Amyotrophic lateral sclerosis phenotypes significantly differ in terms of magnetic susceptibility properties of the precentral cortex

The aim of our study was to investigate whether the magnetic susceptibility varies according to the amyotrophic lateral sclerosis (ALS) phenotypes based on the predominance of upper motor neuron (UMN)/lower motor neuron (LMN) impairment. We retrospectively collected imaging and clinical data of 47 ALS patients (12 with UMN predominance (UMN-ALS), 16 with LMN predominance (LMN-ALS), and 19 with no clinically defined predominance (Np-ALS)). We further enrolled 23 healthy controls (HC) and 15 ALS mimics (ALS-Mim). These participants underwent brain 3-T magnetic resonance imaging (3-T MRI) with T1-weighted and gradient-echo multi-echo sequences. Automatic segmentation and quantitative susceptibility mapping (QSM) were performed. The skewness of the susceptibility values in the precentral cortex (SuscSKEW) was automatically computed, compared among the groups, and correlated to the clinical variables. The Kruskal-Wallis test showed significant differences in terms of SuscSKEW among groups (χ2(3) = 24.2, p < 0.001), and pairwise tests showed that SuscSKEW was higher in UMN-ALS compared to those in LMN-ALS (p < 0.001), HC (p < 0.001), Np-ALS (p = 0.012), and ALS-Mim (p < 0.001). SuscSKEW was highly correlated with the Penn UMN score (Spearman's rho 0.612, p < 0.001). This study demonstrates that the clinical ALS phenotypes based on UMN/LMN sign predominance significantly differ in terms of magnetic susceptibility properties of the precentral cortex. Combined MRI-histopathology investigations are strongly encouraged to confirm whether this evidence is due to iron overload in UMN-ALS, unlike in LMN-ALS. • Magnetic susceptibility in the precentral cortex reflects the prevalence of UMN/LMN impairment in the clinical ALS phenotypes. • The degree of UMN/LMN impairment might be well described by the automatically derived measure of SuscSKEW in the precentral cortex. • Increased SuscSKEW in the precentral cortex is more relevant in UMN-ALS patients compared to those in Np-ALS and LMN-ALS patients

Cerebellar haemorrhages and pons development in extremely low birth weight infants.

UNLABELLED Neuropathological and Magnetic Resonance Imaging (MRI) studies showed a high frequency of posterior fossa abnormalities in preterms. To assess whether cerebellar haemorrhages (CH) diagnosed with ultrasound and/or MRI affect pons development in ELBW infants. The anteroposterior diameter of the pons was measured manually on the midline sagittal T1 MR image in 75 ELBW babies consecutively scanned at term postmenstrual age. Subjects with CH were identified and compared to babies with no posterior fossa bleeding. Nine ELBW infants with CH (CH-Group: median gestational age -GA- 26 wks, range 23-27; birth weight -BW- 680 g, 425-980) were compared with 66 babies with normal cerebellum (Control-Group: GA 28 wks, 23-33; BW 815 g, 430-1000). The two groups were comparable for BW (p=0.088) while GA was significantly shorter in CH babies (p=0.005). The pontine diameter was significantly lower in CH-Group compared to Control-Group (12.8 +/- 2.2 vs 14.8 +/- 1.2 mm; p<0.001). CONCLUSIONS Cerebellar haemorrhages seem to affect the development of the pons in ELBW with the youngest GA

Sexual Dimorphism in the Brain Correlates of Adult-Onset Depression: A Pilot Structural and Functional 3T MRI Study

Major Depressive Disorder (MDD) is a disabling illness affecting more than 5% of the elderly population. Higher female prevalence and sex-specific symptomatology have been observed, suggesting that biologically-determined dimensions might affect the disease onset and outcome. Rumination and executive dysfunction characterize adult-onset MDD, but sex differences in these domains and in the related brain mechanisms are still largely unexplored. The present pilot study aimed to explore any interactions between adult-onset MDD and sex on brain morphology and brain function during a Go/No-Go paradigm. We hypothesized to detect diagnosis by sex effects on brain regions involved in self-referential processes and cognitive control. Twenty-four subjects, 12 healthy (HC) (mean age 68.7 y, 7 females and 5 males) and 12 affected by adult-onset MDD (mean age 66.5 y, 5 females and 7 males), underwent clinical evaluations and a 3T magnetic resonance imaging (MRI) session. Diagnosis and diagnosis by sex effects were assessed on regional gray matter (GM) volumes and task-related functional MRI (fMRI) activations. The GM volume analyses showed diagnosis effects in left mid frontal cortex (p < 0.01), and diagnosis by sex effects in orbitofrontal, olfactory, and calcarine regions (p < 0.05). The Go/No-Go fMRI analyses showed MDD effects on fMRI activations in left precuneus and right lingual gyrus, and diagnosis by sex effects on fMRI activations in right parahippocampal gyrus and right calcarine cortex (p < 0.001, ≥ 40 voxels). Our exploratory results suggest the presence of sex-specific brain correlates of adult-onset MDD-especially in regions involved in attention processing and in the brain default mode-potentially supporting cognitive and symptom differences between sexes

CSF β-amyloid predicts prognosis in patients with multiple sclerosis

Background: The importance of predicting disease progression in multiple sclerosis (MS) has increasingly been recognised, hence reliable biomarkers are needed. Objectives: To investigate the prognostic role of cerebrospinal fluid (CSF) Amyloid beta1-42 (A) levels by the determination of a cut-off value to classify patients in slow and fast progressors. To evaluate possible association with white (WM) and grey matter (GM) damage at early disease stages. Methods: Sixty patients were recruited and followed-up for three to five years. Patients underwent clinical assessment, CSF analysis to determine Aβ levels, and brain MRI (at baseline and after 1 year). T1-weighted volumes were calculated. T2-weighted scans were used to quantify WM lesion loads. Results: Lower CSF Aβ levels were observed in patients with a worse follow-up EDSS (r=−0.65, p0.05). Conclusions: Low CSF Aβ levels may represent a predictive biomarker of disease progression in MS

Can early-onset acquired demyelinating syndrome (ADS) hide pediatric Behcet's disease? A case report

Behcet's disease (BD) is a rare vasculitis characterized by multisystemic inflammation. Central nervous system (CNS) involvement is rare and heterogeneous, particularly in the pediatric population. A diagnosis of neuro-Behcet could be highly challenging, especially if neurological manifestations precede other systemic features; however, its timely definition is crucial to prevent long-term sequelae. In this study, we describe the case of a girl who, at 13 months of age, presented with a first episode of encephalopathy compatible with acute disseminated encephalomyelitis, followed, after 6 months, by a neurological relapse characterized by ophthalmoparesis and gait ataxia, in association with new inflammatory lesions in the brain and spinal cord, suggesting a neuromyelitis optica spectrum disorder. The neurological manifestations were successfully treated with high-dose steroids and intravenous immunoglobulins. In the following months, the patient developed a multisystemic involvement suggestive of Behcet's disease, characterized by polyarthritis and uveitis, associated with HLA-B51 positivity. The challenge presented by this unique case required a multidisciplinary approach involving pediatric neurologists, neuro-radiologists, and pediatric rheumatologists, with all of these specialists creating awareness about early-onset acquired demyelinating syndromes (ADSs). Given the rarity of this presentation, we performed a review of the literature focusing on neurological manifestations in BD and differential diagnosis of patients with early-onset ADS

CSF β-amyloid and white matter damage: a new perspective on Alzheimer's disease

Objective: To assess the connection between amyloid pathology and white matter (WM) macro- and micro-structural damage in demented patients compared with controls. Methods: Eighty-five participants were recruited: 65 with newly diagnosed Alzheimer’s disease (AD), non-AD dementia or mild cognitive impairment (MCI), and 20 age- and sex-matched heatlhy controls. β-amyloid1-42 (Aβ) levels were determined in cerebrospinal fluid (CSF) samples from all patients and 5 controls. Among patients, 42 had pathological CSF Aβ levels (Aβ+), while 23 had normal CSF Aβ levels (Aβ-). All participants underwent neurological examination, neuropsychological testing and brain magnetic resonance imaging (MRI). We used T2-weighted scans to quantify white matter (WM) lesion loads (LL), and diffusion weighted images (DWI) to assess their microstructural substrate. Non-parametric statistical tests were used for between-group comparisons and multiple regression analyses. Results: We found an increased WM-LL in Aβ(+) compared to both, healthy controls (p=0.003) and Aβ(-) patients (p=0.02). Interestingly, CSF Aβ concentration was the best predictor patients’ WM-LL (r=-0.30, p<0.05) when using age as a covariate. Lesion apparent diffusion coefficient (ADC) value was higher in all patients than in controls (p=0.0001), and correlated with WM-LL (r=0.41, p=0.001). In Aβ(+), WM-LL correlated with WM microstructural damage in the left peritrigonal WM (p<0.0001). Conclusions: WM damage is crucial in Alzheimer’s disease (AD) pathogenesis. The correlation between CSF Aβ levels and WM-LL suggests a direct link between amyloid pathology and WM macro- and microstructural damage

Differential diagnosis of neurodegenerative dementias with the explainable MRI based machine learning algorithm MUQUBIA

Biomarker-based differential diagnosis of the most common forms of dementia is becoming increasingly important. Machine learning (ML) may be able to address this challenge. The aim of this study was to develop and interpret a ML algorithm capable of differentiating Alzheimer's dementia, frontotemporal dementia, dementia with Lewy bodies and cognitively normal control subjects based on sociodemographic, clinical, and magnetic resonance imaging (MRI) variables. 506 subjects from 5 databases were included. MRI images were processed with FreeSurfer, LPA, and TRACULA to obtain brain volumes and thicknesses, white matter lesions and diffusion metrics. MRI metrics were used in conjunction with clinical and demographic data to perform differential diagnosis based on a Support Vector Machine model called MUQUBIA (Multimodal Quantification of Brain whIte matter biomArkers). Age, gender, Clinical Dementia Rating (CDR) Dementia Staging Instrument, and 19 imaging features formed the best set of discriminative features. The predictive model performed with an overall Area Under the Curve of 98%, high overall precision (88%), recall (88%), and F1 scores (88%) in the test group, and good Label Ranking Average Precision score (0.95) in a subset of neuropathologically assessed patients. The results of MUQUBIA were explained by the SHapley Additive exPlanations (SHAP) method. The MUQUBIA algorithm successfully classified various dementias with good performance using cost-effective clinical and MRI information, and with independent validation, has the potential to assist physicians in their clinical diagnosis