Diffusion spectrum imaging predicts hippocampal sclerosis in mesial temporal lobe epilepsy patients

Advances in diagnostic imaging and interventional treatment of aphasia after basal ganglia stroke

Post-stroke basal ganglia aphasia is an unusual and transient form of aphasia resulting from basal ganglia damage. It is commonly believed that the generation of language function primarily resides in regular language regions of the brain; however, recent findings indicate a prevalence of basal ganglia stroke aphasia as high as 22%. Subcortical structures (e.g., basal ganglia) also play an important role in language processing. Aphasia seriously affects the quality of life and functional outcomes of patients, and early diagnosis and intervention are important for the prognosis of and rehabilitation from aphasia after basal ganglia stroke. In recent years, the main diagnostic methods for basal ganglia aphasia include diffusion tensor imaging, diffusion spectral imaging, and functional magnetic resonance imaging, which explore the changes in patients with basal ganglia aphasia compared to those without from the perspectives of fiber tract imaging and brain function alteration, respectively, and are able to predict the recovery of patients before and after treatment. Treatment for post-stroke basal ganglia aphasia includes transcranial magnetic stimulation, a recent emerging therapeutic technique, in addition to conventional medications and speech rehabilitation. Consequently, understanding this condition is crucial. This review delves into its causes, imaging methods, and therapeutic interventions, offering a systematic and comprehensive analysis of these aspects.

Advances in Neuroimaging and Multiple Post-Processing Techniques for Epileptogenic Zone Detection of Drug-Resistant Epilepsy

Among the approximately 20 million patients with drug-resistant epilepsy (DRE) worldwide, the vast majority can benefit from surgery to minimize seizure reduction and neurological impairment. Precise preoperative localization of epileptogenic zone (EZ) and complete resection of the lesions can influence the postoperative prognosis. However, precise localization of EZ is difficult, and the structural and functional alterations in the brain caused by DRE vary by etiology. Neuroimaging has emerged as an approach to identify the seizure-inducing structural and functional changes in the brain, and magnetic resonance imaging (MRI) and positron emission tomography (PET) have become routine noninvasive imaging tools for preoperative evaluation of DRE in many epilepsy treatment centers. Multimodal neuroimaging offers unique advantages in detecting EZ, especially in improving the detection rate of patients with negative MRI or PET findings. This approach can characterize the brain imaging characteristics of patients with DRE caused by different etiologies, serving as a bridge between clinical and pathological findings and providing a basis for individualized clinical treatment plans. In addition to the integration of multimodal imaging modalities and the development of special scanning sequences and image post-processing techniques for early and precise localization of EZ, the application of deep machine learning for extracting image features and deep learning-based artificial intelligence have gradually improved diagnostic efficiency and accuracy. These improvements can provide clinical assistance for precisely outlining the scope of EZ and indicating the relationship between EZ and functional brain areas, thereby enabling standardized and precise surgery and ensuring good prognosis. However, most existing studies have limitations imposed by factors such as their small sample sizes or hypothesis-based study designs. Therefore, we believe that the application of neuroimaging and post-processing techniques in DRE requires further development and that more efficient and accurate imaging techniques are urgently needed in clinical practice. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY: Stage 2.

The alteration of cortical microstructure similarity in drug-resistant epilepsy correlated with mTOR pathway genes

BACKGROUND

Drug-resistant epilepsy (DRE) is associated with distributed laminar disruptions due to cytoarchitectonic pathologies, which may be characterized by multimodal MRI approaches such as morphometric similarity networks (MSNs). However, the genetic and histological underpinning of MSN alterations in DRE remains poorly understood, hampering its clinical application.

METHODS

We enrolled 60 patients with DRE and 23 controls, acquiring T1 and diffusion spectrum imaging data with a 3.0T GE SIGNA Premier scanner. Morphometric similarity networks (MSNs) were constructed and analyzed to identify microstructure similarity differences between patients and controls. Subsequently, patient-specific MSN alteration patterns were associated with gene expression using the GAMBA tool, and layer-specific neuronal signature mapping were also applied. During these analyses, sex and age were adjusted as covariates and multiple comparisons corrections were applied when appropriate.

FINDINGS

We observed widespread MSN changes in patients with DRE and identified five distinct MSN alteration patterns. Major patterns presented pattern-specific associations with expressions of epilepsy-related genes, particularly involving the mTOR pathway. Histological analysis confirmed the presence of cortical microstructure changes in areas with MSN alterations and revealed cellular abnormalities matching the aforementioned genetic risks.

INTERPRETATION

Our findings highlight the potential of quantifying laminar-related microstructure integrity using MSN to uncover the cytoarchitectonic changes in the pathophysiology of DRE. This approach may facilitate the identification of genetic and histological underpinnings of MSN alterations in DRE, ultimately aiding in the development of targeted therapeutic strategies.

FUNDINGS

The National Natural Science Foundation of China, the Ministry of Science and Technology of the People's Republic of China, and the Beijing Municipal Health Commission.

Research Progress in Diffusion Spectrum Imaging

Studies have demonstrated that many regions in the human brain include multidirectional fiber tracts, in which the diffusion of water molecules within image voxels does not follow a Gaussian distribution. Therefore, the conventional diffusion tensor imaging (DTI) that hypothesizes a single fiber orientation within a voxel is intrinsically incapable of revealing the complex microstructures of brain tissues. Diffusion spectrum imaging (DSI) employs a pulse sequence with different b-values along multiple gradient directions to sample the diffusion information of water molecules in the entire q-space and then quantitatively estimates the diffusion profile using a probability density function with a high angular resolution. Studies have suggested that DSI can reliably observe the multidirectional fibers within each voxel and allow fiber tracking along different directions, which can improve fiber reconstruction reflecting the true but complicated brain structures that were not observed in the previous DTI studies. Moreover, with increasing angular resolution, DSI is able to reveal new neuroimaging biomarkers used for disease diagnosis and the prediction of disorder progression. However, so far, this method has not been used widely in clinical studies, due to its overly long scanning time and difficult post-processing. Within this context, the current paper aims to conduct a comprehensive review of DSI research, including the fundamental principles, methodology, and application progress of DSI tractography. By summarizing the DSI studies in recent years, we propose potential solutions towards the existing problem in the methodology and applications of DSI technology as follows: (1) using compressed sensing to undersample data and to reconstruct the diffusion signal may be an efficient and promising method for reducing scanning time; (2) the probability density function includes more information than the orientation distribution function, and it should be extended in application studies; and (3) large-sample study is encouraged to confirm the reliability and reproducibility of findings in clinical diseases. These findings may help deepen the understanding of the DSI method and promote its development in clinical applications.

Combined [18F]FDG-PET with MRI structural patterns in predicting post-surgical seizure outcomes in temporal lobe epilepsy patients

OBJECTIVES

To integrate the glucose metabolism measured using [¹⁸F]FDG PET/CT and anatomical features measured using MRI to forecast the post-surgical seizure outcomes of intractable temporal lobe epilepsy.

METHODS

This retrospective study enrolled 63 patients with drug-resistant temporal lobe epilepsy. Z-transform of the patients' PET images based on comparison with a database of healthy controls, cortical thickness, and quantitative anisotropy (QA) of the diffusion spectrum imaging concordant/non-concordant with cortical resection was adopted to quantify their predictive values for the post-surgical seizure outcomes.

RESULTS

The PET hypometabolism region was concordant with the surgical field in 47 of the 63 patients. Forty-two patients were seizure-free post-surgery. The sensitivity and specificity of PET in predicting seizure freedom were 89.4% and 68.8%, respectively. Complete resection of foci with overlapped PET, cortical thickness, and QA abnormalities resulted in Engel I in 27 patients, which was a good predictor of seizure freedom with an odds ratio (OR) of 19.57 (95% CI 2.38-161.25, p = 0.006). Hypometabolism involved in multiple lobes (OR = 7.18, 95% CI 1.02-50.75, p = 0.048) and foci of hypometabolism with QA/cortical thickness abnormalities outside surgical field (OR = 14.72, 95% CI 2.13-101.56, p = 0.006) were two major predictors of Engel III/IV outcomes. ORs of QA to predict Engel I and seizure recurrence were 14.64 (95% CI 2.90-73.80, p = 0.001) and 12.01 (95% CI 2.91-49.65, p = 0.001), respectively.

CONCLUSION

Combined PET and structural pattern is helpful to predict the post-surgical seizure outcomes and worse outcomes of Engel III/IV. This might decrease unnecessary surgical injuries to patients who are potentially not amenable to surgery.

KEY POINTS

• A combined metabolic and structural pattern is helpful to predict the post-surgical seizure outcomes. • Favorable post-surgical seizure outcome was most likely reached in patients whose hypometabolism overlapped with the structural changes. • Hypometabolism in multiple lobes and QA or cortical thickness abnormalities outside the surgical field were predictors of worse seizure outcomes of Engel III/IV.