groups: good-response (patients without seizures within 24 weeks) and poor-response (patients with ≥ 1 seizure within 24 weeks). Thickness of CC was compared between patients (good-response and poor-response groups).and healthy controls.

RESULTS

The thicknesses of genu and isthmus were significantly reduced in the Selects group than healthy controls. Isthmus and splenium were significantly thinner in poor responders than those in the good-response group (p = 0.005 and p < 0.001, respectively). The total number of seizures was negatively correlated with the thickness of the body, isthmus, and splenium (p < 0.001). There was no significant difference in CC thickness of the children with and without electrical status epilepticus in sleep (ESES). The thickness of the isthmus and splenium were significantly thinner in patients receiving ≥ 2 antiepileptic drugs (p = 0.002 and p = 0.001, respectively).

CONCLUSIONS

Our study highlights the notable differences in areas of CC in Selects patients. These changes may help uncover the underlying cause of seizure recurrence and antiepileptic drug (AED) response. Different thinner parts of CC may be a protective mechanism to prevent seizure spread to other brain regions. CC thickness can be used as a new radiologic biomarker for predicting first AED response and seizure recurrence in Selects patients.

Perilesional white matter integrity in drug-resistant epilepsy related to focal cortical dysplasia

PURPOSE

We aimed to investigate the differences of white matter (WM) between the focal cortical dysplasia (FCD) patients with drug-resistant epilepsy and those with drug-responsive epilepsy.

METHODS

Thirty epileptic patients with MRI-identified or histologically proven FCD were consecutively enrolled. Fractional anisotropy (FA) and mean diffusivity (MD) of the ipsilateral perilesional WM and contralateral homotopic WM layer masks were computed and corrected by the FA/MD of the corresponding hemispheric WM. The difference was evaluated using paired t-tests. The FA, MD and volumes of hemispheric WM and corpus callosum were also calculated.

RESULTS

Patients with drug-resistant epilepsy showed significantly decreased FA and increased MD among ipsilateral perilesional WM layer 1 and 2, while patients with drug-responsive epilepsy showed decreased FA in only ipsilateral perilesional WM layer l, compared to remaining ipsilateral perilesional WM layers and contralateral layers 1 through 6. The integrity and volumes of the hemispheric WM and corpus callosum were similar between the two groups.

CONCLUSION

We demonstrated that the WM microstructural alterations differed between epileptic patients with FCD according to their antiepileptic drug responses. More extensive perilesional WM abnormality is observed in patients with drug-resistant epilepsy related to FCD.

Fiber ball white matter modeling in focal epilepsy

Multicompartment diffusion magnetic resonance imaging (MRI) approaches are increasingly being applied to estimate intra‐axonal and extra‐axonal diffusion characteristics in the human brain. Fiber ball imaging (FBI) and its extension fiber ball white matter modeling (FBWM) are such recently described multicompartment approaches. However, these particular approaches have yet to be applied in clinical cohorts. The modeling of several diffusion parameters with interpretable biological meaning may offer the development of new, noninvasive biomarkers of pharmacoresistance in epilepsy. In the present study, we used FBI and FBWM to evaluate intra‐axonal and extra‐axonal diffusion properties of white matter tracts in patients with longstanding focal epilepsy. FBI/FBWM diffusion parameters were calculated along the length of 50 white matter tract bundles and statistically compared between patients with refractory epilepsy, nonrefractory epilepsy and controls. We report that patients with chronic epilepsy had a widespread distribution of extra‐axonal diffusivity relative to controls, particularly in circumscribed regions along white matter tracts projecting to cerebral cortex from thalamic, striatal, brainstem, and peduncular regions. Patients with refractory epilepsy had significantly greater markers of extra‐axonal diffusivity compared to those with nonrefractory epilepsy. The extra‐axonal diffusivity alterations in patients with epilepsy observed in the present study could be markers of neuroinflammatory processes or a reflection of reduced axonal density, both of which have been histologically demonstrated in focal epilepsy. FBI is a clinically feasible MRI approach that provides the basis for more interpretive conclusions about the microstructural environment of the brain and may represent a unique biomarker of pharmacoresistance in epilepsy.

Altered structural connectome in non-lesional newly diagnosed focal epilepsy: Relation to pharmacoresistance

Despite an expanding literature on brain alterations in patients with longstanding epilepsy, few neuroimaging studies investigate patients with newly diagnosed focal epilepsy (NDfE). Understanding brain network impairments at diagnosis is necessary to elucidate whether or not brain abnormalities are principally due to the chronicity of the disorder and to develop prognostic markers of treatment outcome. Most adults with NDfE do not have MRI-identifiable lesions and the reasons for seizure onset and refractoriness are unknown. We applied structural connectomics to T1-weighted and multi-shell diffusion MRI data with generalized q-sampling image reconstruction using Network Based Statistics (NBS). We scanned 27 patients within an average of 3.7 (SD = 2.9) months of diagnosis and anti-epileptic drug treatment outcomes were collected 24 months after diagnosis. Seven patients were excluded due to lesional NDfE and outcome data was available in 17 patients. Compared to 29 healthy controls, patients with non-lesional NDfE had connectomes with significantly decreased quantitative anisotropy in edges connecting right temporal, frontal and thalamic nodes and increased diffusivity in edges between bilateral temporal, frontal, occipital and parietal nodes. Compared to controls, patients with persistent seizures showed the largest effect size (|d|>=1) for decreased anisotropy in right parietal edges and increased diffusivity in edges between left thalamus and left parietal nodes. Compared to controls, patients who were rendered seizure-free showed the largest effect size for decreased anisotropy in the edge connecting the left thalamus and right temporal nodes and increased diffusivity in edges connecting right frontal nodes. As demonstrated by large effect sizes, connectomes with decreased anisotropy (edge between right frontal and left insular nodes) and increased diffusivity (edge between right thalamus and left parietal nodes) were found in patients with persistent seizures compared to patients who became seizure-free. Patients who had persistent seizures showed larger effect sizes in all network metrics than patients who became seizure-free when compared to each other and compared to controls. Furthermore, patients with focal-to-bilateral tonic-clonic seizures (FBTCS, N = 11) had decreased quantitative anisotropy in a bilateral network involving edges between temporal, parietal and frontal nodes with greater effect sizes than those of patients without FBTCS (N = 9). NBS findings between patients and controls indicated that structural network changes are not necessarily a consequence of longstanding refractory epilepsy and instead are present at the time of diagnosis. Computed effect sizes suggest that there may be structural network MRI-markers of future pharmacoresistance and seizure severity in patients with a new diagnosis of focal epilepsy.

Longitudinal analysis of structural connectivity in patients with newly diagnosed focal epilepsy of unknown origin

OBJECTIVES

The aim of this longitudinal study was to clarify whether significant alterations in structural connectivity occur over time in patients with newly diagnosed focal epilepsy of unknown origin.

METHODS

A total of 40 patients with newly diagnosed focal epilepsy of unknown origin and with normal brain magnetic resonance imaging (MRI) on visual inspection were enrolled. All subjects underwent MRI twice involving three-dimensional volumetric T1-weighted imaging, which were suitable for structural volume analysis. Gray matter volumes were obtained using the FreeSurfer image analysis suite, and structural connectivity analyses were performed using Matlab-based BRain Analysis using graPH theory software.

RESULTS

The median interval between the two MRI scans was 18.5 months in patients with epilepsy. There was a general tendency toward decreased gray matter volumes on the second scan compared with the initial scan. However, the volumes of the right and left thalamus and brainstem on the second MRI scan had an increased tendency compared with those on the initial MRI scan. In measures of connectivity, there were significant differences between the two MRI scans. The mean clustering coefficient, global efficiency, local efficiency, and the small-worldness index were significantly increased, whereas the characteristic path length was decreased on the second MRI scan compared with the initial MRI scan.

CONCLUSIONS

The structural connectivity in patients with newly diagnosed focal epilepsy of unknown origin increases over time in the initial stage. These alterations and increases in structural connectivity may be related to underlying epileptogenicity in the initial stages of epilepsy.

Resting-state functional brain networks in adults with a new diagnosis of focal epilepsy

OBJECTIVES

Newly diagnosed focal epilepsy (NDfE) is rarely studied, particularly using advanced neuroimaging techniques. Many patients with NDfE experience cognitive impairments, particularly with respect to memory, sustained attention, mental flexibility, and executive functioning. Cognitive impairments have been related to alterations in resting-state functional brain networks in patients with neurological disorders. In the present study, we investigated whether patients with NDfE had altered connectivity in large-scale functional networks using resting-state functional MRI.

METHODS

We recruited 27 adults with NDfE and 36 age- and sex-matched healthy controls. Resting-state functional MRI was analyzed using the Functional Connectivity Toolbox (CONN). We investigate reproducibly determined large-scale functional networks, including the default mode, salience, fronto-parietal attention, sensorimotor, and language networks using a seed-based approach. Network comparisons between patients and controls were thresholded using a FDR cluster-level correction approach.

RESULTS

We found no significant differences in functional connectivity between seeds within the default mode, salience, sensorimotor, and language networks and other regions of the brain between patients and controls. However, patients with NDfE had significantly reduced connectivity between intraparietal seeds within the fronto-parietal attention network and predominantly frontal and temporal cortical regions relative to controls; this finding was demonstrated including and excluding the patients with brain lesions. No common alteration in brain structure was observed in patients using voxel-based morphometry. Findings were not influenced by treatment outcome at 1 year.

CONCLUSIONS

Patients with focal epilepsy have brain functional connectivity alterations at diagnosis. Functional brain abnormalities are not necessarily a consequence of the chronicity of epilepsy and are present when seizures first emerge.

Resting state functional connectivity patterns associated with pharmacological treatment resistance in temporal lobe epilepsy

There are no functional imaging based biomarkers for pharmacological treatment response in temporal lobe epilepsy (TLE). In this study, we investigated whether there is an association between resting state functional brain connectivity (RsFC) and seizure control in TLE. We screened a large database containing resting state functional magnetic resonance imaging (Rs-fMRI) data from 286 epilepsy patients. Patient medical records were screened for seizure characterization, EEG reports for lateralization and location of seizure foci to establish uniformity of seizure localization within patient groups. Rs-fMRI data from patients with well-controlled left TLE, patients with treatment-resistant left TLE, and healthy controls were analyzed. Healthy controls and cTLE showed similar functional connectivity patterns, whereas trTLE exhibited a significant bilateral decrease in thalamo-hippocampal functional connectivity. This work is the first to demonstrate differences in neural network connectivity between well-controlled and treatment-resistant TLE. These differences are spatially highly focused and suggest sites for the etiology and possibly treatment of TLE. Altered thalamo-hippocampal RsFC thus is a potential new biomarker for TLE treatment resistance.

Corpus callosum atrophy and post-surgical seizures in temporal lobe epilepsy associated with hippocampal sclerosis

OBJECTIVE

Our aim in this retrospective study was to explore whether corpus callosum atrophy could predict the post-surgical seizure control in patients with temporal lobe epilepsy associated with Hippocampal Sclerosis (HS).

METHODS

We used the Corpus Callosum Index (CCI) obtained from best mid-sagittal T2/FLAIR or T1-weighted MRI at two time-points, more than one year apart. CCI has been mainly used in Multiple Sclerosis (MS), but not in epilepsy, so we tested the validity of our results performing a proof of concept cohort, incorporating MS patients with and without epilepsy. Then, we explored this measurement in a well-characterized and long-term cohort of patients with temporal lobe epilepsy associated with HS.

RESULTS

In the proof of concept cohort (MS without epilepsy n:40, and MS with epilepsy, n:15), we found a larger CCI atrophy rate in MS patients with poor epilepsy control vs. MS without epilepsy (p:0.01). Then, in HS patients (n:74), annualized CCI atrophy rate was correlated with the long-term Engel scale (Rho:0.31, p:0.007). In patients with post-surgical seizure recurrence, a larger CCI atrophy rate was found one year before any seizure relapse. Univariate analysis showed an increased risk of seizure recurrence in males, higher pre-surgical seizure frequency, necessity of invasive EEG monitoring, and higher CCI atrophy rate. Two of these variables were independent predictors in the multivariate analysis, male gender (HR:4.87, p:0.002) and CCI atrophy rate (HR:1.21, p:0.001).

CONCLUSION

We demonstrated that atrophy of the corpus callosum, using the CCI, is related with poor seizure control in two different neurological disorders presenting with epilepsy, which might suggest that corpus callosum atrophy obtained in early post-surgical follow-up, could be a biomarker for predicting recurrences and guiding treatment plans.

Can we predict drug response by volumes of the corpus callosum in newly diagnosed focal epilepsy?

OBJECTIVE

The aim of this study was to investigate whether volumes of the corpus callosum could predict a response to antiepileptic drugs in patients with newly diagnosed focal epilepsy.

METHODS

Fifty-three patients with newly diagnosed focal epilepsy of unknown etiology and healthy subjects were enrolled in this study. First, we analyzed the differences in the volumes of the corpus callosum between patients with epilepsy and healthy subjects. Second, we divided patients with epilepsy into antiepileptic drug responders and drug nonresponders groups, according to their seizure controls, and evaluated the differences in the volumes of the corpus callosum between the groups. Third, we conducted correlation analyses between the volumes of the corpus callosum and mean diffusion measures in healthy subjects.

RESULTS

The volumes of the corpus callosum in patients with epilepsy were significantly lower than those in normal controls (p = .0001). Among epilepsy patients, the volumes of the corpus callosum were significantly lower in antiepileptic drug responders compared with nonresponders (p = .0481), which was the only independent variable for predicting antiepileptic drug response (OR = 10.07, p = .0434). In addition, we found that the volumes of the corpus callosum were significantly correlated with the mean diffusion measures (fractional anisotropy, r = .408, p = .0027; mean diffusivity, r = -0.403, p = .0028) in normal controls.

CONCLUSIONS

We demonstrated that the volumes of the corpus callosum were different according to antiepileptic drug responses in patients with newly diagnosed focal epilepsy, which might suggest that the volumes of the corpus callosum could be a new biomarker for predicting responses to antiepileptic drugs.