Exploring ASL perfusion MRI as a substitutive modality for 18F-FDG PET in determining the laterality of mesial temporal lobe epilepsy.

OBJECTIVE: The aim of this investigation was to determine whether a correlation could be discerned between perfusion acquired through ASL MRI and metabolic data acquired via 18F-fluorodeoxyglucose (18F-FDG) PET in mesial temporal lobe epilepsy (mTLE). METHODS: ASL MRI and 18F-FDG PET data were gathered from 22 mTLE patients. Relative cerebral blood flow (rCBF) asymmetry index (AIs) were measured using ASL MRI, and standardized uptake value ratio (SUVr) maps were obtained from 18F-FDG PET, focusing on bilateral vascular territories and key bitemporal lobe structures (amygdala, hippocampus, and parahippocampus). Intra-group comparisons were carried out to detect hypoperfusion and hypometabolism between the left and right brain hemispheres for both rCBF and SUVr in right and left mTLE. Correlations between the two AIs computed for each modality were examined. RESULTS: Significant correlations were observed between rCBF and SUVr AIs in the middle temporal gyrus, superior temporal gyrus, and hippocampus. Significant correlations were also found in vascular territories of the distal posterior, intermediate anterior, intermediate middle, proximal anterior, and proximal middle cerebral arteries. Intra-group comparisons unveiled significant differences in rCBF and SUVr between the left and right brain hemispheres for right mTLE, while hypoperfusion and hypometabolism were infrequently observed in any intracranial region for left mTLE. CONCLUSION: The study's findings suggest promising concordance between hypometabolism estimated by 18F-FDG PET and hypoperfusion determined by ASL perfusion MRI. This raises the possibility that, with prospective technical enhancements, ASL perfusion MRI could be considered an alternative modality to 18F-FDG PET in the future.

Dynamic Functional Connectivity Analysis Using Network-Based Brain State Identification, Application on Temporal Lobe Epilepsy.

Epilepsy is a brain network disorder caused by discharges of interconnected groups of neurons and resulting brain dysfunction. The brain network can be characterized by intra- and inter-regional functional connectivity (FC). However, since the BOLD signal is inherently non-stationary, the FC is evidenced to be varying over time. Considering the dynamic characteristics of the functional network, we aimed to obtain dynamic brain states and their properties using network-based analyses for the comparison of healthy control and temporal lobe epilepsy (TLE) groups and also lateralization of TLE patients. We used dwelling time, transition time, and brain network connection in each state as the dynamic features for this purpose. Results showed a significant difference in dwelling time and transition time between the healthy control group and both left TLE and right TLE groups and also a significant difference in brain network connections between the left and right TLE groups.

Assessments of variability in cortical and subcortical measurements and within-network connectivity of the brain using test-retest data.

The evaluation and diagnosis of structural changes in brain caused by disease or treatment over time has become one of the important applications of medical imaging methods, in particular MRI, and it is growing. It is critical to evaluate the reliability of the changes in measurements observed in an individual patient for any clinical decision-making. In this paper, we calculated the repeatability coefficient (RC) as a measure of uncertainty for MRI measurements of subcortical volumes and cortical thickness, and within-network connectivity using test-retest data of 20 healthy subjects. We also evaluated changes in 13 patients who received 20 sessions of transcranial magnetic stimulation as a treatment. The most reliable measure seems to be in the thickness of the left occipital with RC% of 3.5 and the least reliable measure is the brain connectivity within visual network using Yeo's atlas with RC% of 29.4. The most sensitive measure to the percentage of true changes in treated patients is the connectivity within subcortical network of AAL with 76.9%.Clinical Relevance- The results of this study can be useful for evaluating changes in the gray matter structures or functional connectivity of the brain due to a neurological disease such as Alzheimer's or Parkinson's. Also, the obtained results can be used to evaluate the changes caused by any intervention or treatment that may have any positive or negative effect on the brain.

Imaging the neurovascular unit in health and neurodegeneration: a scoping review of interdependencies between MRI measures.

The neurovascular unit (NVU) is a complex structure that facilitates nutrient delivery and metabolic waste clearance, forms the blood-brain barrier (BBB), and supports fluid homeostasis in the brain. The integrity of NVU subcomponents can be measured in vivo using magnetic resonance imaging (MRI), including quantification of enlarged perivascular spaces (ePVS), BBB permeability, cerebral perfusion and extracellular free water. The breakdown of NVU subparts is individually associated with aging, pathology, and cognition. However, how these subcomponents interact as a system, and how interdependencies are impacted by pathology remains unclear. This systematic scoping review identified 26 studies that investigated the inter-relationships between multiple subcomponents of the NVU in nonclinical and neurodegenerative populations using MRI. A further 112 studies investigated associations between the NVU and white matter hyperintensities (WMH). We identify two putative clusters of NVU interdependencies: a 'vascular' cluster comprising BBB permeability, perfusion and basal ganglia ePVS; and a 'fluid' cluster comprising ePVS, free water and WMH. Emerging evidence suggests that subcomponent coupling within these clusters may be differentially related to aging, neurovascular injury or neurodegenerative pathology.

Dynamic causal modeling of reorganization of memory and language networks in temporal lobe epilepsy.

OBJECTIVE: To evaluate the alterations of language and memory functions using dynamic causal modeling, in order to identify the epileptogenic hemisphere in temporal lobe epilepsy (TLE). METHODS: Twenty-two patients with left TLE and 13 patients with right TLE underwent functional magnetic resonance imaging (fMRI) during four memory and four language mapping tasks. Dynamic causal modeling (DCM) was employed on fMRI data to examine effective directional connectivity in memory and language networks and the alterations in people with TLE compared to healthy individuals. RESULTS: DCM analysis suggested that TLE can influence the memory network more widely compared to the language network. For memory mapping, it demonstrated overall hyperconnectivity from the left hemisphere to the other cranial regions in the picture encoding, and from the right hemisphere to the other cranial regions in the word encoding tasks. On the contrary, overall hypoconnectivity was seen from the brain hemisphere contralateral to the seizure onset in the retrieval tasks. DCM analysis further manifested hypoconnectivity between the brain's hemispheres in the language network in patients with TLE compared to controls. The CANTAB® neuropsychological test revealed a negative correlation for the left TLE and a positive correlation for the right TLE cohorts for the connections extracted by DCM that were significantly different between the left and right TLE cohorts. INTERPRETATION: In this study, dynamic causal modeling evidenced the reorganization of language and memory networks in TLE that can be used for a better understanding of the effects of TLE on the brain's cognitive functions.

EEG-based functional connectivity for tactile roughness discrimination.

Tactile sensation and perception involve cooperation between different parts of the brain. Roughness discrimination is an important phase of texture recognition. In this study, we investigated how different roughness levels would influence the brain network characteristics. We recorded EEG signals from nine right-handed healthy subjects who underwent touching three surfaces with different levels of roughness. The experiment was separately repeated in 108 trials for each hand for both static and dynamic touch. For estimation of the functional connectivity between brain regions, the phase lag index method was employed. Frequency-specific connectivity patterns were observed in the ipsilateral and contralateral hemispheres to the hand of interest, for delta, theta, alpha, and beta frequency bands under the study. A number of connections were identified to be in charge of discrimination between surfaces in both alpha and beta frequency bands for the left hand in static touch and for the right hand in dynamic touch. In addition, common connections were determined in both hands for all three roughness in alpha band for static touch and in theta band for dynamic touch. The common connections were identified for the smooth surface in beta band for static touch and in delta and alpha bands for dynamic touch. As observed for static touch in alpha band and for dynamic touch in theta band, the number of common connections between the two hands was decreased by increasing the surface roughness. The results of this research would extend the current knowledge about tactile information processing in the brain. Supplementary Information: The online version contains supplementary material available at 10.1007/s11571-022-09876-1.

Prediction of Normal Tissue Complication Probability (NTCP) After Radiation Therapy Using Imaging and Molecular Biomarkers and Multivariate Modelling.

The aim of this study was to design a predictive radiobiological model of normal brain tissue in low-grade glioma following radiotherapy based on imaging and molecular biomarkers. Fifteen patients with primary brain tumors prospectively participated in this study and underwent radiation therapy. Magnetic resonance imaging (MRI) was obtained from the patients, including T1- and T2-weighted imaging and diffusion tensor imaging (DTI), and a generalized equivalent dose (gEUD) was calculated. The radiobiological model of the normal tissue complication probability (NTCP) was performed using the variables gEUD; axial diffusivity (AD) and radial diffusivity (RD) of the corpus callosum; and serum protein S100B by univariate and multivariate logistic regression XLIIIrd Sir Peter Freyer Memorial Lecture and Surgical Symposium (2018). Changes in AD, RD, and S100B from baseline up to the 6 months after treatment had an increasing trend and were significant in some time points (P-value < 0.05). The model resulting from RD changes in the 6 months after treatment was significantly more predictable of necrosis than other univariate models. The bivariate model combining RD changes in Gy40 dose-volume and gEUD, as well as the trivariate model obtained using gEUD, RD, and S100B, had a higher predictive value among multivariate models at the sixth month of the treatment. Changes in RD diffusion indices and in serum protein S100B value were used in the early-delayed stage as reliable biomarkers for predicting late-delayed damage (necrosis) caused by radiation in the corpus callosum. Current findings could pave the way for intervention therapies to delay the severity of damage to white matter structures, minimize cognitive impairment, and improve the quality of life of patients with low-grade glioma.

Alteration of intracranial blood perfusion in temporal lobe epilepsy, an arterial spin labeling study.

Background: A critical necessity before surgical resection in mesial temporal lobe epilepsy (mTLE) is lateralizing the seizure focus in the temporal lobe. This study aimed to investigate the differences in perfusion pattern changes in right and left mTLE. Methods: 42 mTLE patients (22 left and 20 right mTLE) and 14 controls were surveyed with pulsed arterial spin labeling at 3.0 T. The mean cerebral blood flow (CBF) and asymmetry index (AI) were calculated in the bilateral temporal lobe, amygdala, hippocampus, parahippocampus, and nine bilateral vascular territories ROIs. The alterations in whole-brain CBF were identified using statistical parametric mapping (SPM). Results: CBF decreased in ipsilateral sides in both epilepsy subcohorts, with right mTLE showing a significant difference in most ROIs while left mTLE exhibiting no significant change. CBF comparison of left mTLE and controls showed a significant drop in ROI analysis in left middle temporal and left intermediate posterior cerebral artery and in AI analysis in parahippocampus, distal anterior cerebral artery, distal middle cerebral artery, and intermediate anterior cerebral artery. CBF hypoperfusion was seen in ROI analysis in the left intermediate anterior cerebral artery, left middle temporal, right middle temporal, left superior temporal in the right mTLE compared to controls. Left mTLE CBF differed significantly from right mTLE CBF in right distal middle cerebral artery ROI and AI of proximal middle cerebral artery. Conclusion: Our result revealed that mTLE affects extratemporal regions and both mTLE subcohorts with different perfusion patterns, which may enhance the performance of preoperative MRI assessment in lateralization procedures.

A Memory-efficient Deep Framework for Multi-Modal MRI-based Brain Tumor Segmentation.

Automatic Brain Tumor Segmentation (BraTS) from MRI plays a key role in diagnosing and treating brain tumors. Although 3D U-Nets achieve state-of-the-art results in BraTS, their clinical use is limited due to requiring high-end GPU with high memory. To address the limitation, we utilize several techniques for customizing a memory-efficient yet ac-curate deep framework based on 2D U-nets. In the framework, the simultaneous multi-label tumor segmentation is decomposed into fusion of sequential single-label (binary) segmentation tasks. In addition to reducing the memory consumption, it may also improve the segmentation accuracy since each U-net focuses on a sub-task, simpler than whole BraTS segmentation task. Extensive data augmentations on multi-modal MRI and the batch dice-loss function are also employed to further increase the generalization accuracy. Experiments on BraTS 2020 demonstrate that our framework almost achieves state-of-the-art results. Dice scores of 0.905, 0.903, and 0.822 for whole tumor, tumor core, and enhancing tumor are accomplished on the testing set. Moreover, our customized framework is executable on budget-GPUs with minimum requirement of only 2G RAM. Clinical relevance- We develop a memory-efficient deep Brain tumor segmentation tool that significantly reduces the hardware requirement of tumor segmentation while maintaining comparable accuracy and time. These advantages make our framework suitable for widespread use in clinical applications, especially in low-income regions. We plan to release the framework as a part of a free clinical brain imaging analysis tool. The code for this framework is publicly available:https://github.com/Nima-Hs/BraTS.

Deep learning framework for prediction of infection severity of COVID-19.

With the onset of the COVID-19 pandemic, quantifying the condition of positively diagnosed patients is of paramount importance. Chest CT scans can be used to measure the severity of a lung infection and the isolate involvement sites in order to increase awareness of a patient's disease progression. In this work, we developed a deep learning framework for lung infection severity prediction. To this end, we collected a dataset of 232 chest CT scans and involved two public datasets with an additional 59 scans for our model's training and used two external test sets with 21 scans for evaluation. On an input chest Computer Tomography (CT) scan, our framework, in parallel, performs a lung lobe segmentation utilizing a pre-trained model and infection segmentation using three distinct trained SE-ResNet18 based U-Net models, one for each of the axial, coronal, and sagittal views. By having the lobe and infection segmentation masks, we calculate the infection severity percentage in each lobe and classify that percentage into 6 categories of infection severity score using a k-nearest neighbors (k-NN) model. The lobe segmentation model achieved a Dice Similarity Score (DSC) in the range of [0.918, 0.981] for different lung lobes and our infection segmentation models gained DSC scores of 0.7254 and 0.7105 on our two test sets, respectfully. Similarly, two resident radiologists were assigned the same infection segmentation tasks, for which they obtained a DSC score of 0.7281 and 0.6693 on the two test sets. At last, performance on infection severity score over the entire test datasets was calculated, for which the framework's resulted in a Mean Absolute Error (MAE) of 0.505 ± 0.029, while the resident radiologists' was 0.571 ± 0.039.

A Fast and Memory-Efficient Brain MRI Segmentation Framework for Clinical Applications.

Current segmentation tools of brain MRI provide quantitative structural information for diagnosing neurological disorders. However, their clinical application is generally limited due to high memory usage and time consumption. Although 3D CNN-based segmentation methods have recently achieved the state-of-the-art and come up with timely available results, they heavily require high memory GPUs. In this paper, we customize a memory-efficient (GPU) brain structure segmentation framework, named FLBS, based on nnU-nets which enables our framework to adapt its architecture based on memory constraints dynamically. To further reduce the need for memory, we also reduce multi-label brain segmentation to the fusion of sequential single-label segmentations. In the first step, single label patches are extracted from the T1w and segmentation maps by locating the approximate area of each structure on the MNI305 template, including the safety margin. These considerations not only decrease the hardware usage but also maintains comparable computational time. Moreover, the target brain structures are customizable based on the specific clinical applications. We evaluate the performance in terms of Dice coefficient, runtime, and GPU requirement on OASIS-3 and CoRR-BNU1 datasets. The validation results show our comparable accuracies with state-of-the-arts and confirm the generalizability on unseen datasets while significantly reducing GPU requirements and maintaining runtime duration. Our framework is also executable on a budget GPU with a minimum requirement of 4G RAM. Clinical Relevance- We develop a memory-efficient deep Brain MRI segmentation tool that significantly reduces the hardware requirement of MRI segmentation while maintaining comparable accuracy and time. These advantages make FLBS suitable for widespread use in clinical applications especially for clinics with a limited budget. We plan to release the framework as a part of a free clinical brain imaging analysis tool. The code for this framework is publicly available.

Prediction of delay discounting in intertemporal decisions by future thinking: Accounting for fluency, contents, and functions of future thoughts.

PURPOSE: To study the variations of delay discounting rates as a function of fluency, contents, and functions of future thoughts in healthy subjects. BACKGROUND: Delay discounting (DD) is a concept that can measure a frequent tendency toward smaller, yet immediate rewards, while a delayed reward is greater in value. DD describes people's choices in intertemporal decisions and is associated with self-control. Future thinking (FT) and having a vivid imagination of the future can reduce individuals' DD rates. However, constructing a specific episodic future representation was merely studied in relation to DD. Although fluency and contents of future thoughts have been reported related to various disorders and behaviors, their association with DD has not been previously addressed. METHODS: The present study applies a verbal fluency task named the personal future task (PFT), the functions of future thinking scale (FoFTS), and the 27-item delay discounting questionnaire (DDQ) in order to assess fluency, contents, and functions of future thoughts, and delay discounting in healthy subjects (N = 114, Female = 64%, Male = 36%, Mage = 34.22, SDage = 7.15). RESULTS: Findings indicate that fluency of future thoughts is associated with DD. Among the contents of FT categories, financial contents (future thoughts about money and real estate matters), and regarding functions of FT, engaging in FT for planning are related to DD. Due to the final model, the above-mentioned correlated variables can be considered as significant predictors of intertemporal choices when controlling for education and gender (R2  = 0.4, Adjusted R2  = 0.33, F = 5.186, p-value = 0.001). CONCLUSION: The frequency of future thoughts one can generate, specifically future thoughts about financial contents, is associated with less short-sighted intertemporal decisions. The former relationship is enhanced for longer delays (e.g., 5-10 years). Besides, individuals who frequently engage in FT for planning (planning out sequences of actions) discount future rewards to a lesser extent.

A Preliminary Study of Alterations in Iron Disposal and Neural Activity in Ischemic Stroke.

Purpose: The study aimed to evaluate the postrehabilitation changes in deep gray matter (DGM) nuclei, corticospinal tract (CST), and motor cortex area, involved in motor tasks in patients with ischemic stroke. Methods: Three patients participated in this study, who had experienced an ischemic stroke on the left side of the brain. They underwent a standard rehabilitation program for four consecutive weeks, including transcranial direct current stimulation (tDCS), neuromuscular electrical stimulation (NMES), and occupational therapy. The patients' motor ability was evaluated by Fugl-Meyer assessment-upper extremity (FMA-UE) and Wolf motor function test (WMFT). Multimodal magnetic resonance imaging (MRI) was acquired from the patients by a 3 Tesla machine before and after the rehabilitation. The magnetic susceptibility changes were examined in DGM nuclei including the bilateral caudate (CA), putamen (PT), globus pallidus (GP), and thalamus (TH) using quantitative susceptibility mapping (QSM). Functional MRI (fMRI) in the motor cortex areas was acquired to evaluate the postrehab functional motor activity. The three-dimensional corticospinal tract (CST) was reconstructed using diffusion-weighted imaging (DWI) and diffusion tensor tractography (DTT), and the fractional anisotropy (FA) was measured along the tract. Ultimately, the relationship between the structural and functional changes was evaluated in CST and motor cortex. Results: Postrehabilitation FMA-UE and WMFT scores increased for all patients compared to the prerehabilitation. QSM analysis revealed increasing in susceptibility values in GP and CA in all patients at the ipsilesional hemisphere. By fMRI analysis, the ipsilesional hemisphere demonstrated an increase in functional activity in motor areas for all 3 patients. In the ipsilesional hemisphere, the fractional anisotropy (FA) was increased in CST in two patients, while the mean diffusivity (MD) was decreased in CA in a patient, in PT and TH in another patient, and in PT in two patients. Conclusion: This preliminary study demonstrates that the magnetic susceptibility may decrease at some ipsilesional DGM nuclei after tDCS, NMES, and occupational therapy for patients with ischemic stroke, suggesting a drop in the level of iron deposition, which may be associated with an increase in the level of activity in motor cortex after rehabilitation.

Global and local shape features of the hippocampus based on Laplace-Beltrami eigenvalues and eigenfunctions: a potential application in the lateralization of temporal lobe epilepsy.

Using magnetic resonance (MR) images to evaluate changes in the shape of the hippocampus has been an active research topic. This paper presents a new shape analysis approach to quantify and visualize deformations of the hippocampus in epilepsy. The proposed method is based on Laplace-Beltrami (LB) eigenvalues and eigenfunctions as isometric invariant shape features, and thus, the procedure does not require any image registration. In addition to the LB-based shape features, total hippocampal volume and surface area are calculated using manually segmented images. Theses shape and volumetric descriptors are used to distinguish the patients with temporal lobe epilepsy (TLE) (N = 55) from healthy control subjects (N = 12, age = 32.2 ± 9.1, sex (M/F) = 6/6) and patients with right TLE (N = 26, age = 45.1 ± 11.0, sex (M/F) = 9/17) from left TLE (N = 29, age = 45.4 ± 11.9, sex (M/F) = 10/19). Experimental results illustrate the usefulness of the proposed approach for the diagnosis and lateralization of TLE with 93.0% and 86.4% of the cases, respectively. Moreover, the proposed method outperforms the volumetric analysis in terms of both sensitivity (94.9% vs. 88.1%) and specificity (83.3% vs. 50.0%) of the lateralization. The analysis of local hippocampal thickness variations suggests significant deformation in both ipsilateral and contralateral hippocampi of epileptic patients, while there were no differences between right and left hippocampi in controls. It is anticipated that the proposed method could be advantageous in the presurgical evaluation of patients with drug-resistant epilepsy; however, further validation of the method using a larger dataset is required.

Statistical analysis of COVID-19 infection severity in lung lobes from chest CT.

Detection of the COVID 19 virus is possible through the reverse transcription-polymerase chain reaction (RT-PCR) kits and computed tomography (CT) images of the lungs. Diagnosis via CT images provides a faster diagnosis than the RT-PCR method does. In addition to low false-negative rate, CT is also used for prognosis in determining the severity of the disease and the proposed treatment method. In this study, we estimated a probability density function (PDF) to examine the infections caused by the virus. We collected 232 chest CT of suspected patients and had them labeled by two radiologists in 6 classes, including a healthy class and 5 classes of different infection severity. To segment the lung lobes, we used a pre-trained U-Net model with an average Dice similarity coefficient (DSC) greater than 0.96. First, we extracted the PDF to grade the infection of each lobe and selected five specific thresholds as feature vectors. We then assigned this feature vector to a support vector machine (SVM) model and made the final prediction of the infection severity. Using the T-Test statistics, we calculated the p-value at different pixel thresholds and reported the significant differences in the pixel values. In most cases, the p-value was less than 0.05. Our developed model was developed on roughly labeled data without any manual segmentation, which estimated lung infection involvements with the area under the curve (AUC) in the range of [0.64, 0.87]. The introduced model can be used to generate a systematic automated report for individual patients infected by COVID-19.

A Preliminary Study of the Efficacy of Transcranial Direct Current Stimulation in Trigeminal Neuralgia.

The purpose of this study is to assess the efficacy of transcranial direct current stimulation (tDCS) in patients with treatment-refractory trigeminal neuralgia (TN) and examine the utility of neuroimaging methods in identifying markers of such efficacy. Six patients with classical TN refractory to maximal medical treatment, underwent tDCS (three cases inhibitory/cathodic and three cases excitatory/anodic stimulation). All patients underwent pre- and posttreatment functional magnetic resonance imaging (fMRI) during block-design tasks (i.e., Pain, Pain + tDCS, tDCS) as well as single-shell diffusion MRI (dMRI) acquisition. The precise locations of tDCS electrodes were identified by neuronavigation. Five therapeutic tDCS sessions were carried out for each patient with either anodic or cathodic applications. The Numeric Rating Scale of pain (NRS) and the Headache Disability Index (HDI) were used to score the subjective efficacy of treatment. Altered activity of regional sites was identified by fMRI and associated changes in the spinothalamocortical sensory tract (STCT) were measured by the dMRI indices of fractional anisotropy (FA) and mean diffusivity (MD). Fiber counts of the bilateral trigeminal root entry zone (REZ) were performed as an added measure of fiber loss or recovery. All patients experienced a significant reduction in pain scores with a substantial decline in HDI (P value < 0.01). Following a course of anodic tDCS, the ipsilateral caudate, globus pallidus, somatosensory cortex, and the contralateral globus pallidus showed a significantly attenuated activation whereas cathodic tDCS treatment resulted in attenuation of the thalamus and globus pallidus bilaterally, and the somatosensory cortex and anterior cingulate gyrus contralaterally. dMRI analysis identified a substantial increase (>50%) in the number of contralateral sensory fibers in the STCT with either anodic or cathodic tDCS treatment in four of the six patients. A significant reduction in FA (>40%) was observed in the ipsilateral REZ in the posttreatment phase in five of the six patients. Preliminary evidence suggests that navigated tDCS presents a promising method for alleviating the pain of TN. Different patterns of activation manifested by anodic and cathodic stimulation require further elaboration to understand their implication. Activation and attenuation of responses at various sites may provide further avenues for condition treatment.

Comparison of multimodal findings on epileptogenic side in temporal lobe epilepsy using self-organizing maps.

OBJECTIVE: To develop a decision-making tool to evaluate and compare the performance of neuroimaging markers with clinical findings and the significance of attributes for presurgical lateralization of mesial temporal lobe epilepsy (mTLE). METHODS: Thirty-five unilateral mTLE patients who qualified as candidates for surgical resection were studied. Seizure semiology, ictal EEG, ictal epileptogenic zone, interictal-irritative zone, and MRI findings were used as clinical markers. Hippocampal T1 volumetry and FLAIR intensity, DTI estimated; mean diffusivity (MD) in the hippocampus and fractional anisotropy (FA) in posteroinferior cingulum and crus of fornix, and the output of logistic regression method on volumetrics of the hippocampus, amygdala, and thalamus were adopted as neuroimaging markers. The self-organizing map (SOM) method was applied to markers to provide predictive methods for mTLE lateralization. RESULTS: The SOM clustered all clinical attributes correctly with 100% accuracy and sensitivity for both the left and right mTLE. Among the clinical markers, seizure semiology and interictal-irrelative zone are the most sensitive attribute for the left-mTLE group lateralization. The accuracy achieved by applying the SOM method to the neuroimaging attributes was 94%, while the sensitivity was achieved 90% for left and 100% for right mTLE. SOM evidence indicated that the hippocampal volume is the most sensitive attribute for the prediction of the laterality in left-mTLE groups. CONCLUSION: The proposed SOM method showed that neuroimaging markers may not replace with clinical findings. Nevertheless, multimodal neuroimaging can play an effective role in preoperative lateralization to reduce the costs and risks of surgical resection.

Early Detection of Radiation-Induced Injury and Prediction of Cognitive Deficit by MRS Metabolites in Radiotherapy of Low-Grade Glioma.

PURPOSE: To compare the sensitivity of MRS metabolites and MoCA and ACE-R cognitive tests in the detection of radiation-induced injury in low grade glioma (LGG) patients in early and early delayed postradiation stages. METHODS: MRS metabolite ratios of NAA/Cr and Cho/Cr, ACE-R and MoCA cognitive tests, and dosimetric parameters in corpus callosum were analyzed during RT and up to 6-month post-RT for ten LGG patients. RESULTS: Compared to pre RT baseline, a significant decline in both NAA/Cr and Cho/Cr in the corpus callosum was seen at the 4th week of RT, 1, 3, and 6-month post-RT. These declines were detected at least 3 months before the detection of declines in cognitive functions by ACE-R and MoCA tools. Moreover, NAA/Cr alterations at 4th week of RT and 1-month post-RT were significantly negatively correlated with the mean dose received by the corpus callosum, as well as the corpus callosum 40 Gy dose volume, i.e., the volume of the corpus callosum receiving a dose greater than 40 Gy. CONCLUSION: MRS-based biomarkers may be more sensitive than the state-of-the-art cognitive tests in the prediction of postradiation cognitive impairments. They would be utilized in treatment planning and dose sparing protocols, with a specific focus on the corpus callosum in the radiation therapy of LGG patients.

Temporal and extratemporal atrophic manifestation of temporal lobe epilepsy using voxel-based morphometry and corticometry: clinical application in lateralization of epileptogenic zone.

BACKGROUND: Advances in MRI acquisition and data processing have become important for revealing brain structural changes. Previous studies have reported widespread structural brain abnormalities and cortical thinning in patients with temporal lobe epilepsy (TLE), as the most common form of focal epilepsy. METHODS: In this research, healthy control cases (n = 20) and patients with left TLE (n = 19) and right TLE (n = 14) were recruited, all underwent 3.0 T MRI with magnetization-prepared rapid gradient echo sequence to acquire T1-weighted images. Morphometric alterations in gray matter were identified using voxel-based morphometry (VBM). Volumetric alterations in subcortical structures and cortical thinning were also determined. RESULTS: Patients with left TLE demonstrated more prevailing and widespread changes in subcortical volumes and cortical thickness than right TLE, mainly in the left hemisphere, compared to the healthy group. Both VBM analysis and subcortical volumetry detected significant hippocampal atrophy in ipsilateral compared to contralateral side in TLE group. In addition to hippocampus, subcortical volumetry found the thalamus and pallidum bilaterally vulnerable to the TLE. Furthermore, the TLE patients underwent cortical thinning beyond the temporal lobe, affecting gray matter cortices in frontal, parietal, and occipital lobes in the majority of patients, more prevalently for left TLE cases. Exploiting volume changes in individual patients in the hippocampus alone led to 63.6% sensitivity and 100% specificity for lateralization of TLE. CONCLUSION: Alteration of gray matter volumes in subcortical regions and neocortical temporal structures and also cortical gray matter thickness were evidenced as common effects of epileptogenicity, as manifested by the majority of cases in this study.

Distinct patterns of hippocampal subfield volume loss in left and right mesial temporal lobe epilepsy.

OBJECTIVE: To investigate the pattern and severity of hippocampal subfield volume loss in patients with left and right mesial temporal lobe epilepsy (mTLE) using quantitative MRI volumetric analysis. METHODS: A total of 21 left and 14 right mTLE subjects, as well as 15 healthy controls, were enrolled in this cross-sectional study. A publically available magnetic resonance imaging (MRI) brain volumetry system (volBrain) was used for volumetric analysis of hippocampal subfields. The T1-weighted images were processed with a HIPS pipeline. RESULTS: A distinct pattern of hippocampal subfield atrophy was found between left and right mTLE patients when compared with controls. Patients with left mTLE exhibited ipsilateral hippocampal atrophy and segmental volume depletion of the Cornu Ammonis (CA) 2/CA3, CA4/dentate gyrus (DG), and strata radiatum-lacunosum-moleculare (SR-SL-SM). Those with right mTLE exhibited similar ipsilateral hippocampal atrophy but with additional segmental CA1 volume depletion. More extensive bilateral subfield volume loss was apparent with right mTLE patients. CONCLUSION: We demonstrate that left and right mTLE patients show a dissimilar pattern of hippocampal subfield atrophy, suggesting the pathophysiology of epileptogenesis in left and right mTLE to be different.