Region-specific atrophy of precentral gyrus in patients with amyotrophic lateral sclerosis

Potential of neuroimaging as a biomarker in amyotrophic lateral sclerosis: from structure to metabolism

Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease characterized by motor neuron degeneration. The development of ALS involves metabolite alterations leading to tissue lesions in the nervous system. Recent advances in neuroimaging have significantly improved our understanding of the underlying pathophysiology of ALS, with findings supporting the corticoefferent axonal disease progression theory. Current studies on neuroimaging in ALS have demonstrated inconsistencies, which may be due to small sample sizes, insufficient statistical power, overinterpretation of findings, and the inherent heterogeneity of ALS. Deriving meaningful conclusions solely from individual imaging metrics in ALS studies remains challenging, and integrating multimodal imaging techniques shows promise for detecting valuable ALS biomarkers. In addition to giving an overview of the principles and techniques of different neuroimaging modalities, this review describes the potential of neuroimaging biomarkers in the diagnosis and prognostication of ALS. We provide an insight into the underlying pathology, highlighting the need for standardized protocols and multicenter collaborations to advance ALS research.

A resting-state functional magnetic resonance imaging study of altered functional brain activity in cardiac arrest survivors with good neurological outcome

Purpose

To investigate the spontaneous brain activity alterations in survivors of cardiac arrest (CA) with good neurological outcome using resting-state functional magnetic resonance imaging (rs-fMRI) with amplitude of low-frequency fluctuation (ALFF) and regional homogeneity (ReHo) methods.

Materials and methods

Thirteen CA survivors with favorable neurological outcomes and 13 healthy controls (HCs) were recruited and underwent rs-fMRI scans. The ALFF and ReHo methods were applied to assess the regional intensity and synchronization of spontaneous brain activity. Correlation analyses were performed to explore the relationships between the mean ALFF and ReHo values in significant clusters and clinical parameters.

Results

The survivors of CA showed significantly decreased ALFF values in the left postcentral gyrus and precentral gyrus and increased ALFF values in the left hippocampus and parahippocampal gyrus than HCs. Significantly decreased ReHo values were observed in the left inferior occipital gyrus and middle occipital gyrus in the patients. Mean ALFF values in the left hippocampus and parahippocampal gyrus were positively correlated with the time to return of spontaneous circulation (r = 0.794, p = 0.006) in the patient group.

Conclusion

Functional activity alterations in the brain areas corresponding to known cognitive and physical impairments were observed in CA survivors with preserved neurological function. Our results could advance the understanding of the neurological mechanisms underlying the residual deficits in those patients.

Feature selection from magnetic resonance imaging data in ALS: a systematic review

Background:

With the advances in neuroimaging in amyotrophic lateral sclerosis (ALS), it has been speculated that multiparametric magnetic resonance imaging (MRI) is capable to contribute to early diagnosis. Machine learning (ML) can be regarded as the missing piece that allows for the useful integration of multiparametric MRI data into a diagnostic classifier. The major challenges in developing ML classifiers for ALS are limited data quantity and a suboptimal sample to feature ratio which can be addressed by sound feature selection.

Methods:

We conducted a systematic review to collect MRI biomarkers that could be used as features by searching the online database PubMed for entries in the recent 4 years that contained cross-sectional neuroimaging data of subjects with ALS and an adequate control group. In addition to the qualitative synthesis, a semi-quantitative analysis was conducted for each MRI modality that indicated which brain regions were most commonly reported.

Results:

Our search resulted in 151 studies with a total of 221 datasets. In summary, our findings highly resembled generally accepted neuropathological patterns of ALS, with degeneration of the motor cortex and the corticospinal tract, but also in frontal, temporal, and subcortical structures, consistent with the neuropathological four-stage model of the propagation of pTDP-43 in ALS.

Conclusions:

These insights are discussed with respect to their potential for MRI feature selection for future ML-based neuroimaging classifiers in ALS. The integration of multiparametric MRI including DTI, volumetric, and texture data using ML may be the best approach to generate a diagnostic neuroimaging tool for ALS.

Age at symptom onset influences cortical thinning distribution and survival in amyotrophic lateral sclerosis

PURPOSE

The lifetime risk of developing amyotrophic lateral sclerosis (ALS) increases in the elderly, and greater age at symptom onset has been identified as a negative prognostic factor in the disease. However, the underlying neurobiological mechanisms are still poorly investigated. We hypothesized that older age at symptom onset would have been associated with greater extra-motor cortical damage contributing to worse prognosis, so we explored the relationship between age at symptom onset, cortical thinning (CT) distribution, and clinical markers of disease progression.

METHODS

We included 26 ALS patients and 29 healthy controls with T1-weighted magnetic resonance imaging (MRI). FreeSurfer 6.0 was used to identify regions of cortical atrophy (CA) in ALS, and to relate age at symptom onset to CT distribution. Linear regression analyses were then used to investigate whether MRI metrics of age-related damage were predictive of clinical progression. MRI results were corrected using the Monte Carlo simulation method, and regression analyses were further corrected for disease duration.

RESULTS

ALS patients exhibited significant CA mainly encompassing motor regions, but also involving the cuneus bilaterally and the right superior parietal cortex (p < 0.05). Older age at symptom onset was selectively associated with greater extra-motor (frontotemporal) CT, including pars opercularis bilaterally, left middle temporal, and parahippocampal cortices (p < 0.05), and CT of these regions was predictive of shorter survival (p = 0.004, p = 0.03).

CONCLUSION

More severe frontotemporal CT contributes to shorter survival in older ALS patients. These findings have the potential to unravel the neurobiological mechanisms linking older age at symptom onset to worse prognosis in ALS.

Altered intrinsic brain activity in patients with hepatic encephalopathy

Neuropsychiatric deficits are common in patients with liver cirrhosis (LC), especially in those with hepatic encephalopathy (HE). Previous studies reveal abnormalities in brain activity underlying the neuropsychiatric deficits in LC patients; however, the results are inconsistent. We conducted a meta-analysis of resting-state functional magnetic resonance imaging studies using anisotropic effect-size signed differential mapping software on LC patients to characterize the most consistent regional activity alterations, and to evaluate the potential effect of liver transplantation (LT) on brain function. Meta-regression analyses were performed to explore the relationship between brain alterations and clinical variables. Compared with healthy controls, the typical patterns of increased regional activity in the fronto-striato-cerebellar network and decreased activity in the visuo-sensorimotor network and cingulate gyrus were identified in LC patients, which remained significant in the subgroup meta-analyses of minimal HE (MHE) and overt HE (OHE) patients. Functional deficits in the default mode network (DMN) were found in OHE patients compared with MHE patients. Ammonia level positively correlated with brain activity in the right middle temporal gyrus, and the completion time of number connection test A negatively correlated with brain activity in the left anterior cingulate gyrus. In addition, patients showed increased activity in the visuo-sensorimotor network and precuneus after LT. Our study suggests that alterations in the fronto-striato-cerebellar and visuo-sensorimotor networks may be the potential pathophysiological mechanisms underlying HE, and deficits in the DMN may indicate the progression of HE. LT may improve brain function in the visuo-sensorimotor network. This study has registered in the PROSPERO (CRD42020212758).

Quantifying changes on susceptibility weighted images in amyotrophic lateral sclerosis using MRI texture analysis

Objective: Susceptibility-weighted imaging (SWI) has been used to identify neurodegeneration in amyotrophic lateral sclerosis (ALS) through qualitative gross visual comparison of signal intensity. The aim of this study was to quantitatively identify cerebral degeneration in ALS on SWI using texture analysis. Methods: SW images were acquired from 17 ALS patients (58.4 ± 10.3 years, 13M/4F, ALSFRS-R 41.2 ± 4.1) and 18 healthy controls (56.3 ± 17.6 years, 9M/9F) at 4.7 tesla. Textures were computed within the precentral gyrus and basal ganglia and compared between patients and controls using ANCOVA with age and gender as covariates. Texture features were correlated with clinical measures in patients. Texture features found to be significantly different between patients and controls in the precentral gyrus were then used in a whole-brain 3D texture analysis. Results: The texture feature autocorrelation was significantly higher in ALS patients compared to healthy controls in the precentral gyrus and basal ganglia (p < 0.05). Autocorrelation correlated significantly with clinical measures such as disease progression rate and finger tapping speed (p < 0.05). Whole brain 3D texture analysis using autocorrelation revealed differences between ALS patients and controls within the precentral gyrus on SWI images (p < 0.001). Conclusion: Texture analysis on SWI can quantitatively identify cerebral differences between ALS patients and controls.