Quantitative susceptibility mapping of the normal-appearing white matter as a potential new marker of disability progression in multiple sclerosis

OBJECTIVES

To investigate the normal-appearing white matter (NAWM) susceptibility in a cohort of newly diagnosed multiple sclerosis (MS) patients and to evaluate possible correlations between NAWM susceptibility and disability progression.

METHODS

Fifty-nine patients with a diagnosis of MS (n = 53) or clinically isolated syndrome (CIS) (n = 6) were recruited and followed up. All participants underwent neurological examination, blood sampling for serum neurofilament light chain (sNfL) level assessment, lumbar puncture for the quantification of cerebrospinal fluid (CSF) β-amyloid1-42 (Aβ) levels, and brain MRI. T2-weighted scans were used to quantify white matter (WM) lesion loads. For each scan, we derived the NAWM volume fraction and the WM lesion volume fraction. Quantitative susceptibility mapping (QSM) of the NAWM was calculated using the susceptibility tensor imaging (STI) suite. Susceptibility maps were computed with the STAR algorithm.

RESULTS

Primary progressive patients (n = 9) showed a higher mean susceptibility value in the NAWM than relapsing-remitting (n = 44) and CIS (n = 6) (p = 0.01 and p = 0.02). Patients with a higher susceptibility in the NAWM showed increased sNfL concentration (ρ = 0.38, p = 0.004) and lower CSF Aβ levels (ρ = -0.34, p = 0.009). Mean NAWM susceptibility turned out to be a predictor of the expanded disability status scale (EDSS) worsening at follow-up (β = 0.41, t = 2.66, p = 0.01) and of the MS severity scale (MSSS) (β = 0.38, t = 2.43, p = 0.019).

CONCLUSIONS

QSM in the NAWM seems to predict the EDSS increment over time. This finding might provide evidence on the role of QSM in identifying patients with an increased risk of early disability progression.

KEY POINTS

• NAWM-QSM is higher in PPMS patients than in RRMS. • NAWM-QSM seems to be a predictor of EDSS worsening over time. • Patients with higher NAWM-QSM show increased sNfL concentration and lower CSF Aβ levels.

Low CSF β-amyloid levels predict early regional grey matter atrophy in multiple sclerosis

BACKGROUND AND PURPOSE

Grey matter (GM) atrophy is present from the earliest stages of multiple sclerosis (MS) and occurs largely in a nonrandom manner. However, the biological mechanisms underlying the progression of regional atrophy are still unclear. Aim of this study is to investigate whether amyloid pathology might be involved in determining the pattern of GM atrophy over time.

METHODS

Forty-six subjects were recruited: 31 newly diagnosed relapsing-remitting (RR-) MS patients and 15 age- and sex-matched healthy controls (HC). Aβ levels were determined in CSF samples from all subjects. All participants underwent brain magnetic resonance imaging (MRI) at baseline, and 23 out of 31 patients at one year follow-up. T1-weighted scans were segmented using the Geodesic Information Flows software. Non-parametric statistical tests were used for between-group comparisons and multiple regression analyses.

RESULTS

CSF Aβ concentration was the best predictor of global GM loss over time after age (β = 0.403; p = 0.024), in particular in the left precuneus (p = 0.045), in the left middle cingulate gyrus (p = 0.009), in the left precentral gyrus (p = 0.021) and in the right angular gyrus (p = 0.029).

CONCLUSIONS

CSF Aβ levels seem to be crucial in MS early brain volume loss as GM atrophy manifests in regions particularly vulnerable to early Aβ deposition.

Multiparametric MR imaging of Parkinsonisms at 3 tesla: Its role in the differentiation of idiopathic Parkinson's disease versus atypical Parkinsonian disorders

OBJECTIVES

The Nigrosome-1 and putaminal hypointensity depicted on susceptibility-weighted imaging (SWI), and midbrain atrophy assessed on T1-weighted are some of the most common radiological parameters to diagnose Parkinsonism at Magnetic Resonance (MR) imaging. Our aim is to assess the feasibility of these signs in the differentiation of Idiopathic Parkinson's disease (IPD) patients versus disease (DC) and healthy controls (HC) and in the assessment of the Atypical Progressive Parkinsonisms (APPs).

METHODS

Presence or loss of the Nigrosome-1 was assessed retrospectively on multiple-echo SWI obtained on a 3 T scan by two neuroradiologists. Results were compared with the 123I-FP-CIT SPECT images. Morphologic diagnostic features suggestive of APPs such as midbrain atrophy and putaminal hypointensity were evaluated by qualitative scores. The midbrain and putaminal scores were summed (combined score) and then added to the Nigrosome-1 score (global score).

RESULTS

The study included 126 patients with IPD (n = 56), APPs patients (n = 30; 18 PSP, 3 MSA-C, 9 MSA-P), 16 DC and 24 HC. Sensitivity and specificity of the Nigrosome-1 in discriminating IPD from controls were 96,43% and 85.00%, APPs from controls were 100% and 85%, IPD from APPs were 96,43% and 0% respectively. Combined score for midbrain atrophy and putaminal hypointensity resulted in the most accurate for distinguishing APPs from IPD with a value of ≥ 2 (AUC = 0.98).

CONCLUSION

Nigrosome-1 is a valid tool to differentiate IPD-APPs from controls. The combined score of midbrain atrophy and putaminal hypointensity represents a valid diagnostic pointer in the differential diagnosis of APPs from IPD.

Cortical markers of cognitive syndromes in amyotrophic lateral sclerosis

Amyotrophic lateral sclerosis (ALS) can be associated with a spectrum of cognitive and behavioural symptoms, but the related patterns of focal cortical atrophy in non-demented ALS patients remain largely unknown. We enrolled 48 non-demented ALS patients and 26 healthy controls for a comprehensive neuropsychological assessment and a magnetic resonance exam. Behavioural and cognitive impairment was defined on the basis of a data-driven multi-domain approach in 21 ALS patients. Averaged cortical thickness of 74 bilateral brain regions was used as a measure of cortical atrophy. Cortical thinning in a fronto-parietal network, suggesting a disease-specific pattern of neurodegeneration, was present in all patients, independent of cognitive and behavioural status. Between-group and correlational analyses revealed that inferior frontal, temporal, cingular and insular thinning are markers for cognitive and behavioural deficits, with language impairment mainly related to left temporal pole and insular involvement. These specific correlates support the concept of a spectrum of deficits, with an overlap between the ALS cognitive phenotypes and the syndromes of frontotemporal dementia.

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Language, social cognition and executive dysfunctions are frequent symptoms in ALS.

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Fronto-parietal cortical thinning is present in non-demented ALS patients.

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Temporal, cingular and insular thinning are markers for cognitive impairment in ALS.

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Left temporal pole and insular thinning is linked to language impairment in ALS.

Widespread Focal Cortical Alterations in Autism Spectrum Disorder with Intellectual Disability Detected by Threshold-Free Cluster Enhancement

BACKGROUND AND PURPOSE

In the past decades, a large body of work aimed at investigating brain structural anomalies accrued in autism spectrum disorder. Autism spectrum disorder is associated with intellectual disability in up to 50% of cases. However, only a few neuroimaging studies were conducted in autism spectrum disorder with intellectual disability, and none of them benefited from a nonsyndromic intellectual disability control group.

MATERIALS AND METHODS

We performed a voxelwise investigation of the structural alterations in 25 children with autism spectrum disorder with intellectual disability by comparing them with 25 typically developing children and 25 nonsyndromic children with an intellectual disability. Besides a classic voxel-based morphometry statistical approach, the threshold-free cluster enhancement statistical approach was adopted.

RESULTS

Classic voxel-based morphometry results did not survive family-wise error correction. The threshold-free cluster enhancement-based analysis corrected for family-wise error highlighted the following: 1) widespread focal cortical anomalies and corpus callosum alteration detected in autism spectrum disorder with intellectual disability; 2) basal ganglia and basal forebrain alteration detected both in autism spectrum disorder with intellectual disability and in nonsyndromic intellectual disability; and 3) differences in the frontocingulate-parietal cortex between autism spectrum disorder with intellectual disability and nonsyndromic intellectual disability.

CONCLUSIONS

The present study suggests that the frontocingulate-parietal cortex may be the eligible key region for further investigations aiming at detecting imaging biomarkers in autism spectrum disorder with intellectual disability. The detection of structural alterations in neurodevelopmental disorders may be dramatically improved by using a threshold-free cluster enhancement statistical approach.