Functional MRI activation patterns of cerebellum in patients with epilepsy and brain tumors

Validity of the MoCA as a cognitive screening tool in epilepsy: Are there implications for global care and research?

OBJECTIVE

This study evaluated the diagnostic performance of a widely available cognitive screener, the Montreal cognitive assessment (MoCA), to detect cognitive impairment in older patients (age ≥ 55) with epilepsy residing in the US, using the International Classification of Cognitive Disorders in Epilepsy (IC-CoDE) as the gold standard.

METHODS

Fifty older adults with focal epilepsy completed the MoCA and neuropsychological measures of memory, language, executive function, and processing speed/attention. The IC-CoDE taxonomy divided participants into IC-CoDE Impaired and Intact groups. Sensitivity and specificity across several MoCA cutoffs were examined. Spearman correlations examined relationships between the MoCA total score and clinical and demographic variables and MoCA domain scores and individual neuropsychological tests.

RESULTS

IC-CoDE impaired patients demonstrated significantly lower scores on the MoCA total, visuospatial/executive, naming, language, delayed recall, and orientation domain scores (Cohen's d range: 0.336-2.77). The recommended MoCA cutoff score < 26 had an overall accuracy of 72%, 88.2% sensitivity, and 63.6% specificity. A MoCA cutoff score < 24 yielded optimal sensitivity (70.6%) and specificity (78.8%), with overall accuracy of 76%. Higher MoCA total scores were associated with greater years of education (p = 0.016) and fewer antiseizure medications (p = 0.049). The MoCA memory domain was associated with several standardized measures of memory, MoCA language domain with category fluency, and MoCA abstraction domain with letter fluency.

SIGNIFICANCE

This study provides initial validation of the MoCA as a useful screening tool for older adults with epilepsy that can be used to identify patients who may benefit from comprehensive neuropsychological testing. Further, we demonstrate that a lower cutoff (i.e., <24) better captures cognitive impairment in older adults with epilepsy than the generally recommended cutoff and provides evidence for construct overlap between MoCA domains and standard neuropsychological tests. Critically, similar efforts in other regions of the world are needed.

PLAIN LANGUAGE SUMMARY

The Montreal cognitive assessment (MoCA) can be a helpful tool to screen for cognitive impairment in older adults with epilepsy. We recommend that adults 55 or older with epilepsy who score less than 24 on the MoCA are referred to a neuropsychologist for a comprehensive evaluation to assess any changes in cognitive abilities and mood.

Neuropsychological Evaluation and Functional Magnetic Resonance Imaging Tasks in the Preoperative Assessment of Patients with Brain Tumors: A Systematic Review

BACKGROUND

Current surgical treatment of gliomas relies on a function-preserving, maximally safe resection approach. Functional Magnetic Resonance Imaging (fMRI) is a widely employed technology for this purpose. A preoperative neuropsychological evaluation should accompany this exam. However, only a few studies have reported both neuropsychological tests and fMRI tasks for preoperative planning-the current study aimed to systematically review the scientific literature on the topic.

METHODS

PRISMA guidelines were followed. We included studies that reported both neuropsychological tests and fMRI. Exclusion criteria were: no brain tumors, underage patients, no preoperative assessment, resting-state fMRI only, or healthy sample population/preclinical studies.

RESULTS

We identified 123 papers, but only 15 articles were included. Eight articles focused on language; three evaluated cognitive performance; single papers studied sensorimotor cortex, prefrontal functions, insular cortex, and cerebellar activation. Two qualitative studies focused on visuomotor function and language. According to some authors, there was a strong correlation between performance in presurgical neuropsychological tests and fMRI. Several papers suggested that selecting well-adjusted and individualized neuropsychological tasks may enable the development of personalized and more efficient protocols. The fMRI findings may also help identify plasticity phenomena to avoid unintentional damage during neurosurgery.

CONCLUSIONS

Most studies have focused on language, the most commonly evaluated cognitive function. The correlation between neuropsychological and fMRI results suggests that altered functions during the neuropsychological assessment may help identify patients who could benefit from an fMRI and, possibly, functions that should be tested. Neuropsychological evaluation and fMRI have complementary roles in the preoperative assessment.

Brain Tumor at Diagnosis: From Cognition and Behavior to Quality of Life

BACKGROUND

The present narrative review aims to discuss cognitive-emotional-behavioral symptoms in adults with brain tumors at the time of diagnosis.

METHODS

The PubMed database was searched considering glioma, pituitary adenoma, and meningioma in adulthood as pathologies, together with cognitive, neuropsychological, or behavioral aspects.

RESULTS

Although a significant number of studies describe cognitive impairment after surgery or treatment in adults with brain tumors, only few focus on cognitive-emotional-behavioral symptoms at diagnosis. Furthermore, the importance of an effective communication and its impact on patients' quality of life and compliance with treatment are seldom discussed.

CONCLUSIONS

Adults with brain tumors have needs in terms of cognitive-emotional-behavioral features that are detectable at the time of diagnosis; more research is needed to identify effective communication protocols in order to allow a higher perceived quality of life in these patients.

Causal Evidence for a Role of Cerebellar Lobulus Simplex in Prefrontal-Hippocampal Interaction in Spatial Working Memory Decision-Making

Spatial working memory (SWM) is a cerebrocerebellar cognitive skill supporting survival-relevant behaviors, such as optimizing foraging behavior by remembering recent routes and visited sites. It is known that SWM decision-making in rodents requires the medial prefrontal cortex (mPFC) and dorsal hippocampus. The decision process in SWM tasks carries a specific electrophysiological signature of a brief, decision-related increase in neuronal communication in the form of an increase in the coherence of neuronal theta oscillations (4-12 Hz) between the mPFC and dorsal hippocampus, a finding we replicated here during spontaneous exploration of a plus maze in freely moving mice. We further evaluated SWM decision-related coherence changes within frequency bands above theta. Decision-related coherence increases occurred in seven frequency bands between 4 and 200 Hz and decision-outcome-related differences in coherence modulation occurred within the beta and gamma frequency bands and in higher frequency oscillations up to 130 Hz. With recent evidence that Purkinje cells in the cerebellar lobulus simplex (LS) represent information about the phase and phase differences of gamma oscillations in the mPFC and dorsal hippocampus, we hypothesized that LS might be involved in the modulation of mPFC-hippocampal gamma coherence. We show that optical stimulation of LS significantly impairs SWM performance and decision-related mPFC-dCA1 coherence modulation, providing causal evidence for an involvement of cerebellar LS in SWM decision-making at the behavioral and neuronal level. Our findings suggest that the cerebellum might contribute to SWM decision-making by optimizing the decision-related modulation of mPFC-dCA1 coherence.

Cognitive impairment in people with epilepsy: Montreal Cognitive Assessment (MoCA) as a screening tool

OBJECTIVES

Although cognitive impairment is common in people with epilepsy, it is often neglected in outpatient clinics. MoCA is a simple and reliable test, which was validated for the cognitive screening of Alzheimer's and vascular dementia. The aim of our study was to evaluate MoCA as a tool for a cognitive screening of people with epilepsy.

METHODS

Our study included 50 people with epilepsy and 46 healthy individuals. All participants took the Slovenian version of the MoCA. Mean age, education and MoCA scores were compared between the two groups.

RESULTS

There was no significant difference between people with epilepsy and the controls in age (47.6, SD 18.1 vs 50.9, SD 14.0 years) or education (12.8, SD 2.8 vs 13.4, SD 2.8 years). People with epilepsy had significantly lower total MoCA scores than did the controls (23.3, SD 4.5 vs 27.5, SD 1.9 points; p < 0.001).

CONCLUSIONS

People with epilepsy achieved a lower score in several cognitive domains compared to the control group. MoCA can be used as an appropriate screening tool for cognitive impairment in people with epilepsy in the outpatient clinic. For a more accurate evaluation, neuropsychological assessments should be used.

Forecasting stroke-like episodes and outcomes in mitochondrial disease

In this retrospective, multicentre, observational cohort study, we sought to determine the clinical, radiological, EEG, genetics and neuropathological characteristics of mitochondrial stroke-like episodes and to identify associated risk predictors. Between January 1998 and June 2018, we identified 111 patients with genetically determined mitochondrial disease who developed stroke-like episodes. Post-mortem cases of mitochondrial disease (n = 26) were identified from Newcastle Brain Tissue Resource. The primary outcome was to interrogate the clinico-radiopathological correlates and prognostic indicators of stroke-like episode in patients with mitochondrial encephalomyopathy, lactic acidosis and stroke-like episodes syndrome (MELAS). The secondary objective was to develop a multivariable prediction model to forecast stroke-like episode risk. The most common genetic cause of stroke-like episodes was the m.3243A>G variant in MT-TL1 (n = 66), followed by recessive pathogenic POLG variants (n = 22), and 11 other rarer pathogenic mitochondrial DNA variants (n = 23). The age of first stroke-like episode was available for 105 patients [mean (SD) age: 31.8 (16.1)]; a total of 35 patients (32%) presented with their first stroke-like episode ≥40 years of age. The median interval (interquartile range) between first and second stroke-like episodes was 1.33 (2.86) years; 43% of patients developed recurrent stroke-like episodes within 12 months. Clinico-radiological, electrophysiological and neuropathological findings of stroke-like episodes were consistent with the hallmarks of medically refractory epilepsy. Patients with POLG-related stroke-like episodes demonstrated more fulminant disease trajectories than cases of m.3243A>G and other mitochondrial DNA pathogenic variants, in terms of the frequency of refractory status epilepticus, rapidity of progression and overall mortality. In multivariate analysis, baseline factors of body mass index, age-adjusted blood m.3243A>G heteroplasmy, sensorineural hearing loss and serum lactate were significantly associated with risk of stroke-like episodes in patients with the m.3243A>G variant. These factors informed the development of a prediction model to assess the risk of developing stroke-like episodes that demonstrated good overall discrimination (area under the curve = 0.87, 95% CI 0.82-0.93; c-statistic = 0.89). Significant radiological and pathological features of neurodegeneration were more evident in patients harbouring pathogenic mtDNA variants compared with POLG: brain atrophy on cranial MRI (90% versus 44%, P < 0.001) and reduced mean brain weight (SD) [1044 g (148) versus 1304 g (142), P = 0.005]. Our findings highlight the often idiosyncratic clinical, radiological and EEG characteristics of mitochondrial stroke-like episodes. Early recognition of seizures and aggressive instigation of treatment may help circumvent or slow neuronal loss and abate increasing disease burden. The risk-prediction model for the m.3243A>G variant can help inform more tailored genetic counselling and prognostication in routine clinical practice.

Fuzzy C-Means Algorithm-Based ARM-Linux-Embedded System Combined with Magnetic Resonance Imaging for Progression Prediction of Brain Tumors

The aim of this research was to analyze the application of fuzzy C-means (FCM) algorithm-based ARM-Linux-embedded system in magnetic resonance imaging (MRI) images for prediction of brain tumors. The optimized FCM (OFCM) algorithm was proposed based on kernel function, and the ARM-Linux-embedded imaging system was designed under ARM9 chip and Linux recorder, which were applied in MRI images of brain tumor patients. It was found that the sensitivity, specificity, and accuracy of the OFCM algorithm (90.46%, 88.97%, and 97.46%) were greater obviously than those of the deterministic C-means clustering algorithm (80.38%, 77.98%, and 85.24%) and the traditional FCM algorithm (83.26%, 79.56%, and 86.45%), and the difference was statistically substantial (P < 0.05). The ME and running time of the OFCM algorithm decreased sharply in contrast to those of the deterministic C-means clustering algorithm and the traditional FCM algorithm (P < 0.05). There were great differences in fraction anisotropy (FA) and mean diffusion (MD) of tumor parenchymal area, surrounding edema area, and normal white matter area (P < 0.05). FA of stage III+IV was smaller than those of stage I and II (P < 0.05), while the apparent diffusion coefficient (ADC) of stage III+IV was greater than that of stage I and II (P < 0.05). In conclusion, the poor update data processing and low data clustering efficiency of FCM were solved by OFCM. Moreover, computational efficiency of ARM-Linux-embedded imaging system was improved, so as to better realize the prediction of brain tumor patients through ARM-Linux-embedded system based on adaptive FCM incremental clustering algorithm.

Characterization of cognitive function in survivors of diffuse gliomas using resting-state functional MRI (rs-fMRI)

As treatments for diffuse gliomas have advanced, survival for patients with gliomas has also increased. However, there remains limited knowledge on the relationships between brain connectivity and the lasting changes to cognitive function that glioma survivors often experience long after completing treatment. This resting-state functional magnetic resonance imaging (rs-fMRI) study explored functional connectivity (FC) alterations associated with cognitive function in survivors of gliomas. In this pilot study, 22 patients (mean age 43.8 ± 11.9) with diffuse gliomas who completed treatment within the past 10 years were evaluated using rs-fMRI and neuropsychological measures. Novel rs-fMRI analysis methods were used to account for missing brain in the resection cavity. FC relationships were assessed between cognitively impaired and non-impaired glioma patients, along with self-reported cognitive impairment, non-work daily functioning, and time with surgery. In the cognitively non-impaired patients, FC was stronger in the medial prefrontal cortex, rostral prefrontal cortex, and intraparietal sulcus compared to the impaired survivors. When examining non-work daily functioning, a positive correlation with FC was observed between the accumbens and the intracalcarine cortices, while a negative correlation with FC was observed between the parietal operculum cortex and the cerebellum. Additionally, worse self-reported cognitive impairment and worse non-work daily functioning were associated with increased FC between regions involved in cognition and sensorimotor processing. These preliminary findings suggest that neural correlates for cognitive and daily functioning in glioma patients can be revealed using rs-fMRI. Resting-state network alterations may serve as a biomarker for patients’ cognition and functioning.

The Cerebellum Is Related to Cognitive Dysfunction in White Matter Hyperintensities

Objective

White matter hyperintensities (WMHs) on magnetic resonance imaging (MRI) is frequently presumed to be secondary to cerebral small vessel disease (CSVD) and associated with cognitive decline. The cerebellum plays a key role in cognition and has dense connections with other brain regions. Thus, the aim of this study was to investigate if cerebellar abnormalities could occur in CSVD patients with WMHs and the possible association with cognitive performances.

Methods

A total of 104 right-handed patients with WMHs were divided into the mild WMHs group (n = 39), moderate WMHs group (n = 37), and severe WMHs group (n = 28) according to the Fazekas scale, and 36 healthy controls were matched for sex ratio, age, education years, and acquired resting-state functional MRI. Analysis of voxel-based morphometry of gray matter volume (GMV) and seed-to-whole-brain functional connectivity (FC) was performed from the perspective of the cerebellum, and their correlations with neuropsychological variables were explored.

Results

The analysis revealed a lower GMV in the bilateral cerebellum lobule VI and decreased FC between the left- and right-sided cerebellar lobule VI with the left anterior cingulate gyri in CSVD patients with WMHs. Both changes in structure and function were correlated with cognitive impairment in patients with WMHs.

Conclusion

Our study revealed damaged GMV and FC in the cerebellum associated with cognitive impairment. This indicates that the cerebellum may play a key role in the modulation of cognitive function in CSVD patients with WMHs.

Stage detection of mild cognitive impairment via fMRI using Hilbert Huang transform based classification framework

PURPOSE

This work aims to establish a classification framework for the diagnosis of mild cognitive impairment (MCI) at different stages (early MCI and late MCI) through direct analysis of resting-state functional magnetic resonance imaging (rs-fMRI) signals and using the accuracy (total correct rate), specificity (correct rate of late MCI) and sensitivity (correct rate of early MCI) to validate its classification performance.

METHODS

All fMR images of subjects were parcellated into 116 regions of interest (ROIs) by applying the Anatomical Automatic Labeling (AAL) template, and the average rs-fMRI signals of each ROI were extracted. The Hilbert-Huang transform (HHT) was introduced into the framework to decompose each rs-fMRI signal into a series of intrinsic mode functions (IMFs) and to analyze these nonstationary and nonlinear time-series from the perspective of multiresolution. After obtaining the instantaneous frequencies and amplitudes of all IMFs of a signal, the Hilbert weighted frequencies (HWFs) were calculated and combined into a vector as the feature of the corresponding ROI. Support Vector Machine (SVM) was implemented to classify MCI at different stages. We used the independent two-sample t-test as the feature selection method and measured the classification performance through the leave-one-out cross-validation (LOOCV) method.

RESULTS

Results on 77 early MCI (eMCI) and 64 late MCI (lMCI) with baseline rs-fMRI data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) yielded 87.94% classification accuracy. Some of the brain regions with significant differences found by previous studies have been confirmed in this work. We found that HWF characteristics exhibited a significant downward trend in all cerebellar regions. The rs-fMRI signals in differential brain regions have not changed completely, but only altered in some narrow frequency bands. The analysis results showed that during the progress of MCI, the main changes of rs-fMRI were concentrated in IMF3, while IMFs with other indexes also contained HWF features with high SVM weights, such as Orbitofrontal superior frontal gyrus in IMF2, Insula in IMF4, and Lobule Ⅲ of vermis in IMF5, indicating that other IMFs provide important information for the diagnosis of MCI as well.

CONCLUSIONS

This work confirmed the classification ability of HHT-based classification framework in classification of at different stages of MCI. Through the analysis, we found that during the progress of MCI the main changes of rs-fMRI were concentrated in IMF3, and HWF characteristics showed a significant downward trend in all cerebellar regions.