Nucleus basalis of Meynert damage and cognition in patients with multiple sclerosis

Association between diffusion tensor imaging measurements and cognitive performances in people with multiple sclerosis: A systematic review and meta-analysis

BACKGROUND

Alterations in structural connectivity of brain networks have been linked to complex cognitive functions in people with multiple sclerosis (PwMS). However, a definitive consensus on the optimal diffusion tensor imaging (DTI) markers as indicators of cognitive performance remains incomplete and inconclusive. This systematic review and meta-analysis aimed to explore the evidence on the correlation between DTI metrics and cognitive functions in PwMS.

METHODS

A comprehensive literature search was conducted across PubMed/MEDLINE, Embase, Scopus, and the Web of Science up to March 2024 to identify studies reporting the correlation between DTI metrics and cognitive functions. Cognitive function was assessed using the Symbol Digit Modalities Test (SDMT), California Verbal Learning Test (CVLT), and Brief Visuospatial Memory Test-Revised (BVMT-R). The pooled correlation coefficients were estimated using R software version 4.4.0 with the random effect model.

RESULTS

Out of 1952 studies, 38 studies on 2055 PwMS fulfilled the inclusion criteria. The meta-analysis indicated that the SDMT exhibited the greatest correlation with corpus callosum fractional anisotropy (FA) (r = 0.54, 95 % CI: 0.4 to 0.66, p-value < 0.001, I2 = 34.1 %, p-heterogeneity = 0.19) and mean diffusivity (MD) (r = -0.48, 95 % CI: 0.61 to -0.33, p-value < 0.001, I2 = 0 %, p-heterogeneity = 0.77), white matter FA (r = 0.39, 95 % CI: 0.24 to 0.52, p-value < 0.001, I2 = 0 %, p-heterogeneity = 0.1), and fornix FA (r = 0.35, 95 % CI: 0.12 to 0.54, p-value = 0.003, I2 = 50.7 %, p-heterogeneity = 0.18) and MD (r = -0.35, 95 % CI: 0.49 to -0.19, p-value < 0.001, I2 = 0 %, p-heterogeneity = 0.5).

CONCLUSION

DTI measurements, including corpus callosum FA and MD, white matter FA, and fornix FA and MD, represent the indicators of cognitive performance in PwMS. Nonetheless, these findings warrant cautious interpretation due to the restricted kinds of cognitive tests and methodological variability across studies.

Cranial volume measurement with artificial intelligence and cognitive scales in patients with clinically isolated syndrome

Objective

We aimed to investigate the relationship between volumetric measurements of specific brain regions which were measured with artificial intelligence (AI) and various neuropsychological tests in patients with clinically isolated syndrome.

Materials and methods

A total of 28 patients diagnosed with CIS were included in the study. The patients were administered Öktem Verbal Memory Processes Test, Symbol Digit Modalities Test (SDMT), Backward-Forward Digit Span Test, Stroop Test, Trail Making Test, Controlled Oral Word Association Test (COWAT), Brief Visuospatial Memory Test, Judgement of Line Orientation Test, Beck Depression Scale, Beck Anxiety Scale and Fatigue Severity Scale. Artificial intelligence assisted BrainLab Elements™ Atlas-Based Automatic Segmentation program was used for calculating volumes. The measured volumes were compared with the reference database. In addition, neuropsychological test performances and volumetric measurements of the patients were compared.

Results

Of the patients included in the study, 78.6% were female and 21.4% were male, with an average age of 33 years. Verbal Memory Processes Test, SDMT, Backward-Forward Digit Span, JLOT, and Stroop Test showed significant correlations with multiple anatomical regions, particularly the anterior thalamic nucleus, which was associated with the highest number of cognitive tests. The JLOT exhibited the strongest correlation with six different brain regions (p < 0.001).

Conclusion

The Judgement of Line Orientation and Stroop Tests, correlated with multiple brain regions, especially the anterior thalamic nucleus, underscoring the importance of these tests in assessing cognitive function in CIS.

Sleep Deficits Inter-Link Lower Basal Forebrain-Posterior Cingulate Connectivity and Perceived Stress and Anxiety Bidirectionally in Young Men

BACKGROUND

The basal nucleus of Meynert (BNM), a primary source of cholinergic projections to the cortex, plays key roles in regulating the sleep-wake cycle and attention. Sleep deficit is associated with impairment in cognitive and emotional functions. However, whether or how cholinergic circuit, sleep, and cognitive/emotional dysfunction are inter-related remains unclear.

METHODS

We curated the Human Connectome Project data and explored BNM resting state functional connectivities (rsFC) in relation to sleep deficit, based on the Pittsburgh Sleep Quality Index (PSQI), cognitive performance, and subjective reports of emotional states in 687 young adults (342 women). Imaging data were processed with published routines and evaluated at a corrected threshold. We assessed the correlation between BNM rsFC, PSQI, and clinical measurements with Pearson regressions and their inter-relationships with mediation analyses.

RESULTS

In whole-brain regressions with age and alcohol use severity as covariates, men showed lower BNM rsFC with the posterior cingulate cortex (PCC) in correlation with PSQI score. No clusters were identified in women at the same threshold. Both BNM-PCC rsFC and PSQI score were significantly correlated with anxiety, perceived stress, and neuroticism scores in men. Moreover, mediation analyses showed that PSQI score mediated the relationship between BNM-PCC rsFC and these measures of negative emotions bidirectionally in men.

CONCLUSIONS

Sleep deficit is associated with negative emotions and lower BNM rsFC with the PCC. Negative emotional states and BNM-PCC rsFC are bidirectionally related through poor sleep quality. These findings are specific to men, suggesting potential sex differences in the neural circuits regulating sleep and emotional states.

Correlation Between the Functional Connectivity of Basal Forebrain Subregions and Vigilance Dysfunction in Temporal Lobe Epilepsy With and Without Focal to Bilateral Tonic-Clonic Seizure

PURPOSE

Previous research has shown that subcortical brain regions are related to vigilance in temporal lobe epilepsy (TLE). However, it is unknown whether alterations in the function and structure of basal forebrain (BF) subregions are associated with vigilance impairment in distinct kinds of TLE. We aimed to investigate changes in the structure and function BF subregions in TLE patients with and without focal to bilateral tonic-clonic seizures (FBTCS) and associated clinical features.

METHODS

A total of 50 TLE patients (25 without and 25 with FBTCS) and 25 healthy controls (HCs) were enrolled in this study. The structural and functional alterations of BF subregions in TLE were investigated using voxel-based morphometry (VBM) and resting-state functional connectivity (rsFC) analysis. Correlation analyses were utilized to investigate correlations between substantially altered imaging characteristics and clinical data from patients.

RESULTS

FBTCS patients had a lower rsFC between Ch1-3 and the bilateral striatum as well as the left cerebellum posterior lobe than non-FBTCS patients. In comparison to non-FBTCS patients, the rsFC between Ch4 and the bilateral amygdala was also lower in FBTCS patients. Compared to HCs, the TLE patients had reduced rsFC between the BF subregions and the cerebellum, striatum, default mode network, frontal lobe, and occipital lobes. In the FBTCS group, the rsFC between the left Ch1-3 and striatum was positive correlated with the vigilance measures. In the non-FBTCS group, the rsFC between the left Ch4 and striatum was significantly negative correlated with the alertness measure.

CONCLUSION

These results extend current understanding of the pathophysiology of impaired vigilance in TLE and imply that the BF subregions may serve as critical nodes for developing and categorizing TLE biomarkers.

Towards Multimodal Machine Learning Prediction of Individual Cognitive Evolution in Multiple Sclerosis

Multiple sclerosis (MS) manifests heterogeneously among persons suffering from it, making its disease course highly challenging to predict. At present, prognosis mostly relies on biomarkers that are unable to predict disease course on an individual level. Machine learning is a promising technique, both in terms of its ability to combine multimodal data and through the capability of making personalized predictions. However, most investigations on machine learning for prognosis in MS were geared towards predicting physical deterioration, while cognitive deterioration, although prevalent and burdensome, remained largely overlooked. This review aims to boost the field of machine learning for cognitive prognosis in MS by means of an introduction to machine learning and its pitfalls, an overview of important elements for study design, and an overview of the current literature on cognitive prognosis in MS using machine learning. Furthermore, the review discusses new trends in the field of machine learning that might be adopted for future studies in the field.